AndreaBozzo/ceres-open-data-index huggingface dataset:
Arctic Ocean Chlorophyll-a time series and trend from Observations Reprocessing

'''DEFINITION'''

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

'''CONTEXT'''

Phytoplankton – and chlorophyll concentration , which is  a measure of  phytoplankton concentration – respond rapidly to changes in environmental conditions. Chlorophyll concentration is highly seasonal in the Arctic Ocean region due to a strong dependency on light and nutrient availability, which in turn are driven by seasonal sunlight and sea-ice cover dynamics, as well as changes in mixed layer. In the past two decades, an increase in annual net primary production by Arctic Ocean phytoplankton has been observed and linked to sea-ice decline (Arrigo and van Dijken, 2015); in the same line Kahru et al. (2011) have showed that chlorophyll concentration peaks are appearing increasingly earlier in the year in parts of the Arctic. It is therefore of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales in the area, in order to be able to separate potential long-term climate signals from natural variability in the short term.

'''CMEMS KEY FINDINGS'''

While the overall trend average for the 1997-2021 period in the Arctic Sea is positive (0.86 ± 0.17 % per year), a continued plateau in the linear trend, initiated in 2013 is observed in the time series extension, with both the amplitude and the baseline of the cycle continuing to decrease during 2021 as reported for previous years (Sathyendranath et al., 2018). In particular, the annual average for the region in 2021 is 1.05 mg m-3 - a 30% reduction on 2020 values. There appears to be no appreciable changes in the timings or amplitude of the 2021 spring and autumn blooms. 

'''DOI (product):''' 
https://doi.org/10.48670/moi-00188
Baltic Sea Biogeochemistry Reanalysis

'''Short description:'''

This Baltic Sea Biogeochemical Reanalysis product provides a biogeochemical reanalysis for the whole Baltic Sea area, inclusive the Transition Area to the North Sea, from January 1993 and up to minus maximum 1 year relative to real time. The product is produced by using the biogeochemical model ERGOM one-way online-coupled with the ice-ocean model system Nemo. All variables are avalable as daily, monthly and annual means and include nitrate, phosphate, ammonium, dissolved oxygen, ph, chlorophyll-a, secchi depth, surface partial co2 pressure and net primary production. The data are available at the native model resulution (1 nautical mile horizontal resolution, and 56 vertical layers).

'''DOI (product) :'''

https://doi.org/10.48670/moi-00012
Floating Sargassum algae detection Index from satellite observations

'''Short description:'''

You can find here the Multi Observation Sargassum floating algae detection index product.
It contains 8 NRT datasets of information of the presence of sargassum algae, over 3 domains (Caribbean Seas, North Brazilian Shelve and Northern Tropical Atlantic) on regular grids from 20 meters to 1.1 kilometer resolution, of  10-minutes instantaneous, daily aggregated and weekly mean data on a daily basis.

'''DOI (product) :''' 
https://doi.org/10.48670/mds-00363
Global Ocean Heat Content trend map from Reanalysis & Multi-Observations Reprocessing

'''DEFINITION'''

Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). The regional OHC values are then averaged from 60°S-60°N aiming
i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change.
ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2).
Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017).

'''CONTEXT'''

Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019).

'''CMEMS KEY FINDINGS'''

Regional trends for the period 2005-2023 from the Copernicus Marine Service multi-ensemble approach show warming at rates ranging from the global mean average up to more than 8 W/m2 in some specific regions (e.g. northern hemisphere western boundary current regimes). There are specific regions where a negative trend is observed above noise at rates up to about -5 W/m2 such as in the subpolar North Atlantic. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020).

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

'''DOI (product):''' 
https://doi.org/10.48670/moi-00236
Baltic Sea Ice Extent from Observations Reprocessing

'''DEFINITION'''

Sea ice extent ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 2 (Samuelsen et al, 2018) and is derived from satellite observations product SEAICE_BAL_PHY_L4_MY_011_019. Sea ice extent is defined as the area covered by sea ice, that is the area of the ocean having more than 15% sea ice concentration. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. Daily sea ice extent values are computed from the daily sea ice concentration maps. All sea ice covering the Baltic Sea is included, except for lake ice. The annual course of the sea ice extent has been calculated as daily mean ice extent for each day-of-year over the period October 1992 – September 2014. Weekly smoothed time series of the sea ice extent have been calculated from daily values using a 7-day moving average filter.

