The Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, replaces the Global sea Ice and Sea Surface Temperature (GISST) data sets, and is a unique combination of monthly globally-complete fields of SST and sea ice concentration on a 1 degree latitude-longitude grid from 1870 to date. WARNING: 10 March 2016: A detailed analysis of HadISST (https://ir.library.oregonstate.edu/xmlui/handle/1957/58385) has identified a number of problems, some of which might limit the utility of the dataset for certain scientific applications, particularly where high resolution SST data or spatial gradients of SST are required. Thanks to Dudley Chelton and Craig Risien for bringing this to our attention and their detailed analysis. The SST data are taken from the Met Office Marine Data Bank (MDB), which from 1982 onwards also includes data received through the Global Telecommunications System (GTS). In order to enhance data coverage, monthly median SSTs for 1871-1995 from the Comprehensive Ocean-Atmosphere Data Set (COADS) (now ICOADS) were also used where there were no MDB data. HadISST1 temperatures are reconstructed using a two stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. Data restrictions: for academic research use only. Updates and supplementary information will be available from http://www.hadobs.org

This archive covers two high resolution sea surface temperature (SST) analysis products developed using an optimum interpolation (OI) technique. The analyses have a spatial grid resolution of 0.25 degree and temporal resolution of 1 day. One product uses Advanced Very High Resolution Radiometer (AVHRR) infrared satellite data, while the other uses satellite data from both AVHRR and the Advanced Microwave Scanning Radiometer from NASA Earth Observing System (AMSR-E). Both products also use sea-ice data, in situ data from ships and buoys, and include a large-scale adjustment of satellite biases with respect to the in situ data. Two products are needed because of the increase in signal variance from AMSR-E due to its near all-weather coverage. For both products, the results show improved spatial and temporal resolution compared to heritage weekly 1.0 degree OISST analyses from the NWS NCEP. The AVHRR-only product uses Pathfinder AVHRR data, when available, from September 1981 through December 2005, and operational Navy AVHRR data for 2006 onwards. Pathfinder AVHRR was chosen because of good agreement with the in situ data. The combined AMSR-E and AVHRR product begins with the start of AMSR data in June 2002 and ends in October 2011, when the AMSR-E instrument ceased to function properly. In this second product, the primary AVHRR contribution is in regions near land where AMSR-E is not available. However, in cloud-free regions, use of both infrared and microwave instruments reduces systematic biases because the error characteristics are independent. For both products, in areas where sea ice is present, SST is estimated from sea ice fraction datasets from NASA GSFC before 2005 and then from NWS NCEP from 2005 onwards. Preliminary products are produced daily in near real-time (1-day latency) and may be updated in the first few days if critical input data become available after the initial production time. After two weeks, a complete or final product is generated with no additional changes expected. The preliminary products from near real-time data productions began in October 2008 for Version 2.0.

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 1854 continuing to the present and includes anomalies computed with respect to a 1971-2000 monthly climatology. Version 5 (v5) is the newest version of ERSST. Major revisions for v5 include: 1) using unadjusted first-guess instead of adjusted first-guess in QC, 2) using latest International Comprehensive Ocean Atmosphere Data Set (ICOADS) Release 3.0 (R3.0) over 1854-2015 instead of R2.5 over 1854-2007, 3) using Argo temperature above 5 meter depth that has not been used in previous version ERSST and other SST analysis, 4) using latest UK Met Office HadISST2 ice concentration over 1870-2015 instead of HadISST1 ice concentration over 1870-2010, 5) removing damping in high latitudes north of 60 degrees North and south of 50 degrees South in Empirical Orthogonal Teleconnection (EOT) Functions, 6) adding 10 more EOT modes in the Arctic, 7) reducing spatial filtering in training EOTs, and 8) revising ship SST bias correction relative to nighttime marine air temperature (NMAT) to the one relative to buoy SST observations. Other features remain same as in the previous ERSST version 4. The data are written to monthly netCDF files following CF Metadata Conventions.

The latest version of the Met Office Hadley Centre's sea surface temperature dataset, HadSST.4.1.1.0 is a monthly global field of SST on a 5° latitude by 5° longitude grid from 1850 to the present day. The data have been adjusted to minimise the effects of changes in instrumentation throughout the record. The dataset is presented as a set of interchangeable realisations that capture the temporal and spatial characteristics of the estimated uncertainties in the biases. In addition there are files providing the measurement and sampling uncertainties which must be used in addition to the ensemble to obtain a comprehensive estimate of the uncertainty. The data are not interpolated.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a monthly global merged land-ocean surface temperature analysis product that is derived from two independent analyses. The first is the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the second is a land surface air temperature (LSAT) analysis that uses the Global Historical Climatology Network - Monthly (GHCN-M) temperature database. The NOAAGlobalTemp data set contains global surface temperatures in gridded (5° × 5°) and monthly resolution time series (from 1850 to present time) data files. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. This version, v6.0, an updated version to the current operational release v5.1, is implemented by an Artificial Neural Network method to improve the surface temperature reconstruction over the land.

Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the maximum of the monthly mean climatology of SST (degrees Celsius) from 1985-2013.

Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013.

An SST climatology was first calculated by taking the average of the 5-km weekly SST data for each month, and then averaging for all same-months (e.g., January) over the 1985-2013 time period.

The surface temperature of the world's oceans reached new record levels in the first months of 2024, continuing the trend started in April 2023. As of August 6, 2024, the global sea surface temperature reached 20.98 degrees Celsius, an increase of 0.76 degrees compared to the 1982-2010 average. Overall, 2024 was a year of record temperatures on land and in the sea, with a temperature anomaly of 1.29 degrees with respect to the 20th century average. As of May 2025, temperatures this year remain lower than 2024 temperatures.

This dataset provides global daily sea surface temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA SST CCI). The GMPE system was designed to allow users to compare the outputs from different SST analysis systems and understand their similarities and differences. Although originally intended for comparison of near real time data, it has also been used to compare long historical datasets. Note that the dataset provided here is the climate version of the GMPE dataset. An operational version, with different input products and time coverage, also exists but is not distributed by the CDS. The SST analyses ingested into the GMPE system come from the following seven SST products and providers:

ESA SST CCI Analysis version 2.0 ESA SST CCI Analysis version 1.1 Copernicus Marine Environment Monitoring Service (CMEMS) Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) Reprocessing National Centers for Environmental Information (NCEI) Advanced Very High Resolution Radiometer (AVHRR) Optimal Interpolation (OI) Global Blended SST Analysis Canada Meteorological Center (CMC) 0.2-degree Global Foundation SST Analysis Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) Analysis version 2.2.0.0 Japan Meteorological Agency (JMA) Merged satellite and in-situ Data Global Daily SST (MGDSST) Analysis

These products are all spatially complete (through use of infilling or reconstruction techniques) but were originally produced for different purposes and with different user requirements in mind. Therefore, each producer has made different choices on aspects of data production such as which input observations to use and what type of SST to represent. For example, the CMEMS OSTIA, CMC, and MGDSST analyses attempt to represent the foundation SST (water temperature free of diurnal temperature variability) while the ESA SST CCI and HadISST analyses estimate the SST at a standard depth of 20 cm. The AVHRR OI product, on the other hand, is bias-corrected to in situ observations and hence will be representative of their depths. The GMPE dataset provides the median and standard deviation of the input SST products, the differences between each input product and the median, and the horizontal gradients in each of the input SST products as well as the final ensemble product. The HadISST product consists of 10 different realisations, therefore the median and standard deviation are calculated for an ensemble of 16 input fields. All fields are provided on a common 0.25 degree regular latitude-longitude grid and extend from 1 September 1981 to 31 December 2016, although some of the individual input products cover shorter periods. The dataset will not be extended beyond 2016.

Sea surface temperature is the measure of the water’s temperature at the ocean’s surface. The temperature of the ocean varies primarily with latitude. The warmest waters generally occur along the equator and the coolest near the poles. Most deviations from this pattern occur due to the ocean’s currents. For example you can see warm water moving north along the eastern coast of the United States due to the Gulf Stream current and water along the western coast of the U.S. is cooler than you might predict due to the cold water upwelling. What other deviations from the pattern do you see? Sea surface temperature is measured by satellite instruments that record the energy emanating from the ocean surface globally. The energy is emitted at different wavelengths. The data are then validated with temperature readings collected by ships and buoys. Computers then process and smooth the data according to algorithms written by scientists. Understanding sea surface temperature is important because it helps us understand why some places are warmer or cooler and how that impacts the species living there. Changes in an organism's environment can impact its access to food, alter migration patterns, or change access to mates. Additionally, as Earth warms much of the excess heat is being absorbed by the ocean causing an increase in sea surface temperatures. The National Oceanic and Atmospheric Administration (NOAA) has collected data showing the ocean has warmed approximately 0.13℃ (0.234℉) every ten years over the last century. This can change the range of marine organisms and bleach corals. It also impacts the amount of water vapor available to weather systems increasing the chances for more severe and stronger events. Warmer waters can even change the trajectory of a storm, flooding unprepared places and causing droughts in other locations.This sea surface temperature map layer is composed of radiance measurements collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the National Aeronautics and Space Administration (NASA) Terra and Aqua satellites. The temperature of the top millimeter of water was measured in Celsius and is accurate within half a degree. This map layer shows the average sea surface temperature for the month of December 2020 at one-degree resolution. Compare this layer to one of the other three sea surface temperature layers to see how sea surface temperature changed seasonally in 2020. What do you see? Next, add the ocean currents layer, what do you see?

