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

'''DEFINITION'''

The blksea_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Black Sea over the period 1982-2024 (°C/year). This OMI is derived from the CMEMS Reprocessed Black Sea L4 SST satellite product (SST_BS_SST_L4_REP_OBSERVATIONS_010_022, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-BLKSEA-SST.pdf), which provided the SSTs used to compute the SST trend over the Black Sea. This reprocessed product consists of daily (nighttime) optimally interpolated 0.05° grid resolution SST maps over the Black Sea built from the ESA Climate Change Initiative (CCI) (Embury et al., 2024) and Copernicus Climate Change Service (C3S) initiatives, including also an adjusted version of the AVHRR Pathfinder dataset version 5.3 (Saha et al., 2018) to increase the input observation coverage. 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). In the last decades, since the availability of satellite data (beginning of 1980s), the Black Sea has experienced a warming trend in SST (see e.g. Buongiorno Nardelli et al., 2010; Mulet et al., 2018).

'''KEY FINDINGS'''

Over the past four decades (1982-2024), Sea Surface Temperature (SST) in the Black Sea warmed at a rate of 0.056 ± 0.003 °C per year, corresponding to a mean surface temperature warming of about 2.4 °C. The spatial pattern of the Black Sea SST trend reveals a general warming tendency, ranging from 0.05 °C/year to 0.06 °C/year. The spatial pattern of SST trend is rather homogeneous over the whole basin. Highest values characterize the eastern basin, where the trend reaches the extreme value, while lower values are found close to the western coasts, in correspondence of main rivers inflow. The Black Sea SST trend continues to show the highest intensity among all the other European Seas.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00218
Baltic Sea sea surface temperature extreme variability mean and anomaly (observations)

'''DEFINITION'''

The OMI_EXTREME_SST_BALTIC_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, 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 Baltic Sea has showed in the last two decades a warming trend across the whole basin with more frequent and severe heat waves (IPCC, 2022). This trend is significantly higher when considering only the summer season, which would affect the high extremes (e.g. Høyer and Karagali, 2016).

'''COPERNICUS MARINE SERVICE KEY FINDINGS'''

The mean 99th percentiles showed in the area present values around 20.5ºC in the SouthEast of the Coast of Sweeden with 1.4-1.7ºC of standard deviation (std), 19.6-21.1ºC in the North Coast of Germany with 1.5-3.6ºC of std and 19.6-21.8ºC  in the Estonian Coast with 1.2-5.3ºC of std.
Results for this year show negative anomalies in the Coast of Sweeden (-1.1/-0.4ºC) and either positive or negative anomalies in the North Coast of Germany (-1.2/+1.0) and in the Estonian Coast (-0.7/+0.6ºC) within the standard deviation margin in the respective areas.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00204
Global Ocean- In-Situ Near-Real-Time Observations

'''Short description:'''
Global Ocean   - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC  within 24-48 hours from acquisition in average. Data are collected mainly through global networks (Argo, OceanSites, GOSUD, EGO) and through the GTS

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00036
Global Observed Ocean Physics 3D Quasi-Geostrophic Currents (OMEGA3D)

'''Short description''':

The data are provided weekly over a regular grid at 1/4° horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 ) and ERA5 surface fluxes. 

'''DOI (product) :''' 
https://doi.org/10.48670/moi-00053
Global Ocean Heat Content (0-2000m) time series and trend 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). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. 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'''

Since the year 2005, the upper (0-2000m) near-global (60°S-60°N) ocean warms at a rate of 0.9 ± 0.1 W/m2.

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

'''DOI (product):''' 
https://doi.org/10.48670/moi-00235
Global Ocean Thermosteric Sea Level trend map 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). 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'''

Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades.

'''CMEMS KEY FINDINGS'''

Significant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2023 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -5 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. 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-00241
Mediterranean Ocean Heat Content Anomaly (0-700m) time series and trend from Reanalysis & Multi-Observations Reprocessing

'''DEFINITION'''

Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth:
OHC=∫_(z_1)^(z_2)ρ_0  c_p (T_yr-T_clim )dz 								[1]
with a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 °C-1 (e.g. von Schuckmann et al., 2009).
Time series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and is evaluated for topography deeper than 300m.

'''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 oceans shape our perspectives for the future.
The quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean’s experience (Masina et al., 2017). 
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 degree 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 degree horizontal resolution (GLOBAL_MULTIYEAR_PHY_ENS_001_031): 
GLORYS, C-GLORS, ORAS5, FOAM

•	Two observation based products: 
CORA (INSITU_GLO_PHY_TS_OA_MY_013_052) and 
ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). 
Details on the products are delivered in the PUM and QUID of this OMI. 

'''CMEMS KEY FINDINGS'''

The ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2022 at rate of 1.38±0.08 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018).

'''DOI (product):''' 
https://doi.org/10.48670/moi-00261
High-resolution L3 Sea Surface Wind from MY 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 Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products.

'''DOI (product) :'''  
https://doi.org/10.48670/mds-00338
Global Ocean Heat Content (0-300m) 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). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. 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'''

Since the year 2005, the near-surface (0-300m) near-global (60°S-60°N) ocean warms at a rate of 0.4 ± 0.1 W/m2.

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

'''DOI (product):''' 
https://doi.org/10.48670/moi-00233
Baltic Sea Ice Volume from Observations Reprocessing

'''DEFINITION'''

Sea ice volume 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. The sea ice volume is a product of sea ice concentration and sea ice thickness integrated over respective area. Sea ice concentration is the fractional coverage of an ocean area covered with sea ice. The Baltic Sea area having more than 15% of sea ice concentration is included into the sea ice volume analysis. Daily sea ice volume values are computed from the daily sea ice concentration and sea ice thickness maps. The annual course of the sea ice volume has been calculated as daily mean ice volume for each day-of-year over the period October 1992 – September 2014. Weekly smoothed time series of the sea ice volume 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. Knowledge of the sea ice volume facilitates planning of icebreaking activity and operation of the icebreakers (Valdez Banda et al., 2015; Boström and Österman, 2017). A long-term monitoring of ice parameters is required for design and installation of offshore constructions in seasonally ice covered seas (Heinonen and Rissanen, 2017). A reduction of the sea ice volume in the Baltic Sea has a critical impact on the population of ringed seals (Harkonen et al., 2008). Ringed seals need stable ice conditions for about two months for breeding and moulting (Sundqvist et al., 2012). The sea ice is a habitat for diverse biological assemblages (Enberg et al., 2018).

CMEMS KEY FINDINGS

In the Baltic Sea, the ice season typically begins in October and can last until June. The maximum sea ice volume usually occurs in March, but during the 2023/24 season, it was observed in February. That season recorded lower than average maximum ice volume of approximately 17 km³, well below the long-term climatological average. Between 1993 and 2024, the annual maximum ice volume ranged from a minimum of 3 km³ in 2020 to a peak of 51 km³ in 1996.

A statistically significant declining trend of −0.63 km³ per year (p = 0.02) is evident in the time series of maximum sea ice volume, highlighting a persistent reduction over the past three decades. Recent observations also indicate a concurrent decrease in both sea ice fraction and ice thickness, particularly in the Bothnian Bay, as reported by Singh et al. (2024), further reinforcing the regional impacts of climate change on Baltic Sea ice dynamics.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00201