AndreaBozzo/ceres-open-data-index huggingface dataset:
Arctic Monthly Mean Sea Ice Extent from Observations Reprocessing

'''DEFINITION'''

Sea Ice Extent (SIE) is the area covered by sufficient sea ice, that is the area of ocean having more than 15% Sea Ice Concentration (SIC). SIC is the fractional area of ocean surface that is covered with sea ice. SIC is computed from Passive Microwave satellite observations since the 1970s. SIE is often reported with units of millions square kilometers. SIE includes all sea ice, and does not include lake and river ice. The change in sea ice extent (trend) is expressed in millions of km squared per decade. In addition, trends are expressed relative to the 1979-2024 period in % per decade. These trends are calculated (i) for the annual mean values; (ii) for the winter maximum cover (March in the Northern Hemisphere); (iii) for the summer minimum extent (September in the Northern Hemisphere). The annual mean trend is reported on the key figure. The March and September trends are reported in the text below.  See also section 1.7 in Samuelsen et al. (2016) for an introduction to this Ocean Monitoring Indicator (OMI).


'''CONTEXT'''

Sea ice is frozen seawater that floats at 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 sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and by how much the sea-ice cover is changing is essential for monitoring the health of the Earth (Meredith et al. 2019; Fox-Kemper et al. 2021).


'''CMEMS KEY FINDINGS'''

Since 1979, the Northern Hemisphere sea ice extent has decreased in all months. Loss of sea ice extent during summer exceeds the loss observed during winter periods. Over the period 1979-2024, the annual rate amounts to -0.49 +/- +0.02 millions km squared by decade (-4.27% per decade). Winter (March) sea ice extent trend amounts to -0.37 +/- +0.03 millions km squared by decade (-2.45% per decade). Summer (September) sea ice extent trend amounts to -0.79 +/- +0.06 millions km squared by decade (-12.57% per decade). These values are in agreement with those assessed in the IPCC SROCC (Meredith et al. 2019, with estimates up until year 2018). While 2024 started with the 20th lowest mean January sea ice extent, it ended with the lowest mean December sea ice extent, since 1979. September 2024 was the 5th lowest mean September sea ice extent since 1979. The record low mean September was in 2020.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00191
North Atlantic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 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-00286
Nordic Seas Volume Transports from Reanalysis

'''DEFINITION'''

Net (positive minus negative) volume transport of Atlantic Water through the sections (see Figure 1):  Faroe Shetland Channel (Water mass criteria, T > 5 °C); Barents Sea Opening (T > 3 °C) and the Fram Strait (T > 2 °C). Net volume transport of Overflow Waters (σθ >27.8 kg/m3) exiting from the Nordic Seas to the North Atlantic via the Denmark Strait and Faroe Shetland Channel. For further details, see Ch. 3.2 in von Schuckmann et al. (2018).

'''CONTEXT'''

The poleward flow of relatively warm and saline Atlantic Water through the Nordic Seas to the Arctic Basin, balanced by the overflow waters exiting the Nordic Seas, governs the exchanges between the North Atlantic and the Arctic as well as the distribution of oceanic heat within the Arctic (e.g., Mauritzen et al., 2011; Rudels, 2012). Atlantic Water transported poleward has been found to significantly influence the sea-ice cover in the Barents Sea (Sandø et al., 2010; Årthun et al., 2012; Onarheim et al., 2015) and near Svalbard (Piechura and Walczowski, 2009). Furthermore, Atlantic Water flow through the eastern Nordic seas and its associated heat loss and densification are important factors for the formation of overflow waters in the region (Mauritzen, 1996; Eldevik et al., 2009). These overflow waters together with those generated in the Arctic, exit the Greenland Scotland Ridge, which further contribute to the North Atlantic Deep Water (Dickson and Brown, 1994) and thus play an important role in the Atlantic Meridional Overturning Circulation (Eldevik et al., 2009; Ch. 2.3 in von Schuckmann et al., 2016). In addition to the transport of heat, the Atlantic Water also transports nutrients and zooplankton (e.g., Sundby, 2000), and it carries large amounts of ichthyoplankton of commercially important species, such as Arcto-Norwegian cod (Gadus morhua) and Norwegian spring-spawning herring (Clupea harengus) along the Norwegian coast. The Atlantic Water flow thus plays an integral part in defining both the physical and biological border between the boreal and Arctic realm. Variability of Atlantic Water flow to the Barents Sea has been found to move the position of the ice edge (Onarheim et al., 2015) as well as habitats of various species in the Barents Sea ecosystem (Fossheim et al., 2015).

