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 NCEP/DOE Reanalysis 2 (air.2m.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][level][latitude][longitude]):
air (Daily Forecast of Air temperature at 2 m, degK)


Title: Daily NCEP/DOE Reanalysis 2 (cprat.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
cprat (Daily Mean of Convective Precipitation Rate at Surface, Kg/m^2/s)


Title: Daily NCEP/DOE Reanalysis 2 (dlwrf.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
dlwrf (Daily Mean of Downward Longwave Radiation Flux at Surface, W/m^2)


Title: Daily NCEP/DOE Reanalysis 2 (dswrf.ntat.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
dswrf (Daily Mean of Downward Solar Radiation Flux at the Nominal Top of Atmosphere, W/m^2)


Title: Daily NCEP/DOE Reanalysis 2 (icec.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
icec (Daily Forecast of Ice Concentration at Surface)


Title: Daily NCEP/DOE Reanalysis 2 (lhtfl.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
lhtfl (Daily Mean of Latent Heat Net Flux at Surface, W/m^2)


Title: Daily NCEP/DOE Reanalysis 2 (pevpr.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
pevpr (Daily Mean of Potential Evaporation Rate at Surface, W/m^2)


Title: Daily NCEP/DOE Reanalysis 2 (prate.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
prate (Daily Mean of Precipitation Rate at Surface, Kg/m^2/s)


Title: Daily NCEP/DOE Reanalysis 2 (uflx.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
uflx (Daily Mean of Momentum Flux, u-component at Surface, N/m^2)


Summary:
EMODnet Physics - Absolute Sea Level Trends - trends are derived from the DUACS delayed-time (DT-2018 version) altimeter gridded maps of sea level anomalies based on a stable number of altimeters (two) in the satellite constellation.

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

Institution: CMCC - EMODnet Physics


Summary:
EMODnet Physics - Deseasonalized Sea Level monthly means Global Oceans. This product is based, uses and reprocess the CMEMS product id. SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057.

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


Title: EMODnet Physics - Sea Level monthly mean, Global Oceans. This product is based, uses and reprocess the CMEMS product id. SEALEVEL_GLO_PHY_CLIMATE_L4_REP_OBSERVATIONS_008_057

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)


Title: Global Ocean Data Analysis Project for Carbon (GLODAP) - Mapped Climatologies v2 2016b

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: mean Daily NCEP/DOE Reanalysis 2 (gflux.sfc.gauss.1979)

Summary:
Data is from
National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2)
(4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
gflux (daily mean 6-Hourly Mean of Ground Heat Flux at Surface, W/m^2)


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

Summary:
The NOAA 1/4Â° 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 1Â° 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: SOCHIC-Generalisation/icec_sfc_mon_mean

Summary:
Data is from National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmosphere Model Intercomparison Project (AMIP)-II Reanalysis (Reanalysis-2) (4x/day).  Data interpolated from model (sigma) surfaces to gaussian grid.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
icec (Monthly Mean of Forecast of Ice Concentration at Surface)


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