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 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.

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.

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.

This version has been superseded by a newer version. It is highly recommended for users to access the current version. Users should only access this superseded version for special cases, such as reproducing studies. If necessary, this version can be accessed by contacting NCEI. The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is derived from two independent analyses: 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 1880 to the present, updated monthly, in gridded (5 degree x 5 degree) and time series formats. This data set is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 3.5.4 to version 4.0.0 include an update to the primary input dataset (ERSST) now at version 4.0.0 and GHCN-Monthly now at version 3.3.0. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

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.

Since the 1980s, the annual temperature departure from the average has been consistently positive. In 2024, the global land and ocean surface temperature anomaly stood at 1.29 degrees Celsius above the 20th-century average, the largest recorded across the displayed period. What are temperature anomalies? Temperature anomalies represent the difference from an average or baseline temperature. Positive anomalies show that the observed temperature was warmer than the baseline, whereas a negative anomaly indicates that the observed temperature was lower than the baseline. Land surface temperature anomalies are generally higher than ocean anomalies, although the exact reasons behind this phenomenon are still under debate. Temperature anomalies are generally more important in the study of climate change than absolute temperature, as they are less affected by factors such as station location and elevation. A warming planet The warmest years have been recorded over the past decade, with the highest anomaly in 2024. Global warming has been greatly driven by increased emissions of carbon dioxide and other greenhouse gases into the atmosphere. Climate change is also evident in the declining extent of sea ice in the Northern Hemisphere. Weather dynamics can affect regional temperatures, and therefore, the level of warming can vary around the world. For instance, warming trends and ice loss are most obvious in the Arctic region compared to Antarctica.

The NOAA NCEI (National Center for Environmental Information) Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature dataset derived from the International Comprehensive Ocean-Atmosphere Dataset (ICOADS). Production of the ERSST is on a 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. The newest version of ERSST, version 5, uses new data sets from ICOADS Release 3.0 (Sea Surface Temperatures) SST; SST comes from Argo floats above 5 meters, Hadley Centre sea ice and SST version 2 (HadISST.2) ice concentration. ERSSTv5 has improved SST spatial and temporal variability by * reducing spatial filtering in training the reconstruction functions Empirical Orthogonal Teleconnections (EOTs), * removing high-latitude damping in EOTs, * adding 10 more EOTs in the Arctic. ERSSTv5 improved absolute SST by switching from using Nighttime Marine Air Temperature (NMAT) as a reference to buoy SST as a reference in correcting ship SST biases. Scientists have further improved ERSSTv5 by using unadjusted First-Guess instead of adjusted First-Guess.

Monthly-mean Pacific sea-surface temperature analyses for 1949 to 1962 were compiled by the U.S. Bureau of Commercial Fisheries from observations obtained from the National Weather Records Center. Early years contained about 5000 observations per month, and that number increased to 15,000 by the end of the period. The grid covers the global area north of 20 south latitude.

This dataset provides monthly-averaged ocean potential temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Ocean and sea-ice state estimates from the 'Estimating the Circulation and Climate of the Ocean' are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric state, FLUX, and transports. ECCO V4r4 is a free-running solution of 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. V4r4 data constraints 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 potential 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 Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, 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.

In June 2025, the global land and ocean surface temperature stood 0.98 degrees Celsius above the 20th century average. The warmest years on record for global temperatures have all occurred within the last decade.

this dataset contains underwater temperature (°c) data from seven islands and two submerged rocky reefs along the santa catarina coast, southern brazil, between 26°22’ s and 28°26’ s. temperature records were acquired every 20 minutes, between december 2012 and july 2014, using a hobo pendant® temperature data logger ua-002. the data loggers were installed underwater by scuba divers and anchored to the rocky reef with epoxy at 5 m and 12 m depth, in the islands, and at 22 m depth in the submerged reefs. due to equipment loss, some depths are missing for specific sites. the dataset is structured in seven variables: site, latitude, longitude, date, time of sampling, temperature (°c) and depth (meters).

The QuOTA dataset is a collection of ocean temperature profiles from the Indian Ocean and Tasman Sea surrounding Australia. The data was collected together from various sources and instrument types, duplicate checked and quality controlled. Automated and expert/manual quality control was performed on the data. The automated quality control is discussed in Gronell, A., and S.E. Wijffels. 2008. A Semiautomated Approach for Quality Controlling Large Historical Ocean Temperature Archives. Journal Atmospheric and Oceanic Technology. v25, pp990-1003.

Temperature data from the QuOTA project are available in 5m bins in netcdf format, 2m bins by WMO squares and in a gridded format (described in documents list). They are also available in full resolution netcdf format as described in attached documentation.

Full resolution netcdf: QuOTATasmanSea.tar.gz QuOTAIndianOcean.tar.gz Format documented in 'MQNC_format.doc'

2m binned netcdf: Tasman2mbinsWMOsquares.zip IndianOcean2mbinsWMOsquares.zip Format documented in 'TZ_5m_2m_bins_header_description.doc'

5m binned netcdf: TasmanSea_TZ_5mbin_profiles.nc.gz IndianOcean_TZ_5mbin_profiles.nc.gz Format documented in 'TZ_5m_2m_bins_header_description.doc'

Gridded data netcdf: QuOTA_gridded_monthly_nc.gz Format documented in 'griddedQuotadesc.doc'

The dataset is useful as a high-quality upper ocean temperature dataset in quality control test validation, among other uses. The data collected covers the years 1772-2005. The project end was approximately 2008.

