AndreaBozzo/ceres-open-data-index huggingface dataset:
Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (Near Real Time)

'''Short description: '''

For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.
* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''multi''' products, and S3A & S3B only for the '''olci''' products.
* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').

* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a '''cloud free''' product.
* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.
* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').

To find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''GlobColour'''. 

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00279
Arctic Ocean - High Resolution Sea Ice Information L3

'''Short description:''' 

Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid.

'''DOI (product) :'''   
https://doi.org/10.48670/mds-00343
Arctic Ocean Biogeochemistry Analysis and Forecast

'''Short description:'''

The operational TOPAZ5-ECOSMO Arctic Ocean system uses the ECOSMO biological model coupled online to the TOPAZ5 physical model (ARCTIC_ANALYSISFORECAST_PHY_002_001 product). It is run daily to provide 10 days of forecast of 3D biogeochemical variables ocean. The coupling is done by the FABM framework.

Coupling to a biological ocean model provides a description of the evolution of basic biogeochemical variables. The output consists of daily mean fields interpolated onto a standard grid and 40 fixed levels in NetCDF4 CF format. Variables include 3D fields of nutrients (nitrate, phosphate, silicate), phytoplankton and zooplankton biomass, oxygen, chlorophyll, primary productivity, carbon cycle variables (pH, dissolved inorganic carbon and surface partial CO2 pressure in seawater) and light attenuation coefficient. Surface Chlorophyll-a from satellite ocean colour is assimilated every week and projected downwards using a modified Ardyna et al. (2013) method. A new 10-day forecast is produced daily using the previous day's forecast and the most up-to-date prognostic forcing fields.
Output products have 6.25 km resolution at the North Pole (equivalent to 1/8 deg) on a stereographic projection. See the Product User Manual for the exact projection parameters.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00003
Global Ocean Waves Reanalysis

'''Short description:'''

GLOBAL_MULTIYEAR_WAV_001_032 for the global wave reanalysis describing past sea states since years 1980. This product also bears the name of WAVERYS within the GLO-HR MFC for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5° irregular grid. Average wave quantities derived from this wave spectrum such as the SWH (significant wave height) or the average wave period are delivered on a regular 1/5° grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. WAVERYS takes into account oceanic currents from the GLORYS12 physical ocean reanalysis and assimilates SWH observed from historical altimetry missions and directional wave spectra from Sentinel 1 SAR from 2017 onwards.

'''DOI (product):'''
https://doi.org/10.48670/moi-00022
Black Sea Overturning Circulation Index from Reanalysis

'''DEFINITION'''

This ocean monitoring indicator (OMI) provides a time series of Meridional Overturning Circulation (MOC) Strength in density coordinates, area-averaged and calculated for the period from 1993 to the most recent year with the availability of reanalysis data in the Black Sea (BS). It contains 1D (time dimension) maximum MOC data computed from the Black Sea Reanalysis (BLK-REA; BLKSEA_MULTIYEAR_PHY_007_004) (Ilicak et al., 2022). The MOC is calculated by summing the meridional transport provided by the Copernicus Marine BLK-REA within density bins. The Black Sea MOC indicator represents the maximum MOC value across the basin for a density range between 22.45 and 23.85 kg/m³, which corresponds approximately to a depth interval of 25 to 80 m. To understand the overturning circulation of the Black Sea, we compute the residual meridional overturning circulation in density space. Residual overturning as a function of latitude (y) and density (σ ̅) bins can be computed as follows:
ψ^* (y,σ ̅ )=-1/T ∫_(t_0)^(t_1)▒∫_(x_B1)^(x_B2)▒〖∫_(-H)^0▒H[σ ̅-σ(x,y,z,t)] ×ν(x,y,z,t)dzdxdt,〗
where H is the Heaviside function and ν is the meridional velocity. We used 100 σ_2  (potential density anomaly with reference pressure of 2000 dbar) density bins to remap the mass flux fields.

