Accessible: public

Title: CMEMS - CORA: Coriolis Ocean database for ReAnalysis

Summary:
The COriolis Ocean Dataset for Reanalysis (hereafter "CORA") product is a global dataset of in situ temperature and salinity measurements. The CORA observations comes from many different sources collected by Coriolis data centre in collaboration with the In Situ Thematic Centre of the Copernicus Marine Service (CMEMS INSTAC). The observation integrated in the CORA product have been acquired both by autonomous platforms (Argo profilers, fixed moorings , gliders , drifters, sea mammals) , research or opportunity vessels (CTDs, XBTs, ferrybox).
From the near real time CMEMS In Situ Thematic Centre product validated on a daily and weekly basis for forecasting purposes, a scientifically validated product is created. It s a "reference product" updated on a yearly basis since 2007. This product has been controlled using an objective analysis (statistical tests) method and a visual quality control (QC). This QC procedure has been developed with the main objective to improve the quality of the dataset to the level required by the climate application and the physical ocean re-analysis activities. It provides T and S weekly gridded fields and individual profiles both on their original level with QC flags and interpolated level. The measured parameters, depending on the data source, are : temperature, salinity. The reference level of measurements is immersion (in meters) or pressure (in decibars).
CORA contains historical profiles extracted from the EN.4 global T&S dataset, World Ocean Atlas, SeaDataNet, ICES and other data aggregators.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
PSAL (practical salinity, PSU)
PSAL_ERR (Practical salinity Error, PSS-78)
PSAL_PCTVAR (Error on salinity  (% variance), percent)
TEMP (sea temperature, degree_Celsius)
TEMP_ERR (Temperature Error, degree_Celsius)
TEMP_PCTVAR (Error on temperature  (% variance), percent)

Institution: Copernicus Marine Service (CMEMS)


Title: Daily Southern Ocean Sea Level Anomaly And Geostrophic Currents from multimission altimetry, 2013-2019

Summary:
Sea Level Anomaly measured by Altimetry and derived variables

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][longitude][latitude]):
sla (Sea Level Anomaly, m)
formal_error (Optimal Interpolation Formal Error, m)
U (Zonal Geostrophic Current Anomaly, m/s)
V (Meridional Geostrophic Current Anomaly, m/s)

Institution: CLS,CNES


Summary:
Monthly climatology fields for product GLOBAL_REANALYSIS_PHY_001_030

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][depth][latitude][longitude]):
thetao (Temperature, degree_C)
so (Salinity, PSU)
uo (Eastward velocity, m s-1)
vo (Northward velocity, m s-1)

Institution: Mercator Ocean


Title: NOAA - GLobal Ocean Data Analysis Project (GLODAP)

Summary:
1 X 1 global mapped field of anthropogenic carbon content from the GLODAPv2 data product. Mapping is performed using the DIVA software (Troupin et al., 2012). Error fields are calculated using the clever poor mans error calculation method in DIVA (Beckers et al., 2014). The error fields represent the mapping error only, and does not include measurement or calculation uncertainties in the input data.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [depth][latitude][longitude]):
Cant (moles of anthropogenic carbon content per unit mass in seawater, micro-mol kg-1)
Cant_error (anthropogenic carbon content error, micro-mol kg-1)
Cant_Input_mean (bin averaged input data, micro-mol kg-1)
Cant_Input_std (standard deviation of bin averaged input data, micro-mol kg-1)
Cant_Input_N (number of data in bins)
Cant_relerr (relative error)
NO3 (moles of nitrate per unit mass in seawater, micro-mol kg-1)
NO3_error (nitrate error, micro-mol kg-1)
NO3_Input_mean (bin averaged input data, micro-mol kg-1)
NO3_Input_std (standard deviation of bin averaged input data, micro-mol kg-1)
NO3_Input_N (number of data in bins)
NO3_relerr (relative error)
OmegaA (aragonite saturation state calculated at in situ temperature and pressure)
OmegaA_error (OmegaAr error)
OmegaA_Input_mean (bin averaged input data, micro-mol kg-1)
OmegaA_Input_std (standard deviation of bin averaged input data, micro-mol kg-1)
OmegaA_Input_N (number of data in bins)
OmegaA_relerr (relative error)
OmegaC (calcite saturation state calculated at in situ temperature and pressure)
OmegaC_error (OmegaCa error)
OmegaC_Input_mean (bin averaged input data, micro-mol kg-1)
OmegaC_Input_std (standard deviation of bin averaged input data, micro-mol kg-1)
OmegaC_Input_N (number of data in bins)
... (61 more variables)

Institution: Global Ocean Data Analysis Project (GLODAP)


Title: NOAA - Optimum Interpolation (OI) Sea Surface Temperature (SST) V2 High Resolution Dataset

Summary:
The NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases. OISST belongs to a family of products that are sometimes referred to as "Reynolds SST" for Richard W. Reynolds, a NOAA scientist who worked to improve the accuracy of the SST analyses by optimizing the advantages of in situ (ship and buoy) and satellite data. Older Reynolds SST products have been retired, except for the 1° weekly OISST. The dataset was developed using a methodology that includes bias adjustment of satellite and ship observations (referenced to buoys) to compensate for platform differences and sensor biases. This proved critical during the Mt. Pinatubo eruption in 1991, when the widespread presence of volcanic aerosols resulted in infrared satellite temperatures that were much cooler than actual ocean temperatures (Reynolds 1993).