'''CMEMS KEY FINDINGS'''

The flow of Atlantic Water through the Færøy-Shetland Channel amounts to 2.7 Sv (Berx et al., 2013). The corresponding model-based estimate was 2.3 Sv for the full period 1991-2024. 
In the Barents Sea Opening, the model indicates a long-term average net Atlantic Water inflow of 2.2 Sv, as compared with the long-term estimate from observations of 2 Sv (Smedsrud et al., 2013).
In the Fram Strait, the model data indicates a positive trend in the Atlantic Water transport to the Arctic. This trend may be explained by increased temperature in the West Spitsbergen Current during the period 2005-2010 (e.g., Walczowski et al., 2012), which caused a larger fraction of the water mass to be characterized as Atlantic Water (T > 2 °C).

'''DOI (product):''' 
https://doi.org/10.48670/moi-00189
Black Sea, Bio-Geo-Chemical, L4, monthly means, daily gapfree and climatology Satellite Observations (1997-ongoing)

'''Short description:'''

For the '''Black 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 (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); moreover, daily climatology for chlorophyll concentration is provided.
* '''''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""''', '''""pp""''' and climatology data)

'''Spatial resolution''': 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_MY""'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00304
Global Ocean Physics Analysis and Forecast

'''Short description'''

The Operational Mercator global ocean analysis and forecast system at 1/12 degree is providing 10 days of 3D global ocean forecasts updated daily. The time series is aggregated in time in order to reach a two full year’s time series sliding window.

This product includes daily and monthly mean files of temperature, salinity, currents, sea level, mixed layer depth and ice parameters from the top to the bottom over the global ocean. It also includes hourly mean surface fields for sea level height, temperature and currents. The global ocean output files are displayed with a 1/12 degree horizontal resolution with regular longitude/latitude equirectangular projection.

50 vertical levels are ranging from 0 to 5500 meters.

This product also delivers a special dataset for surface current which also includes wave and tidal drift called SMOC (Surface merged Ocean Current).

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00016
Arctic Ocean Physics Reanalysis

'''Short description:'''

The current version of the TOPAZ system - TOPAZ4b -  is nearly identical to the real-time forecast system run at MET Norway. It uses a recent version of the Hybrid Coordinate Ocean Model (HYCOM) developed at University of Miami (Bleck 2002). HYCOM is coupled to a sea ice model; ice thermodynamics are described in Drange and Simonsen (1996) and the elastic-viscous-plastic rheology in Hunke and Dukowicz (1997). The model's native grid covers the Arctic and North Atlantic Oceans,  has fairly homogeneous horizontal spacing (between 11 and 16 km). 50 hybrid layers are used in the vertical (z-isopycnal). TOPAZ4b uses the Deterministic version of the Ensemble Kalman filter (DEnKF; Sakov and Oke 2008) to assimilate remotely sensed as well as temperature and salinity profiles. The output is interpolated onto standard grids and depths for convenience. Daily values are provided at all depths and surfaces momentum and heat fluxes are provided as well. Data assimilation, including the 100-member ensemble production, is performed weekly. Sea ice thickness prior to 2010 has been post-processed by an AI-based procedure. 

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00007
Atlantic- European North West Shelf- Ocean Physics Reanalysis

'''Short  Description:'''

The ocean physics reanalysis for the North-West European Shelf is produced using an ocean assimilation model, with tides, at 7 km horizontal resolution.  
The ocean model is NEMO (Nucleus for European Modelling of the Ocean), using the 3DVar NEMOVAR system to assimilate observations. These are surface temperature and vertical profiles of temperature and salinity. The model is forced by lateral boundary conditions from the GloSea5, one of the multi-models used by [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_REANALYSIS_PHY_001_026 GLOBAL_REANALYSIS_PHY_001_026] and at the Baltic boundary by the [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_PHY_003_011 BALTICSEA_REANALYSIS_PHY_003_011]. The atmospheric forcing is given by the ECMWF ERA5 atmospheric reanalysis. The river discharge is from a daily climatology. 