'''CONTEXT'''

The BS meridional overturning circulation (BS-MOC) is a clockwise circulation in the northern part up to 150 m connected to cold intermediate layer (CIL) and an anticlockwise circulation in the southern part that could be connected to the influence of the Mediterranean Water inflow into the BS. In contrast to counterparts observed in the deep Atlantic and Mediterranean overturning circulations, the BS-MOC is characterized by shallowness and relatively low strength. However, its significance lies in its capacity to monitor the dynamics and evolution of the CIL which is crucial for the ventilation of the subsurface BS waters. The monitoring of the BS-MOC evolution from the BLK-REA can support the understanding how the CIL formation is affected due to climate change.  The study of Black Sea MOC is relatively new. For more details, see Ilicak et al., (2022).

'''KEY FINDINGS'''

The MOC values show a significant decline from 1994 to 2009, corresponding to the reduction in the CIL during that period. However, after 2010, the MOC in the Black Sea increased from 0.07 Sv (1 Sv = 106 m3/s) to 0.10 Sv. The CIL has nearly disappeared in recent years, as discussed by Stanev et al. (2019) and Lima et al. (2021) based on observational data and reanalysis results. The opposite pattern observed since 2010 suggests that mechanisms other than the CIL may be influencing the Black Sea MOC.
For the OMI we have used an updated version of the reanalysis (version E4R1) which has a different spinup compared to the OSR6 (version E3R1).

'''DOI (product):''' 
https://doi.org/10.48670/mds-00349
North Atlantic Ocean Chlorophyll-a time series and trend from Observations Reprocessing

'''DEFINITION'''

The time series are derived from the regional chlorophyll reprocessed (REP) products as distributed by CMEMS which, in turn, result from the application of the regional chlorophyll algorithms over remote sensing reflectances (Rrs) provided by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al. 2019; Jackson 2020). Daily regional mean values are calculated by performing the average (weighted by pixel area) over the region of interest. A fixed annual cycle is extracted from the original signal, using the Census-I method as described in Vantrepotte et al. (2009). The deseasonalised time series is derived by subtracting the mean seasonal cycle from the original time series, and then fitted to a linear regression to, finally, obtain the linear trend. 

'''CONTEXT'''

Phytoplankton – and chlorophyll concentration as a proxy for phytoplankton – respond rapidly to changes in environmental conditions, such as temperature, light and nutrients availability, and mixing. The response in the North Atlantic ranges from cyclical to decadal oscillations (Henson et al., 2009); it is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the North Atlantic are known to respond to climate variability associated with the North Atlantic Oscillation (NAO), with the initiation of the spring bloom showing a nominal correlation with sea surface temperature and the NAO index (Zhai et al., 2013).

'''CMEMS KEY FINDINGS'''

While the overall trend average for the 1997-2021 period in the North Atlantic Ocean is slightly positive (0.16 ± 0.12 % per year), an underlying low frequency harmonic signal can be seen in the deseasonalised data. The annual average for the region in 2021 is 0.25 mg m-3. Though no appreciable changes in the timing of the spring and autumn blooms have been observed during 2021, a lower peak chlorophyll concentration is observed in the timeseries extension. This decrease in peak concentration with respect to the previous year is contributing to the reduction trend.

'''DOI (product):'''
https://doi.org/10.48670/moi-00194
European North West Shelf Sea Surface Temperature time series and trend from Observations Reprocessing

'''DEFINITION'''

The omi_climate_sst_northwestshelf_area_averaged_anomalies product for 2024 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1982 and averaged over the European North West Shelf Seas. The NORTHWESTSHELF SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish areas(SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST- NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from re-processed ESA SST CCI, C3S (Embury et al., 2019). Anomalies are computed against the 1991-2020 reference period. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018).

'''CONTEXT'''

Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). 

