AndreaBozzo/ceres-open-data-index huggingface dataset:
Marine Heatwave Index in the Barents Sea from Reanalysis

'''DEFINITION'''

Temperature deviation from the 30-year (1991-2020) daily climatological mean temperature for the Barents Sea region (68°N - 80°N, 18°E - 55°E), relative to the difference between the daily climatological average and the 90th percentile above the climatological mean. Thus, when the index is above 1 the area is in a state of a marine heatwave, and when the index is below -1 the area is in a state of a marine cold spell, following the definition by Hobday et al. (2016). For further details, see Lien et al. (2024).""

'''CONTEXT'''
Anomalously warm oceanic events, often termed marine heatwaves, can potentially impact the ecosystem in the affected region. The marine heatwave concept and terminology was systemized by Hobday et al. (2016), and a generally adopted definition of a marine heatwave is a period of more than five days where the temperature within a region exceeds the 90th percentile of the seasonally varying climatological average temperature for that region. The Barents Sea region has warmed considerably during the most recent climatological average period (1991-2020) due to a combination of climate warming and positive phase of regional climate variability (e.g., Lind et al., 2018 ; Skagseth et al., 2020 ; Smedsrud et al., 2022), with profound consequences for marine life where boreal species are moving northward at the expense of arctic species (e.g., Fossheim et al., 2015; Oziel et al., 2020;  Husson et al., 2022).

'''KEY FINDINGS'''

There is a clear tendency of reduced frequency and intensity of marine cold spells, and a tendency towards increased frequency and intensity of marine heat waves in the Barents Sea. 

'''DOI (product):''' 
https://doi.org/10.48670/mds-00346
High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Baltic Sea

'''Short description:'''

For the Baltic Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.

'''DOI (product) :'''  
https://doi.org/10.48670/mds-00332
Atlantic- European North West Shelf- Ocean Biogeochemistry Reanalysis

'''Short  Description:'''

The ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butensch&ouml;n et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data.

The description of the model and its configuration, including the products validation is provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf CMEMS-NWS-QUID-004-011]. 

Products are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually,  providing a six-month extension of the time series. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf CMEMS-NWS-PUM-004-009_011] for details.

'''Associated products:'''

This model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009].
An analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 NWSHELF_MULTIYEAR_BGC_004_011].

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00058
Kuroshio Phase from Observations Reprocessing

'''DEFINITION'''

The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS delayed-time (CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. 

""CONTEXT"" 

The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014).

""CMEMS KEY FINDINGS"" 

The different states of the Kuroshio extension phase have been presented and validated by (Bessières et al., 2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00222
North Sea/Baltic Sea - Sea Surface Temperature Analysis L3S

'''Short description:''' 

For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00154
Arctic Ocean Sea Ice Drift REPROCESSED

Arctic sea ice drift datasets at 3, 6 and 30 day lags during winter. 
The Arctic low resolution sea ice drift products provided from IFREMER have a 62.5 km grid resolution for ASCAT, QuikSCAT, CFOSAT and SSM/I data. 
AMSR sea ice drift datasets have a 31.25 km grid resolution.
These products are delivered as daily products at 3, 6 and 30 days for the cold season extended at fall and spring : from September until May, it is updated on a monthly basis. The data are « merged » product from radiometer and scatterometer :

* QuikSCAT, ASCAT observations for over 20 years

* QuikSCAT, ASCAT & SSM/I merged products for over 20 years

* AMSR observations for over 20 years

* SSM/I 85 GHz V & H Merged product (1992-1999)

* CFOSAT observations


The exhaustive list can be found through the “Data access” link from the menu.

'''DOI (product) :'''  
https://doi.org/10.48670/moi-00126
GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS

'''Short description:'''

Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.

As a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.

This product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.

One file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00179
Atlantic- European North West Shelf- Wave Physics Reanalysis

'''Short  description:'''

This product provides long term hindcast outputs from a wave model for the North-West European Shelf. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a two-tier Spherical Multiple Cell grid mesh (3 and 1.5 km cells) derived from with the 1.5km grid used for [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013 NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013]. The model is forced by lateral boundary conditions from a Met Office Global wave hindcast. The atmospheric forcing is given by the [https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ECMWF ERA-5] Numerical Weather Prediction reanalysis. Model outputs comprise wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, consistent with physical ocean and wave analysis-forecast products. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-015.pdf CMEMS-NWS-PUM-004-015] for more information. Further details of the model, including source term physics, propagation schemes, forcing and boundary conditions, and validation, are provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-015.pdf CMEMS-NWS-QUID-004-015].
The product is updated biannually provinding six-month extension of the time series.

'''Associated products:'''

[https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014 NORTHWESTSHELF_ANALYSIS_FORECAST_WAV_004_014].

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00060
Arctic Ocean Sea Ice Analysis and Forecast

'''Short Description:'''

The Arctic Sea Ice Analysis and Forecast system uses the neXtSIM stand-alone sea ice model running the Brittle-Bingham-Maxwell sea ice rheology on an adaptive triangular mesh of 10 km average cell length. The model domain covers the whole Arctic domain, including the Canadian Archipelago and the Bering Sea. neXtSIM is forced with surface atmosphere forcings from the ECMWF (European Centre for Medium-Range Weather Forecasts) and ocean forcings from TOPAZ5, the ARC MFC PHY NRT system (002_001a). neXtSIM runs daily, assimilating manual ice charts, sea ice thickness from CS2SMOS in winter and providing 9-day forecasts. The output variables are the ice concentrations, ice thickness, ice drift velocity, snow depths, sea ice type, sea ice age, ridge volume fraction and albedo, provided at hourly frequency. The adaptive Lagrangian mesh is interpolated for convenience on a 3 km resolution regular grid in a Polar Stereographic projection. The projection is identical to other ARC MFC products.