Further details of the model, including the product validation are provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf CMEMS-NWS-QUID-004-009]. 

Products are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are temperature, salinity, horizontal currents, sea level, mixed layer depth, and bottom temperature. Temperature, salinity and currents, 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 provinding 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 further details.

'''Associated products:'''

This model is coupled with a biogeochemistry model (ERSEM) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011]. An analysis-forecast product is available from [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 NWSHELF_ANALYSISFORECAST_PHY_LR_004_011].
The product is updated biannually provinding six-month extension of the time series.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00059
Gulf Stream destabilization point

'''DEFINITION'''

The destabilization point of the Gulf Stream marks the longitude where the current transitions  from a boundary-following current to an open-ocean jet. It is identified using the 25 cm SSH contour, from detrended Absolute Dynamic Topography (ADT), a standard method validated in multiple studies (Lillibridge & Mariano, 2013; Rossby et al., 2014; Andres, 2016; Chi et al., 2021; Guo et al., 2023). 

Monthly Gulf Stream paths from 1993 to  present are divided into 0.5° longitude bins, and the northernmost latitude of the SSH contour is used to compute the latitudinal variance in each bin. The destabilization point is defined as the first longitude where this variance reaches 0.42°², which represents half the maximum variance observed across the full time series ( Sánchez-Román et al., 2024). 

A 95% confidence interval is computed from monthly estimates, and a 5-year running mean is applied to smooth high-frequency fluctuations. Longitudinal shifts of the destabilization point can exceed 1400 km, influenced by climate modes such as the North Atlantic Oscillation (NAO) (Sánchez-Román et al., 2024). 

 

'''CONTEXT'''

The Gulf Stream is a major western boundary current which forms the northward flowing path of the Atlantic Meridional Overturning Circulation (AMOC). It carries near-surface warm waters from the Gulf of Mexico to the subpolar North Atlantic, playing an important role in its climate variability and change. Interannual lateral displacements in the Gulf Stream’s position impact associated water transport to subpolar regions, altering the global climate system (Guo et al., 2023).  

Tracking the destabilization point provides an early indicator of structural changes in the North Atlantic circulation system. Its position directly reflects the length of the stable jet segment and the onset region of meanders. The observed shifts indicate modifications in the jet’s structure, its stability, and its interaction with oceanic variability. Its evolution reveals a significant westward migration followed by an eastward shift over the past three decades. 


'''KEY FINDINGS'''

The results reveal  a low-frequency westward and southward displacement of the Gulf Stream destabilization point between 1995 and 2012, followed by a previously unreported reversed migration starting in 2013. These changes affect the intensity of mesoscale variability and the transport of waters toward the subpolar North Atlantic. They appear to be correlated with the dynamics of the North Atlantic Oscillation, whose successive phases modulate the position of the current. 


'''DOI (product):'''
https://doi.org/10.48670/mds-00377
Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics L3 NRT daily observations

'''Short description:'''

For the '''Arctic''' Ocean '''Satellite Observations''', Italian National Research Council (CNR – Rome, Italy) is providing '''Bio-Geo_Chemical (BGC)''' products.
* Upstreams: OLCI-S3A & OLCI-S3B for the '''""olci""''' products.
* Variables: Chlorophyll-a ('''CHL'''), Suspended Matter ('''SPM'''), Diffuse Attenuation ('''KD490'''), Detrital and Dissolved Material Absorption Coef. ('''ADG443''), Phytoplankton Absorption Coef. ('''APH443''), Total Absorption Coef. ('''ATOT443'') and  Reflectance ('''RRS''').

* Temporal resolutions: '''daily''', '''monthly'''.
* Spatial resolutions:  '''300 meters''' (olci).
* Recent products are organized in datasets called Near Real Time ('''NRT''').