'''CMEMS KEY FINDINGS '''

The overall trend in the SST anomalies in this region is 0.016 ±0.001 °C/year over the period 1982-2024.  

'''DOI (product):''' 
https://doi.org/10.48670/moi-00275
Global Ocean-Delayed Mode in-situ Observations of surface and sub-surface ocean currents

"'Short description: '''

Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed:
* The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided.
* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata.
* The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program.
* The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms
* The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories

'''DOI (product) :'''
https://doi.org/10.17882/86236
Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis

'''Short description:''' 

This product provides daily (nighttime), gap-free (Level-4, L4) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Mediterranean Sea, at high (HR, 1/16°) and ultra-high (UHR, 1/100°) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values (centered at 00:00 UTC) and is produced by the Italian National Research Council – Institute of Marine Sciences (CNR-ISMAR). Additionally, each daily SST L4 dataset, both HR and UHR, is accompanied by a corresponding anomaly L4 dataset, representing the daily SST anomaly computed using the 1991–2020 reference period, in accordance with the World Meteorological Organization (WMO) recommendations.
L4 maps are generated by selecting only the highest-quality SST observations from upstream Level-2 (L2) data acquired within a short local nighttime window, in order to minimize cloud contamination and avoid the effects of the diurnal cycle. The main L2 sources currently ingested include SLSTR from Sentinel-3A and -3B, VIIRS from NOAA-21, NOAA-20, and Suomi-NPP, AVHRR from Metop-B and -C, and SEVIRI. A two-step algorithm allows to interpolate SST data at high and ultra-high spatial resolution, applying statistical techniques (Buongiorno Nardelli et al., 2013; Buongiorno Nardelli et al., 2015). Additionally, from 2024 onwards, an improved first-guess field has been used in the generation of the MED UHR L4 data, enhancing the product's spatial resolution of SST features and the accuracy of SST gradients via machine learning techniques (Fanelli et al., 2024).

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00172
GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS

'''Short description:'''

Near-Real-Time gridded multi-mission merged satellite significant wave height, based on Copernicus Marine level-3 SWH datasets. Only valid data are included. It merges multiple along-track SWH data (Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT, SWOT-nadir, HaiYang-2B and HaiYang-2C). There are two datasets in the product, the first provides daily gridded data at a 2° horizontal resolution of VAVH_DAILY fields which are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC. The second, provides VAVH_INST field  which is an estimate of the instantaneous wave field every 6h, and 0.5° spatial resolution, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00180
Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density

'''Short description:'''

This product consists of daily global gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/8° of resolution, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite SST information. The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and provide both NRT and MY daily and monthly datasets. 

Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.

'''DOI (product) :''' 
https://doi.org/10.48670/moi-00051
Black Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis

'''Short description:''' 

This product provides daily (nighttime), gap-free (Level-4, L4) maps of foundation Sea Surface Temperature (SST) - that is, the SST free from diurnal warming - over the Black Sea, at high (HR, 1/16°) and ultra-high (UHR, 1/100°) spatial resolutions, covering the period from 2008 to present. Each map represents nighttime SST values (centered at 00:00 UTC) and is produced by the Italian National Research Council – Institute of Marine Sciences (CNR-ISMAR). Additionally, each daily SST L4 dataset, both HR and UHR, is accompanied by a corresponding anomaly L4 dataset, representing the daily SST anomaly computed using the 1991–2020 reference period, in accordance with the World Meteorological Organization (WMO) recommendations.
L4 maps are generated by selecting only the highest-quality SST observations from upstream Level-2 (L2) data acquired within a short local nighttime window, in order to minimize cloud contamination and avoid the effects of the diurnal cycle. The main L2 sources currently ingested include SLSTR from Sentinel-3A and -3B, VIIRS from NOAA-21, NOAA-20, and Suomi-NPP, AVHRR from Metop-B and -C, and SEVIRI. A two-step algorithm allows to interpolate SST data at high and ultra-high spatial resolution, applying statistical techniques (Buongiorno Nardelli et al., 2009; Buongiorno Nardelli et al., 2013).