CONTEXT

Sea ice coverage has a vital role in the annual course of physical and ecological conditions in the Baltic Sea. Moreover, it is an important parameter for safe winter navigation. The presence of sea ice cover sets special requirements for navigation, both for the construction of the ships and their behavior in ice, as in many cases, merchant ships need icebreaker assistance. Temporal trends of the sea ice extent could be a valuable indicator of the climate change signal in the Baltic Sea region. It has been estimated that a 1 °C increase in the average air temperature results in the retreat of ice-covered area in the Baltic Sea about 45,000 km2 (Granskog et al., 2006). Decrease in maximum ice extent may influence vertical stratification of the Baltic Sea (Hordoir and Meier, 2012) and affect the onset of the spring bloom (Eilola et al., 2013). In addition, statistical sea ice coverage information is crucial for planning of coastal and offshore construction. Therefore, the knowledge about ice conditions and their variability is required and monitored in Copernicus Marine Service.

CMEMS KEY FINDINGS

Sea ice in the Baltic Sea exhibits a strong seasonal cycle, typically forming in October and persisting until early June. The 2023/24 ice season was notable for an intermediate maximum extent, reaching approximately 105 000 km² on 12 February 2024. Initial ice formation was observed as early as 22 October 2023 in the northern Bay of Bothnia. Expansion accelerated through November and December, with rapid growth continuing into January. The extent remained above the climatological median for much of the freezing period.

Ice coverage peaked in mid-February and then gradually declined, with the Baltic Sea becoming ice-free by 4 June 2024, marking a longer-than-average season. The peak day for sea ice extent varies annually but typically falls between late February and early March (Singh et al., 2024). Over the past 30 years, the highest recorded extent was 260,000 km² during the 2010/11 winter. While there has been a declining trend in sea ice extent from 1993 to 2024, the linear trend remains statistically insignificant.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00200
Global Ocean Thermosteric Sea Level anomaly (0-2000m) time series and trend from Reanalysis & Multi-Observations Reprocessing

'''DEFINITION'''

The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). 

'''CONTEXT'''

The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). 

'''CMEMS KEY FINDINGS'''

Since the year 2005 the upper (0-2000m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.3±0.3 mm/year. 

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

'''DOI (product):'''
https://doi.org/10.48670/moi-00240
GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM REPROCESSED SATELLITE MEASUREMENTS

Multi-year Level-3 along-track significant wave height data derived from satellite altimetry missions including Topex, ERS-1, ERS-2, Jason-1, Jason-2, Jason-3, Envisat, Saral/AltiKa, Cryosat-2, Sentinel-3A, Sentinel-3B and Sentinel-6A. The dataset contains the altimeter L3 V4 data of the ESA Climate Change Initiative (CCI) Sea State project. Along-track measurements from the different missions are merged into single daily multi-mission files, resulting in a unified dataset that includes observations from all available satellite missions.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00176
Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD

'''Short description''':

You can find here the Multi Observation Global Ocean ARMOR3D L4 analysis and multi-year processing. This is 3D Temperature, Salinity, Heights, Geostrophic Currents and Mixed Layer Depth, available on a 1/8 degree regular grid and on 50 depth levels from the surface down to the bottom. These are  NRT and MY  datasets of monthly and daily mean together with climatological uncertainties. 

'''DOI (product) :''' 
https://doi.org/10.48670/moi-00052
Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast (Low Resolution)

'''Short description:'''

The NWSHELF_ANALYSISFORECAST_PHY_LR_004_001 is produced by a coupled hydrodynamic-biogeochemical model system with tides, implemented over the North East Atlantic and Shelf Seas at 7 km of horizontal resolution and 24 vertical levels.
The product is updated daily, providing 7-day forecast for temperature, salinity, currents, sea level and mixed layer depth.
Products are provided at quarter-hourly, hourly, daily de-tided (with Doodson filter), and monthly frequency.

'''DOI (product) :'''   
https://doi.org/10.48670/mds-00367
Black Sea Waves Analysis and Forecast

'''Short description''': 

The wave analysis and forecasts for the Black Sea are produced with the third generation spectral wave model WAM Cycle 6. The hindcast and ten days forecast are produced twice a day on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40° x 1/40°) with 24 directional and 30 frequency bins. The number of active wave model grid points is 81,531. The model takes into account depth refraction, wave breaking, and assimilation of satellite wave and wind data. The system provides a hindcast and ten days forecast with one-hourly output twice a day. The atmospheric forcing is taken from ECMWF analyses and forecast data. Additionally, WAM is forced by surface currents and sea surface height from BLKSEA_ANALYSISFORECAST_PHY_007_001. Monthly statistics are provided operationally on the Product Quality Dashboard following the CMEMS metrics definitions.

'''Citation''': 
Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Analysis and Forecast (CMEMS BS-Waves, EAS5 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS).

