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.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' For the Global ocean, the ESA Ocean Colour CCI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. Phytoplankton functional types (PFT) products provide daily chlorophyll concentrations of three size-classes, consisting of nano, pico and micro-phytoplankton. 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-00097

'''Short description:''' The product contains a reprocessed multi year version of the daily composite dataset from SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_006 covering the Sentinel1 years from autumn 2014 until 1 year before present '''DOI (product) :''' https://doi.org/10.48670/mds-00328

'''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 upper (0-700m) near-global (60°S-60°N) ocean warms at a rate of 0.6 ± 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-00234

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description :''' For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Chlorophyll-a''' and '''Optics''' products [1997 - present] based on the '''Copernicus-GlobColour''' processor. * '''Chlorophyll and Bio''' products refer to Chlorophyll-a, Primary Production (PP) and Phytoplankton Functional types (PFT). Products are based on a multi sensors/algorithms approach to provide to end-users the best estimate. Two dailies Chlorophyll-a products are distributed: ** one limited to the daily observations (called L3), ** the other based on a space-time interpolation: the '''Cloud Free''' (called L4). * '''Optics''' products refer to Reflectance (RRS), Suspended Matter (SPM), Particulate Backscattering (BBP), Secchi Transparency Depth (ZSD), Diffuse Attenuation (KD490) and Absorption Coef. (ADG/CDM). * The spatial resolution is 4 km. For Chlorophyll, a 1 km over the Atlantic (46°W-13°E , 20°N-66°N) is also available for the '''Cloud Free''' product, plus a 300m Global coastal product (OLCI S3A & S3B merged). *Products (Daily, Monthly and Climatology) are based on the merging of the sensors SeaWiFS, MODIS, MERIS, VIIRS-SNPP&JPSS1, OLCI-S3A&S3B. Additional products using only OLCI upstreams are also delivered. * Recent products are organized in datasets called NRT (Near Real Time) and long time-series in datasets called REP/MY (Multi-Years). The NRT products are provided one day after satellite acquisition and updated a few days after in Delayed Time (DT) to provide a better quality. An uncertainty is given at pixel level for all products. To find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''GlobColour'''. See [http://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-030-032-033-037-081-082-083-085-086-098.pdf QUID document] for a detailed description and assessment. '''DOI (product) :''' https://doi.org/10.48670/moi-00096

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''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 wave length). 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-00143

'''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

'''DEFINITION''' The ocean monitoring indicator on mean sea level has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on mean sea level is derived from the DUACS delayed-time (DT-2024 version, “my” (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and by the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The time series of area averaged anomalies correspond to the area average of the maps in the Global Ocean weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from global GIA correction of -0.3mm/yr (common global GIA correction, see Spada, 2017). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit. The trend uncertainty of 0.3 mm/yr is provided at 90% confidence level using altimeter error budget (Quet et al 2024 [in prep.]). 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 depending on the period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. '''CONTEXT''' Change in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers(WCRP Global Sea Level Budget Group, 2018). According to the recent IPCC 6th assessment report (IPCC WGI, 2021), global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018 with a rate of rise that has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm/yr 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). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, 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). ""KEY FINDINGS "" Over the [1999/02/20 to 2025/10/18] period, global mean sea level rises at an average rate of 3.8  0.3 mm/year. This trend estimation is based on the altimeter measurements corrected from the global GIA correction (Spada, 2017) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). About 30% of this rise can be attributed to ocean thermal expansion (WCRP Global Sea Level Budget Group, 2018; von Schuckmann et al., 2018), 60% is due to land ice melt from glaciers and from the Antarctic and Greenland ice sheets. The remaining 10% is attributed to changes in land water storage, such as soil moisture, surface water and groundwater. From year to year, the global mean sea level record shows significant variations related mainly to the El Niño Southern Oscillation (Cazenave and Cozannet, 2014). '''DOI (product):''' https://doi.org/10.48670/moi-00237

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ocean monitoring indicator on 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 are also available in the Copernicus Marine Service catalogue (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The mean sea level evolution estimated in the global ocean (hereafter GMSL) 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 scare fit of sinusoidal function) and the time series is low-pass filtered (175 days cut-off). The time series is corrected for the effect of the Glacial Isostatic Adjustment using the ICE5G-VM2 GIA model (Peltier, 2004). During 1993-1998, the 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. Accounting for this correction changes the shape of the time series, which is no more linear but quadratic, indicating mean sea level acceleration during the altimetry era. 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). 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). '''CMEMS KEY FINDINGS''' Over the [1993/01/01, 2021/08/02] period, global mean sea level rises at a rate of 3.3  0.4 mm/year. This trend estimation is based on the altimeter measurements corrected from the Topex-A drift at the beginning of the time series (Legeais et al., 2020) and global GIA (Peltier, 2004). The observed global trend agrees with other recent estimates (Oppenheimer et al., 2019; IPCC WGI, 2021). '''DOI (product):''' https://doi.org/10.48670/moi-00237

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''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 wave length). 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