The paper describing the QuOTA quality control process is available in the CSIRO Research Publications Repository (RPR): http://hdl.handle.net/102.100.100/118409?index=1 Lineage: Ocean temperature profiles were collected from: NOAA World Ocean Database, 2001 CSIRO XBT data Bureau of Meteorology XBT data Far Seas Fisheries data (Japan) NOAA AOML XBT datasets GTSPP datasets Scripps XBT datasets WOCE (World Ocean Circulation Experiment) datasets Insititut de Recherche pour le Developpement, Noumea, New Caledonia Early CSIRO Research voyages Early Argo profiling float data

This layer represents the CMIP6 future projects for the ensemble average of ocean temperature. This is a multidimensional dataset with slices representing ocean temperature for Shared Socioeconomic Pathways (SSPs), used to model and understand how different socioeconomic factors might influence future climate change. SSP Scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5.Time Extent: 1985 (historical average), 2035, 2045, 2055, 2065, 2075, 2085*Central year of 30-year period (2020-2049, 2030-2059, 2040-2069, 2050-2079, 2060-2089, 2070-2099)Depth Extent (meters): -30, -50, -100, -200, -500Unit: degC (degrees Celsius)Source: NOAA Climate Change Web PortalThis layer can be used to better understand potential impacts of future climate change to the ocean environment.

This metadata record describes moored seawater temperature data collected at Cape Perpetua, Oregon, USA, by PISCO. Measurements were collected using StowAway Tidbit Temperature Loggers (Onset Computer Corp. TBI32-05+37) beginning 2001-06-14. The instrument depth was 009 meters, in an overall water depth of 30 meters (both relative to Mean Sea Level, MSL). The sampling interval was 5.0 minutes.

The Historical Sea Temperature Time Series from the Atlantic Arctic data collection is a compilation of selected existing multidecadal to century-scale sea temperature time series, derived from historical measurements from various sources. These data were compiled within the The Arctic System Science Program (ARCSS) project "Collaborative Research: Synthesis of Modes of Ocean-Ice-Atmosphere Covariability in the Arctic System from Multivariate Century-Scale Observations", following upon data collection efforts supported by the Norwegian Research Council and the Nordic Council of Ministers. The purpose of the data set is to provide a convenient compilation and consistent description of these data that are published though scattered. The original time series were produced by individual researchers and institutions in Denmark, Faroe Islands, Norway and Russia (see documentation and references in the readme file). Spatial and temporal coverage varies. Spatially, the data range from west Greenland to the Barents Sea. The representativeness of these point or transect data is typically sub-regional (10s of km). Temporally, the time series range from several decades to about one century. There are some missing values, usually in the early part of the series. Provided here are typically monthly data. Data are available in an ASCII file.

The SeaDataCloud TS historical data collection V2 for the North Atlantic Ocean, includes open access in situ data on temperature and salinity of water column in the North Atlantic Ocean from 10°N to 62°N, including the Labrador Sea, The data were retrieved from the SeaDataNet infrastructure at summer 2019. The dataset format is Ocean Data View (ODV - http://odv.awi.de/) binary collection. The quality control of the data has been performed with the help of ODV software. Data Quality Flags have been revised and set up using the elaborated by SeaDataNet2 project QC procedures in conjunction with the visual expert check. The final number of the Temperature and Salinity profiles (stations) in the collection is 10119755.

For data access please register at http://www.marine-id.org/.

Rutgers University Marine Remote Sensing Mid Atlantic Bight - Currents & Temperatures are available online through CODAR (Coastal Ocean RADAR) Overlays on SST Satellite images.

The satellite images that correspond with the CODAR (Coastal Ocean Radar) data, is described in detail in the following online documentation: "http://marine.rutgers.edu/mrs/education/class/josh/hf_radar.html".

The images combine the satellite temperature images with surface currents from CODAR ocean Radar. The arrows indicate the speed and direction of the surface currents. Images will only be updated when there is a clear satellite image to overlay the CODAR vectors on. The naming convention remains the same (eg. image 010402.0725.n14.gif is from 01=2001, 04=April, 02=2nd day, 0725=7:25 GMT, n14=satellite

. GMT (Greenwich Mean Time) is 4 hours ahead of EDT from Apr-Oct, and

5 hours ahead from Nov-Mar. The number to the right of the image indicates the size in kbytes. The bigger the size, the more data there is. For those visiting the Jersey shore, go to "http://www.thecoolroom.org" Where you will learn about the underwater weather along the Jersey coast.

Ensemble Median Global sea surface temperature (EMSST) is a daily SST dataset constructed by Nagoya University from an ensemble of 18 global SST products for the period from January 1, 1988 to February 28, 2019. The data set includes SST calculated as an ensemble median on each 0.25 degree by 0.25 degree grids over global ice-free oceans. The data set also includes an ensemble mean, standard deviation, minimum, maximum, number and kind of source products used.