'''DOI (Product)''': 
https://doi.org/10.48670/mds-00360
Baltic Sea Cod Reproductive Volume from Reanalysis

'''DEFINITION'''

The cod reproductive volume ocean monitoring indicator was introduced in Copernicus Marine Service Ocean State Report, Issue 3 (Raudsepp et al, 2019) and is derived from regional reanalysis modelling results for the Baltic Sea BALTICSEA_MULTIYEAR_PHY_003_011 and BALTICSEA_MULTIYEAR_BGC_003_012. The volume has been calculated taking into account the three most important influencing abiotic factors of cod reproductive success: salinity > 11 g/kg, oxygen concentration > 2 ml/l and water temperature over 1.5°C (MacKenzie et al., 1996; Heikinheimo, 2008; Plikshs et al., 2015). The daily volumes are calculated as the volumes of the water with salinity > 11 g/kg, oxygen content > 2 ml/l and water temperature over 1.5°C in the Baltic Sea International Council for the Exploration of the Sea subdivisions of 25-28 (ICES, 2019).

CONTEXT

Cod (Gadus morhua) is a characteristic fish species in the Baltic Sea with major economic importance. Spawning stock biomasses of the Baltic cod have gone through a steep decline in the late 1980s (Bryhn et al., 2022). Water salinity and oxygen concentration affect cod stock through the survival of eggs (Westin and Nissling, 1991; Wieland et al., 1994). Major Baltic Inflows provide a suitable environment for cod reproduction by bringing saline oxygenated water to the deep basins of the Baltic Sea (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland). Increased cod reproductive volume has a positive effect on cod reproduction success, which should reflect an increase of stock size indicator 4–5 years after the Major Baltic Inflow (Raudsepp et al., 2019). Eastern Baltic cod reaches maturity around age 2–3, depending on the population density and environmental conditions. Low oxygen and salinity cause stress, which negatively affects cod recruitment, whereas sufficient conditions may bring about male cod maturation even at the age of 1.5 years (Cardinale and Modin, 1999; Karasiova et al., 2008). There are a number of environmental factors affecting cod populations (Bryhn et al., 2022). 

CMEMS KEY FINDINGS

Typically, the cod reproductive volume in the Baltic Sea oscillates between 200 and 400 km³. There have been two distinct periods of significant increase, with maximum values reaching over 1200 km³, corresponding to the aftermath of Major Baltic Inflows (BALTIC_OMI_WMHE_mbi_bottom_salinity_arkona_bornholm and BALTIC_OMI_WMHE_mbi_sto2tz_gotland) from 2003 to 2004 and from 2016 to 2017. Following a decline to the baseline of 200 km³ in 2018, there was a rise to 800 km³ in 2019. The cod reproductive volume hit a second peak of 1000 km³ in 2022, stabilized at 600 km³ and increased to 800 km3 by the end of 2024. However, Bryhn et al. (2022) report no observed increase in the spawning stock biomass of the eastern Baltic Sea cod.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00196
Global Ocean Thermosteric Sea Level anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing

'''DEFINITION'''

The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). 

'''CONTEXT'''

The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). 

'''CMEMS KEY FINDINGS'''

Since the year 2005 the upper (0-700m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.0 ± 0.2 mm/year. 

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

'''DOI (product):'''
https://doi.org/10.48670/moi-00239
European North West Shelf Sea Surface Temperature trend map from Observations Reprocessing

'''DEFINITION'''

The omi_climate_sst_northwestshelf_trend product includes the Sea Surface Temperature (SST) trend for the European North West Shelf Seas 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-NORTHWESTSHELF_v3.pdf), which provided the SSTs used to compute the SST trend over the European North West Shelf Seas. 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., 2019). 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 '''

Over the period 1982-2024, the European North West Shelf Seas mean Sea Surface Temperature (SST) increased at a rate of 0.016 ± 0.001 °C/Year.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00276
Black Sea Physics Analysis and Forecast

'''Short description''':  

The BLKSEA_ANALYSISFORECAST_PHY_007_001 is produced with a hydrodynamic model implemented over the whole Black Sea basin, including the Azov Sea, the Bosporus Strait and a portion of the Marmara Sea for the optimal interface with the Mediterranean Sea through lateral open boundary conditions. The model horizontal grid resolution is 1/40° (ca. 2.5 km) and it has 121 unevenly spaced vertical levels. The product provides analysis and forecast for 3D potential temperature, salinity, horizontal and vertical currents; the 2D variables sea surface height, bottom potential temperature, mixed layer thickness, sea-ice extent and sea-ice thickness.

'''DOI (Product)''': 
https://doi.org/10.48670/mds-00355
Arctic Ocean Colour Plankton, Reflectance, Transparency and Optics MY L3 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:  OCEANCOLOUR_GLO_BGC_L3_MY_009_107 for the '''"multi"''' products and S3A & S3B only for the '''"OLCI"''' products.
* Variables: Chlorophyll-a ('''CHL'''), Diffuse Attenuation ('''KD490''') and  Reflectance ('''RRS''').

* Temporal resolutions: '''daily'''.
* Spatial resolutions: '''4 km''' (multi) or '''300 m''' (OLCI).
* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00292