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sst (Monthly Mean of Sea Surface Temperature, degree_C)

Institution: National Oceanic and Atmospheric Administration (NOAA)


Title: NOAA - Surface Ocean CO2 Atlas Database Version 2024 (SOCAT v2024)

Summary:
Global Ocean - Gridded In Situ reprocessed carbon observations - SOCATv2024. Surface Ocean Carbon Atlas (SOCAT) Gridded (binned) SOCAT observations, with a spatial grid of 1x1 degree and yearly in time. The gridded fields are computed from the monthly 1-degree gridded data, which uses only SOCAT datasets with Quality Control (QC) flags of A through D and SOCAT data points flagged with World Ocean Circulation Experiment (WOCE) flag values of 2. This yearly data is computed using data from the start to the end of each year as described in the summary attribute of each variable.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
count_ncruise (Number of cruises, count)
fco2_count_nobs (Number of fco2 obs, count)
fco2_ave_weighted (fCO2 mean - per cruise weighted, uatm)
fco2_ave_unwtd (fCO2 mean - unweighted all obs, uatm)
fco2_min_unwtd (fCO2 min, uatm)
fco2_max_unwtd (fCO2 max, uatm)
fco2_std_weighted (fCO2 std dev - per cruise weighted, uatm)
fco2_std_unwtd (fCO2 std dev - unweighted all obs, uatm)
sst_count_nobs (Number of valid sst obs, count)
sst_ave_weighted (SST mean - per cruise weighted, degree_C)
sst_ave_unwtd (SST mean - unweighted all obs, degree_C)
sst_min_unwtd (SST min, degree_C)
sst_max_unwtd (SST max, degree_C)
sst_std_weighted (SST std dev - per cruise weighted, degree_C)
sst_std_unwtd (SST std dev - unweighted all obs, degree_C)
salinity_count_nobs (Number of valid salinity obs, count)
salinity_ave_weighted (Salinity mean - per cruise weighted, PSU)
salinity_ave_unwtd (Salinity mean - unweighted all obs, PSU)
salinity_min_unwtd (Salinity min, PSU)
salinity_max_unwtd (Salinity max, PSU)
salinity_std_weighted (Salinity std dev - per cruise weighted, PSU)
salinity_std_unwtd (Salinity std dev - unweighted all obs, PSU)
lat_offset_unwtd (Latitude average offset from cell center, Deg N)
lon_offset_unwtd (Longitude average offset from cell center, Deg E)

Institution: PMEL, NOAA


Title: OCEAN ICE - European circumpolar sea ice production fluxes

Summary:
This dataset on sea ice production (SIP) in Antarctic coastal polynyas offers crucial insights into these regions' roles in sea ice and dense water formation. Using Earth Observation data and atmospheric reanalysis, the dataset employs a heat budget method to estimate SIP. It incorporates sea ice concentration (SIC) from passive microwave sensors and near-surface wind speed and surface air temperature from ECMWF ERA5 reanalysis. Comparative analysis with previous studies confirms the dataset's accuracy in depicting spatial patterns and the magnitude of ice production, though some variations exist across larger polynyas. While simplifications may increase uncertainties in absolute SIP values, this dataset is a valuable resource for understanding the dynamics and variability of Antarctic polynya SIP from 1992 to 2023.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sea_ice_production (m/month)

Institution: Alfred-Wegener-Institut Helmholtz-Zentrum Für Polar- Und Meeresforschung (AWI)


Title: OCEAN ICE - Future freshwater fluxes from the Antarctic ice sheet - 27 drainage basins - high emission scenario

Summary:
This dataset contains yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface meltwater runoff, sub-shelf melt, and calving fluxes (in Gt/yr) for 27 drainage basins under high-emission scenarios.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [quantile][time2][basin]):
Net_Mass_Balance (Gigatonne year-1)
Surface_Meltwater_Runoff_Fluxes (Surface Runoff, Gigatonne year-1)
Sub_Shelf_Melt_Fluxes (Sub Shelf Melt, Gigatonne year-1)
Calving_Fluxes (Calving, Gigatonne year-1)

Institution: ULB


Title: OCEAN ICE - Future freshwater fluxes from the Antarctic ice sheet - 27 drainage basins - low emission scenario

