AndreaBozzo/ceres-open-data-index huggingface dataset:
Arctic Sea Ice Extent from Reanalysis

'''DEFINITION'''

Estimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to:
i) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change.
ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss).
The Arctic sea ice extent used here is based on the “multi-product”  (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread.

'''CONTEXT'''

Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019).  
The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018).

'''CMEMS KEY FINDINGS'''

Since the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time.  

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

'''DOI (product):''' 
https://doi.org/10.48670/moi-00190
Arctic Ocean Wave Analysis and Forecast

'''Short description:'''

The Arctic Ocean Wave Analysis and Forecast system uses the WAM model at 3 km resolution forced with surface winds and boundary wave spectra from the ECMWF (European Centre for Medium-Range Weather Forecasts) together with tidal currents and ice from the ARC MFC forecasts (Sea Ice concentration and thickness). WAM runs twice daily providing one hourly 10 days forecast and one hourly 5 days forecast. From the output variables the most commonly used are significant wave height, peak period and mean direction.

'''DOI (product) :'''  
https://doi.org/10.48670/moi-00002
Global Ocean Daily Gridded Reprocessed L3 Sea Surface Winds from Scatterometer

'''Short description:''' 

For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding  to the L2 swath products:
*0.5 degrees grid for the 50 km scatterometer L2 inputs,
*0.25 degrees grid based on 25 km scatterometer swath observations,
*and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. 

The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees;

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00183
Global Ocean Sea Surface Temperature trend map 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.	The time series for each grid cell was passed through the X11 seasonal adjustment procedure, which decomposes a 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. 
5.	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'''

Warming trends occurred over most of the globe between 1982 and 2024, with the strongest warming in the Northern Pacific and Atlantic Oceans. However, there were cooling trends in parts of the Southern Ocean and the South-East Pacific Ocean.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00243
Mediterranean Sea sea surface temperature extreme variability mean and anomaly (observations)

'''DEFINITION'''

The OMI_EXTREME_SST_MEDSEA_sst_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea surface temperature measured by in situ buoys at depths between 0 and 5 meters. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018).  

'''CONTEXT'''

Sea surface temperature (SST) is one of the essential ocean variables affected by climate change (mean SST trends, SST spatial and interannual variability, and extreme events). In Europe, several studies show warming trends in mean SST for the last years (von Schuckmann et al., 2016; IPCC, 2021, 2022). An exception seems to be the North Atlantic, where, in contrast, anomalous cold conditions have been observed since 2014 (Mulet et al., 2018; Dubois et al. 2018; IPCC 2021, 2022). Extremes may have a stronger direct influence in population dynamics and biodiversity. According to Alexander et al. 2018 the observed warming trend will continue during the 21st Century and this can result in exceptionally large warm extremes. Monitoring the evolution of sea surface temperature extremes is, therefore, crucial.The Mediterranean Sea has showed a constant increase of the SST in the last three decades across the whole basin with more frequent and severe heat waves (Juza et al., 2022). Deep analyses of the variations have displayed a non-uniform rate in space, being the warming trend more evident in the eastern Mediterranean Sea with respect to the western side. This variation rate is also changing in time over the three decades with differences between the seasons (e.g. Pastor et al. 2018; Pisano et al. 2020), being higher in Spring and Summer, which would affect the extreme values.

'''COPERNICUS MARINE SERVICE KEY FINDINGS'''

The mean 99th percentiles showed in the area present values from 25ºC in Ionian Sea and 26º in the Alboran sea and Gulf of Lion to 27ºC in the East of Iberian Peninsula. The standard deviation ranges from 0.6ºC to 1.2ºC in the Western Mediterranean and is around 2.2ºC in the Ionian Sea.
Results for this year show a slight negative anomaly in the Ionian Sea (-1ºC) inside the standard deviation and a clear positive anomaly in the Western Mediterranean Sea reaching +2.2ºC, almost two times the standard deviation in the area.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00267
GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS

'''Short description:'''

Multi-Year gridded multi-mission merged satellite significant wave height  based on CMEMS Multi-Year level-3 SWH datasets itself based on the ESA Sea State Climate Change Initiative data Level 3 product (see the product WAVE_GLO_PHY_SWH_L3_MY_014_005). Only valid data are included. It merges along-track SWH data from the following missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa, Jason-3 and CFOSAT. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC on a 2° horizontal grid ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon) on a 0.5° horizontal grid, using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00177
Atlantic - European North West Shelf - Ocean Wave Analysis and Forecast

'''Short description:'''

The NWSHELF_ANALYSISFORECAST_WAV_004_014 is produced by a coupled hydrodynamic-wave model system, implemented over the North East Atlantic and Shelf Seas at 1.5 km of horizontal resolution. The product is updated daily, providing 7-day forecast of 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-state, and swell components. 
Products are provided at hourly frequency.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00055
South 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.  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 South Pacific gyre area for the 1997 Sept – 2021 December period was positive, with a 0.04% increase in area relative to 2000-01-01 values. Note that this trend is lower than the 0.16% change for the 1997-2020 period, with the sign of the trend remaining unchanged and is not statistically significant (p<0.05).  An underlying low frequency signal is observed with a period of approximately a decade.
During the 1997 Sept – 2021 December period, the trend in chlorophyll concentration was positive (0.66% year-1) in the South Pacific gyre relative to 2000-01-01 values. This rate has increased compared to the rate of  0.45% year-1 for the 1997-2020 period and remains statistically significant (p<0.05).  In the last two years of the timeseries, an increase in the variation from the mean is observed.
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-00229
MEDITERRANEAN SEA MEAN DYNAMIC TOPOGRAPHY

