AndreaBozzo/ceres-open-data-index huggingface dataset:
Iberia Biscay Ireland Sea Surface Temperature trend map from Observations Reprocessing

'''DEFINITION'''

The omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2024, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from re-processed ESA SST CCI, C3S (Embury et al., 2024). Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. 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 0.012 ±0.001 °C/year over the period 1982-2024.    

'''DOI (product):''' 
https://doi.org/10.48670/moi-00257
Global Total (COPERNICUS-GLOBCURRENT), Ekman and Geostrophic currents at the Surface and 15m

'''Short description:'''

This product is a  L4 REP and NRT global total velocity field at 0m and 15m together wiht its individual components (geostrophy and Ekman) and related uncertainties. It consists of the zonal and meridional velocity at a 1h frequency and at 1/4 degree regular grid. The total velocity fields are obtained by combining CMEMS  satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).

'''DOI (product) :''' 
https://doi.org/10.48670/mds-00327
SSS SMOS/SMAP L4 OI - LOPS-v2025

'''Short description:'''

The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information.

Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/

'''DOI (product) :''' 
https://doi.org/10.48670/mds-00369
Global Ocean Colour Plankton and Reflectances MY L3 daily observations

'''Short description:'''

For the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs.
* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''""multi""''' products.
* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT''') and  Reflectance ('''RRS''').

* Temporal resolutions: '''daily''', '''monthly'''.
* Spatial resolutions: '''4 km''' (multi).
* 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 these products in the catalogue, use the search keyword '''""ESA-CCI""'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00282
Mediterranean Sea, Bio-Geo-Chemical, L3, daily Satellite Observations (1997-ongoing)

'''Short description:'''

For the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR – Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:
* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017)
* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)
* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''"multi'''" observations achieved via region-specific algorithm, Volpe et al., 2019)
* '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates)
* '''''pp''''' with the Integrated Primary Production (PP)

'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''"multi"''' products, and OLCI-S3A & S3B for the '''"olci"''' products

'''Temporal resolution''': daily

'''Spatial resolution''': 1 km for '''"multi"''' and 300 meters for '''"olci"'''

To find this product in the catalogue, use the search keyword '''"OCEANCOLOUR_MED_BGC_L3_MY"'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00299
Black Sea - High Resolution and Ultra High Resolution L3S Sea Surface Temperature

'''Short description:''' 

This product provides daily (nighttime), merged multi-sensor (Level-3S, L3S) 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 and is produced by the Italian National Research Council – Institute of Marine Sciences (CNR-ISMAR).
L3S 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.
These L3S data serve as input to an optimal interpolation procedure used to generate gap-free Level-4 (L4) SST fields, as implemented in product 010_006 (Buongiorno Nardelli et al., 2009; Buongiorno Nardelli et al., 2013). 

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00158
Global Ocean- in-situ Near real time observations of ocean currents

'''Short description'''

 This product is entirely dedicated to ocean current data observed in near-real time. Current data from 3 different types of instruments are distributed:
* The near-surface zonal and meridional velocities calculated along the trajectories of the drifting buoys which are part of the DBCP’s Global Drifter Program. These data are delivered together with wind stress components, surface temperature and a wind-slippage correction for drogue-off and drogue-on drifters trajectories. 
* The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency radars that are part of the European High Frequency 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 and in surface, calculated along the trajectories of the floats which are part of the Argo Program.

'''DOI (product) :'''
https://doi.org/10.48670/moi-00041
SAR Sea Ice Berg Concentration and Individual Icebergs Observed with Sentinel-1 & RCM

'''Short description:''' 

The iceberg product contains 9 (6+3) datasets:
Six gridded datasets in netCDF format:
IW, EW and RCMNL modes and mosaic for the two modes) describing iceberg concentration as number of icebergs counted within 10x10 km grid cells. The iceberg concentration is derived by applying a Constant False Alarm Rate (CFAR) algorithm on data from Synthetic Aperture Radar (SAR) satellite sensors.
Three datasets – individual iceberg positions – in shapefile format:
The shapefile format allows the best representation of the icebergs. Each shapefile-dataset also includes a shapefile holding the polygonized satellite coverage
Despite its precision (individual icebergs are proposed), this product is a generic and automated product and needs expertise to be correctly used. For all applications concerning marine navigation, please refer to the national Ice Service of the country concerned.