'''DOI (product) :'''  
https://doi.org/10.48670/moi-00159
Global coastal satellite derived bathymetry static

'''DEFINITION'''

Important note to users:  These data are not to be used for navigation. The data is 100 m resolution and as high quality as possible. It has been produced with state-of-the-art technology and validated to the best of the producer’s ability and where sufficient high-quality data were available. These data could be useful for planning and modelling purposes. The user should independently assess the adequacy of any material, data and/or information of the product before relying upon it. Neither Mercator Ocean International/Copernicus Marine Service nor the data originators are liable for any negative consequences following direct or indirect use of the product information, services, data products and/or data.

Product overview: This is a satellite derived bathymetry product covering the global coastal area (where data retrieval is possible), with 100 m resolution, based on Sentinel-2. This global coastal product has been developed based on 3 methodologies: Intertidal Satellite-Derived Bathymetry; Physics-based optical Satellite-Derived Bathymetry from RTE inversion; and Wave Kinematics Satellite-Derived Bathymetry from wave dispersion.

There is one dataset for each of the methods (including a quality index based on uncertainty) and an additional one where the three datasets were merged (also includes a quality index). Using their expertise and special techniques the consortium tried to achieve an optimal balance between coverage and data quality.

'''DOI (product):''' 
https://doi.org/10.48670/mds-00364
Northwestern Atlantic Ocean Physics and Ice Analysis and Forecast

COLAB-MFC provides a high-resolution ocean physics (PHY) and sea ice (ICE) NRT product for the North-West Atlantic Ocean (NWATL) area along the North American coastline, starting from 7/07/2026 and being regularly extended. This new product also includes a historic catalogue starting from 2025-05-21. The COLAB-PHY-ICE NRT model system is developed and run by Environment Climate Change Canada (ECCC) in charge to produce the native products from 21/05/2025 and for the periodic temporal extensions. On the other side, the post-processed product to deliver to end-users in the standardized Copernicus Marine format is done by NOW Systems with the support of CESGA in terms of supercomputing resources. 

'''DOI (product) :'''   
https://doi.org/10.48670/mds-00381
Mediterranean Sea Chlorophyll-a time series and trend from Observations Reprocessing

'''DEFINITION'''

The time series are derived from the regional chlorophyll reprocessed (MY) product as distributed by CMEMS (OCEANCOLOUR_MED_BGC_L3_NRT_009_141). This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2023). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. This OMI has been introduced since the 2nd issue of Ocean State Report in 2017.

'''CONTEXT'''

Phytoplankton   and chlorophyll concentration as a proxy for phytoplankton   respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016).

'''KEY FINDINGS'''

In the Mediterranean Sea, the average chlorophyll trend for the 1997–2024 period is slightly negative, at -0.77 ± 0.59% per year, reinforcing the findings of the previous releases. This result contrasts with the analysis by Sathyendranath et al. (2018), which reported increasing chlorophyll concentrations across all European seas. From around 2010–2011 onward, excluding the 2018–2019 period, a noticeable decline in chlorophyll levels is evident in the deseasonalized time series (green line) and in the observed maxima (grey line), particularly from 2015. This sustained decline over the past decade contributes to the overall negative trend observed in the Mediterranean Sea.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00259
Global Ocean - Delayed Mode Wave product

'''Short description:'''

These products integrate wave observations aggregated and validated from the Regional EuroGOOS consortium (Arctic-ROOS, BOOS, NOOS, IBI-ROOS, MONGOOS) and Black Sea GOOS as well as from National Data Centers (NODCs) and JCOMM global systems (OceanSITES, DBCP) and the Global telecommunication system (GTS) used by the Met Offices.

'''DOI (product) :'''  
https://doi.org/10.17882/70345