'''Short description:'''

The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00151
Black Sea wave power

'''DEFINITION'''

The Wave Power P is defined by:
P=(ρg^2)/64π H_s^2 T_e
where ρ is the surface water density, g the acceleration due to gravity, Hs the significant wave height (VHM0), and Te the wave energy period (VTM10) also abbreviated with Tm-10 based on the Black Sea wave model reanalysis (product BLKSEA_MULTIYEAR_WAV_007_006). The extreme statistics and related recent changes are defined by (1) the 99th percentile of the Wave Power, (2) the linear trend of 99th percentile of the Wave Power, and (3) the difference (anomaly) of the 99th percentile of the last available year in the multiyear dataset compared against the long-term average as presented in Staneva et al. (2022). The statistics are based on the period 1950 to -18M and are obtained from yearly averages.

'''CONTEXT'''

In the last decade, the European seas have been hit by severe storms, causing serious damage to offshore infrastructure and coastal zones and drawing public attention to the importance of having reliable and comprehensive wave forecasts/hindcasts, especially during extreme events. In addition, human activities such as the offshore wind power industry, the oil industry, and coastal recreation regularly require climate and operational information on maximum wave height at a high resolution in space and time. Thus, there is a broad consensus that a high-quality wave climatology and predictions and a deep understanding of extreme waves caused by storms could substantially contribute to coastal risk management and protection measures, thereby preventing or minimising human and material damage and losses. In this respect, the Wave Power is a crucial quantity to plan and operate wave energy converters (WEC) and for coastal and offshore structures. For both reliable estimates of long-term Wave Power extremes are important to secure a high efficiency and to guarantee a robust and secure design, respectively.

'''KEY FINDINGS'''

The 99th percentile of wave power mean patterns are overall consistent with the respective significant wave height pattern. The maximum 99th percentile of wave power is observed in the southwestern Black Sea. Typical values of in the eastern basin are ~20 kW/m and in the western basin ~45 kW/m. The trend of the 99th percentile of the wave power is decreasing with an average value of 38 W/m/year and a maximum of 100 W/m/year, which is equivalent to a ~10-15% decrease over whole period with respect to the mean. The pattern of the anomaly of the 99th percentile of wave power in 2023 correlates well with that of the wind speed anomaly in 2023, revealing a clear positive wave power anomaly in vast regions of the Black Sea basin (8.5 kW/m on spatial average) with the maxima in the southern half of the domain indicating higher waves in 2023 compared to the average.

'''DOI (product):''' 
https://doi.org/10.48670/mds-00350
Global Ocean Colour Plankton MY L4 monthly 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''').

* Temporal resolutions: '''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-00283
North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations

'''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'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').



* Temporal resolutions: '''daily'''.

* Spatial resolutions: '''1 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-00284
Surface ocean carbon fields

'''Short description:'''

This product corresponds to a L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25° x 0.25° regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al.  2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.

'''DOI (product) :'''
https://doi.org/10.48670/moi-00047
High-resolution L3 Sea Surface Wind from NRT Satellite Measurements over the Arctic Sea

'''Short description:'''

For the Arctic Ocean - 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-00330
Arctic Ocean - High Resolution Sea Ice Information L4

'''Short description:''' 

Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid.

'''DOI (product) :'''   
https://doi.org/10.48670/mds-00344
Global Ocean Physics Reanalysis

'''Short description:'''
 
The GLORYS12V1 product is the CMEMS global ocean eddy-resolving (1/12° horizontal resolution, 50 vertical levels) reanalysis covering the altimetry (1993 onward).

It is based largely on the current real-time global forecasting CMEMS system. The model component is the NEMO platform driven at surface by ECMWF ERA-Interim then ERA5 reanalyses for recent years. Observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter data (Sea Level Anomaly), Satellite Sea Surface Temperature, Sea Ice Concentration and In situ Temperature and Salinity vertical Profiles are jointly assimilated. Moreover, a 3D-VAR scheme provides a correction for the slowly-evolving large-scale biases in temperature and salinity.

This product includes daily and monthly mean files for  temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom. The global ocean output files are displayed on a standard regular grid at 1/12° (approximatively 8 km) and on 50 standard levels.

'''DOI (product) :'''
https://doi.org/10.48670/moi-00021
Baltic Sea Wave Hindcast

'''Short description :'''

This Baltic Sea wave model multiyear product provides a hindcast for the wave conditions in the Baltic Sea since 1/1 1980 and up to 0.5-1 year compared to real time.
This hindcast product consists of a dataset with hourly data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the maximum waves, and also the Stokes drift. Another dataset contains hourly values for five air-sea flux parameters. Additionally a dataset with monthly climatology are provided for the significant wave height and the wave period. The product is based on the wave model WAM cycle 4.7, and surface forcing from ECMWF's ERA5 reanalysis products.  The product grid has a horizontal resolution of 1 nautical mile. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The product provides hourly instantaneously model data.

'''DOI (product) :'''
https://doi.org/10.48670/moi-00014