'''DOI (product) :'''  
https://doi.org/10.48670/moi-00129
Baltic Sea Multiyear Ocean Colour Plankton monthly observations

'''Short description:'''

For the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR – Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:
* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021)

'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv5) for the '''""multi""''' products, and OLCI-S3A & S3B for the '''""olci""''' products

'''Temporal resolutions''': monthly

'''Spatial resolution''': 1 km for '''""multi""''' and 300 meters for '''""olci""'''

To find this product in the catalogue, use the search keyword '''""OCEANCOLOUR_BAL_BGC_L4_MY""'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00308
Atlantic Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (daily interpolated) from Satellite Observations (1997-ongoing)

'''Short description: '''

For the '''Atlantic''' 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''').



* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a "cloud free" product.

* Spatial resolutions: '''1 km'''.

* 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-00289
Mediterranean Sea - High Resolution L4 Sea Surface Temperature Reprocessed

'''Short description:''' 
 
The Reprocessed (REP) Mediterranean (MED) dataset provides a stable and consistent long-term Sea Surface Temperature (SST) time series over the Mediterranean Sea (and the adjacent North Atlantic box) developed for climate applications. This product consists of daily (nighttime), optimally interpolated (L4), satellite-based estimates of the foundation SST (namely, the temperature free, or nearly-free, of any diurnal cycle) at 0.05° resolution grid covering the period from 1st January 1981 to present (approximately one month before real time). The MED-REP-L4 product is built from a consistent reprocessing of the collated level-3 (merged single-sensor, L3C) climate data record (CDR) v.3.0, provided by the ESA Climate Change Initiative (CCI) and covering the period up to 2021, and its interim extension (ICDR) that allows the regular temporal extension for 2022 onwards. 

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00173
Northward Heat Transport for Global Ocean, Atlantic and Indian+Pacific basins from Reanalysis

'''DEFINITION'''

Meridional Heat Transport is computed by integrating the heat fluxes along the zonal direction and from top to bottom of the ocean. 
They are given over 3 basins (Global Ocean, Atlantic Ocean and Indian+Pacific Ocean) and for all the grid points in the meridional grid of each basin. 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'''

After an anusual 2016 year (Bricaud 2016), with a higher global meridional heat transport in the tropical band explained by, the increase of northward heat transport at 5-10 ° N in the Pacific Ocean during the El Niño event, 2017 northward heat transport is lower than the 1993-2014 reference value in the tropical band, for both Atlantic and Indian + Pacific Oceans. At the higher latitudes, 2017 northward heat transport is closed to 1993-2014 values.

Note: The key findings will be updated annually in November, in line with OMI evolutions.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00246
Nino 3.4 Sea Surface Temperature time series from Reanalysis

'''DEFINITION'''

NINO34 sea surface temperature anomaly (°C) is defined as the difference between the sea surface temperature averaged over the 170°W-120°W 5°S,-5°N area  and the climatological reference value over same area  (GLOBAL_MULTIYEAR_PHY_ENS_001_031) . Spatial averaging was weighted by surface area. Monthly mean values are given here. The reference period is 1993-2014. El Nino or La Nina events are defined when the NINO3.4 SST anomalies exceed +/- 0.4°C during a period of six month.

'''CONTEXT'''

El Nino Southern Oscillation (ENSO) is one of the most  important source of climatic  variability resulting  from a strong coupling between ocean and atmosphere in the central tropical Pacific and affecting surrounding populations.  Globally, it impacts ecosystems, precipitation, and freshwater resources (Glantz, 2001). ENSO is mainly characterized by two anomalous states that last from several months to more than a year and recur irregularly on a typical time scale of 2-7 years. The warm phase El Niño is broadly characterized by a weakening of the easterly trade winds at interannual timescales associated with surface and subsurface processes leading to a surface warming in the eastern Pacific. Opposite changes are observed during the cold phase La Niña (review in Wang et al., 2017). Nino 3.4 Sea surface Temperature Anomaly is a good indicator of the state of the Central tropical Pacific El Nino conditions and enable to monitor the evolution the ENSO phase.