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00290
Antarctic Sea Ice Extent from Reanalysis

'''DEFINITION'''

Estimates of Antarctic 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 Southern Hemisphere (excluding ice lakes) and from 1993 up to real time aiming to:
i) obtain the Antarctic sea ice extent as expressed in millions of km squared (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/gain 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)). For the Southern Hemisphere, these trends are calculated from the annual mean values.
The Antarctic 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 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'''

With quasi regular highs and lows, the annual Antarctic sea ice extent shows large variability until several monthly record high in 2014 and record lows in 2017, 2018 and 2023. Since the year 1993, the Southern Hemisphere annual sea ice extent regularly alternates positive and negative trend. The period 1993-2023 have seen a slight decrease at a rate of -0.28*106km2 per decade. This represents a loss amount of -2.4% per decade of Southern Hemisphere sea ice extent during this period; with however large uncertainties. The last quarter of the year 2016 and years 2017 and 2018 experienced unusual losses of ice. The year 2023 is an exceptional year and its average has a strong impact on the whole time series.

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

'''DOI (product):''' 
https://doi.org/10.48670/moi-00186
Baltic Sea Wave Analysis and Forecast

'''Short description:'''

This Baltic Sea wave model product provides forecasts for the wave conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Data are provided with hourly instantaneous data for significant wave height, wave period and wave direction for total sea, wind sea and swell, the Stokes drift, and two paramters for the maximum wave. The product is based on the wave model WAM cycle 4.7. The wave model is forced with surface currents, sea level anomaly and ice information from the Baltic Sea ocean forecast product (BALTICSEA_ANALYSISFORECAST_PHY_003_006). 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).

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00011
Mediterranean Ocean Salt Content (0-300m)

'''DEFINITION'''

Ocean salt content (OSC) is defined and represented here as the volume average of the integral of salinity in the Mediterranean Sea from z1 = 0 m to z2 = 300 m depth:
¯S=1/V ∫V S dV
Time series of annual mean values area averaged ocean salt content are provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and are evaluated in the upper 300m excluding the shelf areas close to the coast with a depth less than 300 m. The total estimated volume is approximately 5.7e+5 km3.

'''CONTEXT'''

The freshwater input from the land (river runoff) and atmosphere (precipitation) and inflow from the Black Sea and the Atlantic Ocean are balanced by the evaporation in the Mediterranean Sea. Evolution of the salt content may have an impact in the ocean circulation and dynamics which possibly will have implication on the entire Earth climate system. Thus monitoring changes in the salinity content is essential considering its link to changes in: the hydrological cycle, the water masses formation, the regional halosteric sea level and salt/freshwater transport, as well as for their impact on marine biodiversity.
The OMI_CLIMATE_OSC_MEDSEA_volume_mean is based on the “multi-product” approach introduced in the seventh issue of the Ocean State Report (contribution by Aydogdu et al., 2023). Note that the estimates in Aydogdu et al. (2023) are provided monthly while here we evaluate the results per year.
Six global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are:

•	The Mediterranean Sea Reanalysis at 1/24°horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020)

•	Four global reanalyses at 1/4°horizontal resolution (GLOBAL_REANALYSIS_PHY_001_031, GLORYS, C-GLORS, ORAS5, FOAM, DOI: https://doi.org/10.48670/moi-00024, Desportes et al., 2022)

•	Two observation-based products: CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, DOI:  https://doi.org/10.17882/46219, Szekely et al., 2022) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012, DOI: https://doi.org/10.48670/moi-00052, Grenier et al., 2021). 
Details on the products are delivered in the PUM and QUID of this OMI.

'''CMEMS KEY FINDINGS'''

The Mediterranean Sea salt content shows a positive trend in the upper 300 m with a continuous increase over the period 1993-2021 at rate of 7.0*10-3 ±3.1*10-4 psu yr-1. 
The overall ensemble mean of different products is 38.57 psu. During the early 1990s in the entire Mediterranean Sea there is a large spread in salinity with the observational based datasets showing a higher salinity, while the reanalysis products present relatively lower salinity. The maximum spread between the period 1993–2021 occurs in the 1990s with a value of 0.12 psu, and it decreases to as low as 0.02 psu by the end of the 2010s.

'''DOI (product):''' 
https://doi.org/10.48670/mds-00325