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''' Based on daily, global climate sea surface temperature (SST) analyses generated by the Copernicus Climate Change Service (C3S) (product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). Analysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: 1. The daily analyses were averaged to create monthly means. 2. A climatology was calculated by averaging the monthly means over the period 1991 - 2020. 3. Monthly anomalies were calculated by differencing the monthly means and the climatology. 4. An area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. 5. The time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. 6. The slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. '''CONTEXT''' Sea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016). '''CMEMS KEY FINDINGS''' Over the period 1982 to 2024, the global average linear trend was 0.012 ± 0.001°C / year (95% confidence interval). 2024 is nominally the warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Niño and La Niña conditions. For example, peaks in the time series coincide with the strong El Niño events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018). '''DOI (product):''' https://doi.org/10.48670/moi-00242

'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''CONTEXT''' Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the near-surface (0-300m) near-global (60°S-60°N) ocean warms at a rate of 0.4 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00233

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the in-situ near real time database are produced monthly. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a support for localized experience (cruises), providing a validation source for operational models, observing seasonal cycle and inter-annual variability. '''DOI (product) :''' https://doi.org/10.48670/moi-00037

'''DEFINITION''' The trend map is derived from version 5 of the global climate-quality chlorophyll time series produced by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020) and distributed by CMEMS. The trend detection method is based on the Census-I algorithm as described by Vantrepotte et al. (2009), where the time series is decomposed as a fixed seasonal cycle plus a linear trend component plus a residual component. The linear trend is expressed in % year -1, and its level of significance (p) calculated using a t-test. Only significant trends (p < 0.05) are included. '''CONTEXT''' Phytoplankton are key actors in the carbon cycle and, as such, recognised as an Essential Climate Variable (ECV). Chlorophyll concentration is the most widely used measure of the concentration of phytoplankton present in the ocean. Drivers for chlorophyll variability range from small-scale seasonal cycles to long-term climate oscillations and, most importantly, anthropogenic climate change. Due to such diverse factors, the detection of climate signals requires a long-term time series of consistent, well-calibrated, climate-quality data record. Furthermore, chlorophyll analysis also demands the use of robust statistical temporal decomposition techniques, in order to separate the long-term signal from the seasonal component of the time series. '''CMEMS KEY FINDINGS''' The average global trend for the 1997-2021 period was 0.51% per year, with a maximum value of 25% per year and a minimum value of -6.1% per year. Positive trends are pronounced in the high latitudes of both northern and southern hemispheres. The significant increases in chlorophyll reported in 2016-2017 (Sathyendranath et al., 2018b) for the Atlantic and Pacific oceans at high latitudes appear to be plateauing after the 2021 extension. The negative trends shown in equatorial waters in 2020 appear to be remain consistent in 2021. '''DOI (product):''' https://doi.org/10.48670/moi-00230

'''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. ESA-CCI data are provided by Plymouth Marine Laboratory at 4km resolution. These are processed using the same in-house software as in the operational processing. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (actually a high air mass cutoff, but approximating to 70deg zenith), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 555 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). 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 presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. A detailed description of calibration & validation is given in the relevant QUID, associated validation reports and quality documentation. '''Processing information:''' ESA-CCI data are provided by Plymouth Marine Laboratory at 4km resolution. These are processed using the same in-house software as in the operational processing. The entire CCI data set is consistent and processing is done in one go. Both OC CCI and the REP product are versioned. Standard masking criteria for detecting clouds or other contamination factors have been applied during the generation of the Rrs, i.e., land, cloud, sun glint, atmospheric correction failure, high total radiance, large solar zenith angle (actually a high air mass cutoff, but approximating to 70deg zenith), coccolithophores, negative water leaving radiance, and normalized water leaving radiance at 555 nm 0.15 Wm-2 sr-1 (McClain et al., 1995). '''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 presence of phytoplankton. By comparing reflectances at different wavelengths and calibrating the result against in-situ measurements, an estimate of chlorophyll content can be derived. '''Quality / Accuracy / Calibration information:''' Detailed description of cal/val is given in the relevant QUID, associated validation reports and quality 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-00101

'''Short description: ''' For the Global Ocean - In-situ observation yearly delivery in delayed mode of Ocean surface currents. '''Detailed description: ''' The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for Ocean surface currents. The data are collected from the Surface Drifter Data Assembly Centre (SD-DAC at NOAA AOML) completed by European data provided by EUROGOOS regional systems and national systems by the regional INS TAC components. All surface drifters data have been processed to check for drogue loss. Drogued and undrogued drifting buoy surface ocean currents are provided with a drogue presence flag as well as a wind slippage correction for undrogued buoy. '''Processing information: ''' From the near real time INS TAC product validated on a daily and weekly basis for forecasting purposes, and from the SD-DAC quality controlled dataset a scientifically validated product is created . It s a """"reference product"""" updated on a yearly basis. This product has been processed using a method that checks for drogue loss. Altimeter and wind data have been used to extract the direct wind slippage from the total drifting buoy velocities. The obtained wind slippage values have then been analyzed to identify probable undrogued data among the drifting buoy velocities dataset. A simple procedure has then been applied to produce an updated dataset including a drogue presence flag as well as a wind slippage correction. '''Suitability, Expected type of users / uses: ''' The product is designed to be assimilated into or for validation purposes of operational models operated by ocean forecasting centers for reanalysis purposes or for research community. These users need data aggregated and quality controlled in a reliable and documented manner.

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in near-real time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) “’Associated products”’ A time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_NOISE_L4_NRT_OBSERVATIONS_008_032 [http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033] describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. '''DOI (product) :''' https://doi.org/10.48670/moi-00147

'''Short description:''' In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology. '''DOI (product) :''' https://doi.org/10.48670/moi-00144

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' Global Ocean- in-situ reprocessed Carbon observations. This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2021 and GLobal Ocean Data Analysis Project GLODAPv2.2021. The SOCATv2022-OBS dataset contains >25 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2022. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2020-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2022-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2020. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products. '''DOI (product) :''' https://doi.org/10.48670/moi-00035