'''DOI (product) :''' 

https://doi.org/10.48670/moi-00004
Black Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis

'''Short description:'''

For the Black Sea - the CNR diurnal sub-skin Sea Surface Temperature product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16° (0.0625°) horizontal resolution over the CMEMS Black Sea (BS) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS BS Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00157
North Pacific Gyre Area Chlorophyll-a time series and trend from Observations Reprocessing

'''DEFINITION'''

Oligotrophic subtropical gyres are regions of the ocean with low levels of nutrients required for phytoplankton growth and low levels of surface chlorophyll-a whose concentration can be quantified through satellite observations. The gyre boundary has been defined using a threshold value of 0.15 mg m-3 chlorophyll for the Atlantic gyres (Aiken et al. 2016), and 0.07 mg m-3 for the Pacific gyres (Polovina et al. 2008). The area inside the gyres for each month is computed using monthly chlorophyll data from which the monthly climatology is subtracted to compute anomalies. A gap filling algorithm has been utilized to account for missing data inside the gyre.  Trends in the area anomaly are then calculated for the entire study period (September 1997 to December 2021).

'''CONTEXT'''

Oligotrophic gyres of the oceans have been referred to as ocean deserts (Polovina et al. 2008). They are vast, covering approximately 50% of the Earth’s surface (Aiken et al. 2016). Despite low productivity, these regions contribute significantly to global productivity due to their immense size (McClain et al. 2004). Even modest changes in their size can have large impacts on a variety of global biogeochemical cycles and on trends in chlorophyll (Signorini et al 2015). Based on satellite data, Polovina et al. (2008) showed that the areas of subtropical gyres were expanding. The Ocean State Report (Sathyendranath et al. 2018) showed that the trends had reversed in the Pacific for the time segment from January 2007 to December 2016. 

'''CMEMS KEY FINDINGS'''

The trend in the North Pacific gyre area for the 1997 Sept – 2021 December period was positive, with a 1.75% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 2.17% reported for the 1997-2020 period. The trend is statistically significant (p<0.05). 
During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was negative (-0.26% year-1) in the North Pacific gyre relative to 2000-01-01 values. This trend is slightly less negative than the trend of -0.31% year-1 for the 1997-2020 period, though the sign of the trend remains unchanged and is statistically significant (p<0.05).  It must be noted that the difference is small and within the uncertainty of the calculations, indicating that the trend is significant, however there may be no change associated with the timeseries extension.
For 2016, The Ocean State Report (Sathyendranath et al. 2018) reported a large increase in gyre area in the Pacific Ocean (both North and South Pacific gyres), probably linked with the 2016 ENSO event which saw large decreases in chlorophyll in the Pacific Ocean. 

'''DOI (product):''' 
https://doi.org/10.48670/moi-00227
Global Ocean Sea Surface Temperature time series and trend from Observations Reprocessing

'''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
Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (Near Real Time)

'''Short description:'''

For the '''Black Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR – Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets:
* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Zibordi et al., 2015; Kajiyama et al., 2018), and the interpolated '''gap-free''' Chl concentration (to provide a ""cloud free"" product) estimated by means of a modified version of the DINEOF algorithm (Volpe et al., 2018)
* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''""multi'''"" observations achieved via region-specific algorithm, Volpe et al., 2019)
* '''''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 resolutions''': monthly and daily (for '''""gap-free""''' and '''""pp""''' data)

'''Spatial resolutions''': 1 km for '''""multi""''' (4 km for '''""pp""''') and 300 meters for '''""olci""'''

To find this product in the catalogue, use the search keyword '''""OCEANCOLOUR_BLK_BGC_L4_NRT""'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00302
High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Black Sea

'''Short description:'''

For the Black Sea - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Near Real-Time Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are updated several times daily to provide the best product timeliness.

'''DOI (product) :'''  
https://doi.org/10.48670/mds-00333
Baltic Sea Surface Ocean Colour Plankton from Sentinel-3 OLCI L4 monthly observations

'''Short description:'''

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

'''Upstreams''': OLCI-S3A & S3B 

'''Temporal resolution''': monthly 

'''Spatial resolution''': 300 meters 

To find this product in the catalogue, use the search keyword '''""OCEANCOLOUR_BAL_BGC_L4_NRT""'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00295
GLOBAL OCEAN GRIDDED L4 SEA SURFACE HEIGHTS AND DERIVED VARIABLES REPROCESSED (COPERNICUS CLIMATE SERVICE)

'''Short description:''' 

DUACS delayed-time altimeter gridded maps of sea surface heights and derived variables over the global Ocean (https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global?tab=overview). The processing focuses on the stability and homogeneity of the sea level record (based on a stable two-satellite constellation) and the product is dedicated to the monitoring of the sea level long-term evolution for climate applications and the analysis of Ocean/Climate indicators. These products are produced and distributed by the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/).

'''DOI (product):'''
https://doi.org/10.48670/moi-00145
Baltic Sea - Diurnal Subskin Sea Surface Temperature Analysis

'''Short description:'''

For the Baltic Sea - the DMI Sea Surface Temperature Diurnal Subskin L4 aims at providing hourly analysis of the diurnal subskin signal at 0.02deg. x 0.02deg. horizontal resolution, using the BAL L4 NRT product as foundation temperature and satellite data from infra-red radiometers. Uses SST satellite products from the sensors: Metop B AVHRR, Sentinel-3 A/B SLSTR, VIIRS SUOMI NPP, NOAA20 and 21. 

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00309