This dataset contains daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

The Smith & Reynolds Extended Reconstructed Sea Surface Temperature (ERSST) Level 4 dataset provides a historical reconstruction of monthly global ocean surface temperatures and temperature anomalies over a 2 degree spatial grid since 1854 from in-situ observations based on a consistent statistical methodology that accounts for uneven sampling distributions over time and related observational biases. Version 4 of this dataset implements release 2.5 of ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and is supplemented by monthly GTS (Global Telecommunications Ship and buoy) system data. As for the prior ERSST version, v4 implements Empirical Orthogonal Teleconnection analysis (EOT) but with an improved tuning method for sparsely sampled regions and periods. ERSST anomalies are computed with respect to a 1971-2000 monthly climatology. The version 4 has been improved from previous version 3b. Major revisions include updated and substantially more complete input data from the ICOADS release 2.5, revised EOTs and EOT acceptance criterion, updated SST quality control procedures, revised SST anomaly evaluation methods, updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2), and buoy SST bias adjustment not previously made in v3b. The ERSST v4 in netCDF format contains extended reconstructed sea surface temperature, SST anomaly, and associated estimated SST error standard deviation fields, incompliance with CF1.6 standard metadata.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a blended product from two independent analysis products: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1850 to the present. The monthly product output is in gridded (5 degree x 5 degree) and time series formats. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. Changes to the data in version 5.1 included: removing the EOT filtering; filling in data gaps over the polar regions; and extending the beginning data coverage from 1880 to 1850.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at http://goto.arcgisonline.com/earthobs2/REMSS_SeaSurfaceTempSea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: DailyTime Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeUpdate Cycle: SporadicArcGIS Server URL: http://earthobs2.arcgis.com/arcgisTime: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. See this Esri blog post for more information on how to use this layer in your analysis. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.This layer is part of the Living Atlas of the World that provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.

In 2024, the global ocean surface temperature was 0.97 degrees Celsius warmer than the 20th-century average. Oceans are responsible for absorbing over 90 percent of the Earth's excess heat from global warming. Departures from average conditions are called anomalies, and temperature anomalies result from recurring weather patterns or longer-term climate change. While the extent of these temperature anomalies fluctuates annually, an upward trend has been observed over the past several decades. Effects of climate change Since the 1980s, every region of the world has consistently recorded increases in average temperatures. These trends coincide with significant growth in the global carbon dioxide emissions, greenhouse gas, and a driver of climate change. As temperatures rise, notable decreases in the extent of arctic sea ice have been recorded. Outlook An increase in emissions from the use of fossil fuels is projected for the coming decades. Nevertheless, global investments in clean energy have increased dramatically since the early 2000s.

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC (PO.DAAC) using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.

This dataset is funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata "history:" attribute to determine if a granule is near-realtime or retrospective.

This dataset includes HadISST1 and HadSST2 from U.K. Hadley Centre. HadISST1 is a unique combination of monthly globally-complete fields of SST and sea ice concentration on a 1 degree latitude-longitude grid from 1870 to date, and HadSST2 is a monthly global field of SST on a 5-degree latitude-longitude grid from 1850 to date, neither interpolated nor variance adjusted. The data may also be obtained from the server at the Hadley Centre [https://www.metoffice.gov.uk/hadobs/] in the UK.

This ArcGIS image service contains a set of monthly global day-night sea surface temperature averages, derived from the AVHRR Pathfinder Version 5 sea surface temperature cloud screened data set (https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst).This image service layer can be viewed using a map with a time slider.The AVHRR Pathfinder SST data sets provide the longest, most accurate, and highest resolution consistently-reprocessed SST climate data record from the AVHRR sensor series. These data files were produced to facilitate the utilization of high resolution Pathfinder v5.0 sea surface temperature data within geographic information system (GIS) software. These day-night combined monthly and yearly means were produced from cloud-screened day-night monthly full resolution files of Pathfinder SST data from 1985-2009. The data are available for download at https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.nodc:0077816. The original .HDF files are archived at the National Centers for Environmental Information under separate accession numbers. The GeoTIFF SST averages were assigned projection GCS_WGS_1984. In addition, browse images in PNG format with an associated KML file for each year are included with these data as well as detailed metadata.This is a time-enabled image service. Each image's date is stored in the "Date" field, with a format of YYYYMM.Sea surface temperatures are represented using this color scale:

Subskin Sea Surface Temperature derived from the imager SEVIRI on MSG satellites (Meteosat-8 and Meteosat-9). SST is retrieved from SEVIRI infrared channels (10.8 and 12.0 µm) using a nonlinear algorithm and the cloud mask from CM SAF. NWP outputs (temperature and humidity profiles), OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiatiave transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The product is hourly and remapped onto a regular cylindrical equidistant latitude/longitude grid at 0.05° resolution and extends from 60°S to 60°N and 60°W to 60°E. The product format is compliant with the Data Specification (GDS) version 2 from the Group for High Resolution Sea Surface Temperatures (GHRSST).

The dataset contains blended satellite-derived sea-surface temperature measurements collected by means of the Geostationary Orbiting Environmental Satellites (GOES) and the Polar-orbiting Operational Environmental Satellites (POES). This global SST analysis provides a daily gap-free map of the foundation sea surface temperature. The data is collected daily, and is available at 2-day, weekly and monthly intervals at a spatial resolution of 0.05 degrees. The geographic coverage extends for the Pacific region,and the temporal coverage ranges from 2012-present.