Summary:
This dataset contains yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 27 Zwally drainage basins (see http://imbie.org/imbie-2016/drainage-basins/) under a low emission scenario.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [quantile][time2][basin]):
Net_Mass_Balance (Gigatonne year-1)
Surface_Meltwater_Runoff_Fluxes (Surface Runoff, Gigatonne year-1)
Sub_Shelf_Melt_Fluxes (Sub Shelf Melt, Gigatonne year-1)
Calving_Fluxes (Calving, Gigatonne year-1)


Title: OCEAN ICE - Future freshwater fluxes from the Antarctic ice sheet - 5 Ocenan sectors - high emission scenario

Summary:
This dataset contains  contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 5 ocean sectors (Weddell Sea, Indian Ocean, western Pacific Ocean, Ross Sea, Amundsen & Bellingshausen Sea) under a high emission scenario

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [quantile][time2][sector]):
Net_Mass_Balance (Gigatonne year-1)
Surface_Meltwater_Runoff_Fluxes (Surface Runoff, Gigatonne year-1)
Sub_Shelf_Melt_Fluxes (Sub Shelf Melt, Gigatonne year-1)
Calving_Fluxes (Calving, Gigatonne year-1)


Title: OCEAN ICE - Future freshwater fluxes from the Antarctic ice sheet - 5 Ocenan sectors - low emission scenario

Summary:
This dataset contains  contain yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface mass balance, sub-shelf melt, and calving fluxes (in Gt/yr) for each of the 5 ocean sectors (Weddell Sea, Indian Ocean, western Pacific Ocean, Ross Sea, Amundsen & Bellingshausen Sea) under a low emission scenario

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [quantile][time2][sector]):
Net_Mass_Balance (Gigatonne year-1)
Surface_Meltwater_Runoff_Fluxes (Surface Runoff, Gigatonne year-1)
Sub_Shelf_Melt_Fluxes (Sub Shelf Melt, Gigatonne year-1)
Calving_Fluxes (Calving, Gigatonne year-1)


Title: OCEAN ICE - Future freshwater fluxes from the Antarctic ice sheet - high emission scenario

Summary:
This dataset contains yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface meltwater runoff, sub-shelf melt, and calving fluxes (in Gt/yr) for the Antarctic Ice Sheet under a high emission scenario.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [quantile][time2][basin]):
Net_Mass_Balance (Gigatonne year-1)
Surface_Meltwater_Runoff_Fluxes (Surface Runoff, Gigatonne year-1)
Sub_Shelf_Melt_Fluxes (Sub Shelf Melt, Gigatonne year-1)
Calving_Fluxes (Calving, Gigatonne year-1)


Title: OCEAN ICE - Future freshwater fluxes from the Antarctic ice sheet - low emission scenario

Summary:
This dataset contains yearly timeseries of the [5 25 50 75 95] percentiles for the calibrated probabilistic projections of Antarctic net mass balance, surface meltwater runoff, sub-shelf melt, and calving fluxes (in Gt/yr) for the Antarctic Ice Sheet under a low emissions scenario.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [quantile][time2][basin]):
Net_Mass_Balance (Gigatonne year-1)
Surface_Meltwater_Runoff_Fluxes (Surface Runoff, Gigatonne year-1)
Sub_Shelf_Melt_Fluxes (Sub Shelf Melt, Gigatonne year-1)
Calving_Fluxes (Calving, Gigatonne year-1)


Title: PSMSL - Absolute Sea Level Trend

Summary:
SSALTO/DUACS Delayed-Time Level-4 sea surface height and derived variables measured by multi-satellite altimetry observations over Global Ocean.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sla (Sea level anomaly, m)

Institution: CMCC - EMODnet Physics


Title: SOOS - Tracking of marine predators to protect Southern Ocean ecosystems

Summary:
We assembled tracking data from seabirds (n = 12 species) and marine mammals (n = 5 species), collected between 1991 and 2016, from across the Antarctic predator research community. See https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Standardised and https://data.aad.gov.au/metadata/records/SCAR_EGBAMM_RAATD_2018_Filtered for the tracking data. Habitat selectivity modelling was applied to these tracking data in order to identify the environmental characteristics important to each species, and to produce circum-Antarctic predictions of important geographic space for each individual species. The individual species maps were then combined to identify regions important to our full suite of species. This approach enabled us to account for incomplete tracking coverage (i.e., colonies from which no animals have been tracked) and to produce an integrated and spatially explicit assessment of areas of ecological importance across the Southern Ocean. The data attached to this metadata record include the single-species maps for Adelie, emperor, king, macaroni, and royal penguins; Antarctic and white-chinned petrels; black-browed, grey-headed, light-mantled, sooty, and wandering albatross; humpback whales; Antarctic fur seal, southern elephant seals, and crabeater and Weddell seals. The data also include the integrated maps that incorporate all species (weighted by colony size, and unweighted). See the paper and its supplementary information for full details on the modelling process and discussion of the model outputs.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
ADPE
ANFS
ANPE
BBAL
CRAS
DMSA
EMPE
GHAL
HUWH
KIPE
LMSA
... (6 more variables)

Institution: Australian Antarctic Division