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NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 6

The NOAA Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature dataset derived from the International Comprehensive Ocean-Atmosphere Dataset (ICOADS). It is produced on a 2 x 2 degree grid with spatial completeness enhanced using statistical methods. This monthly analysis begins in January 1850 continuing to the present and includes anomalies computed with respect to a 1971-2000 monthly climatology. Version 6 (v6) is the newest version of ERSST. ERSSTv6 uses in situ ship, buoy, and Argo observations and reconstructed using an Artificial Neural Network method. The data are available in NetCDF and ASCII formats, with the same file convention of the previous version ERSSTv5. Major changes of ERSSTv6 from previous version ERSSTv5 include: (1) removing a 3-month running average, (2) using an interpolation method of an artificial neural network (ANN) instead of the Empirical Orthogonal Teleconnection (EOT) functions, 3) applying a nearest neighbor check (NNC) quality-control algorithm on ship observations, 4) applying a large-scale (>200 km) filter (LS200) on SST superobservations, and 5) upgrading algorithms in proxy SST from ice concentration using climatological SST and sea surface salinity instead of proxy SST of a constant freezing point of -1.8°C. SST anomalies (SSTAs) are in reference to 1991-2020 climatology, while 1971-2000 climatology was used in previous versions. Other features remain the same as in ERSSTv5. Other features remain same as in the previous ERSST version 5. The data are written to monthly netCDF files following CF Metadata Conventions.

About this Dataset

Updated: 2025-11-20
Metadata Last Updated: 2025-11-20T02:57:01.180Z
Date Created: N/A
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Dataset Owner: N/A

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Title NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 6
Description The NOAA Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature dataset derived from the International Comprehensive Ocean-Atmosphere Dataset (ICOADS). It is produced on a 2 x 2 degree grid with spatial completeness enhanced using statistical methods. This monthly analysis begins in January 1850 continuing to the present and includes anomalies computed with respect to a 1971-2000 monthly climatology. Version 6 (v6) is the newest version of ERSST. ERSSTv6 uses in situ ship, buoy, and Argo observations and reconstructed using an Artificial Neural Network method. The data are available in NetCDF and ASCII formats, with the same file convention of the previous version ERSSTv5. Major changes of ERSSTv6 from previous version ERSSTv5 include: (1) removing a 3-month running average, (2) using an interpolation method of an artificial neural network (ANN) instead of the Empirical Orthogonal Teleconnection (EOT) functions, 3) applying a nearest neighbor check (NNC) quality-control algorithm on ship observations, 4) applying a large-scale (>200 km) filter (LS200) on SST superobservations, and 5) upgrading algorithms in proxy SST from ice concentration using climatological SST and sea surface salinity instead of proxy SST of a constant freezing point of -1.8°C. SST anomalies (SSTAs) are in reference to 1991-2020 climatology, while 1971-2000 climatology was used in previous versions. Other features remain the same as in ERSSTv5. Other features remain same as in the previous ERSST version 5. The data are written to monthly netCDF files following CF Metadata Conventions.
Modified 2025-11-20T02:57:01.180Z
Publisher Name N/A
Contact N/A
Keywords Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature , Oceanic - Surface - Sea-surface Temperature , Geographic Region > Global Ocean , Vertical Location > Sea Surface , 100 km - HTML Markup Was Removed , DOC/NOAA/NESDIS/NCDC > National Climatic Data Center, NESDIS, NOAA, U.S. Department of Commerce , DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce , climatologyMeteorologyAtmosphere
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