'''CMEMS KEY FINDINGS'''

Over the 1993-2023 period, there were several episodes of strong positive ENSO phases in particular in 1998 and 2016, where NINO3.4 indicator reached positive values larger than 2°C (and remained above 0.5°C during more than 6 months). Several La Nina events were also observed like in 2000 and 2008.  
The NINO34 indicator is a good index to monitor the state of ENSO phase and a useful tool to help seasonal forecasting of meteorological conditions. 

Note: The key findings will be updated annually in November, in line with OMI evolutions.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00219
Baltic Sea ice concentration, extent, and classification time series

Gridded sea ice concentration, sea ice extent and classification based on the digitized Baltic ice charts produced by the FMI/SMHI ice analysts. It is produced daily in the afternoon, describing the ice situation daily at 14:00 EET. The nominal resolution is about 1km. The temporal coverage is from the beginning of the season 1980-1981 until today.

'''DOI (product) :'''  
https://doi.org/10.48670/moi-00131
Black Sea Oxygen Trend from Observations Reprocessing

'''DEFINITION'''

The oxygenation status of the Black Sea open basin is described by three complementary indicators, derived from vertical profiles and spatially averaged over the Black Sea open basin (depth > 50m). (1) The oxygen penetration depth is the depth at which  [O2] < 20µM, expressed in [m]. (2) The oxygen penetration density is the potential density anomaly at the oxygen penetration depth [kg/m³]. (3) The oxygen inventory is the vertically integrated oxygen content [mol O2/m²]. The 20µM threshold was chosen to minimize the indicator sensitivity to sensor’s precision. Those three metrics are complementary: Oxygen penetration depth is more easily understood, but present more spatial variability. Oxygen penetration density helps in dissociating biogeochemical processes from shifts in the physical structure. Although less intuitive, the oxygen inventory is a more integrative diagnostic and its definition is more easily transposed to other areas.

'''CONTEXT'''

The Black Sea is permanently stratified, due to the contrast in density between large riverine and Mediterranean inflows. This stratification restrains the ventilation of intermediate and deep waters and confines, within a restricted surface layer, the waters that are oxygenated by photosynthesis and exchanges with the atmosphere. The vertical extent of the oxic layer determines the volume of habitat available for pelagic populations (Ostrovskii and Zatsepin 2011, Sakınan and Gücü 2017) and present spatial and temporal variations (Murray et al. 1989; Tugrul et al. 1992; Konovalov and Murray 2001). At long and mid-term, these variations can be monitored with three metrics (Capet et al. 2016), derived from the vertical profiles that can obtained from traditional ship casts or autonomous Argo profilers (Stanev et al., 2013). A large source of uncertainty associated with the spatial and temporal average of those metrics stems from the small number of Argo floats, scarcely adequate to sample the known spatial variability of those metrics.

'''CMEMS KEY FINDINGS'''

During the past 60 years, the vertical extent of the Black Sea oxygenated layer has narrowed from 140m to 90m (Capet et al. 2016). The Argo profilers active for 2016 suggested an ongoing deoxygenation trend and indicated an average oxygen penetration depth of 72m at the end of 2016, the lowest value recorded during the past 60 years. The oxygenation of subsurface water is closely related to the intensity of cold water formation, an annual ventilation processes which has been recently limited by warmer-than-usual winter air temperature (Capet et al. 2020). In 2017, 2018 and 2020, cold waters formation resulted in a partial reoxygenation of the intermediate layer. Yet, such ventilation has been lacking in winter 2020-2021, and the updated 2021 indicators reveals the lowest oxygen inventory ever reported in this OMI time series. This results in significant detrimental trends now depicted also over the Argo period (2012-2021).

'''DOI (product):''' 
https://doi.org/10.48670/moi-00213
Global Ocean Chlorophyll-a trend map from Observations Reprocessing

'''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