NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC) is located at NASA's Jet Propulsion Laboratory in Pasadena, California. PO.DAAC manages and provides tools and services for NASA's oceanographic and hydrologic data (satellite, airborne, and in-situ) to enable a greater understanding of the physical processes and conditions of the global ocean. Measurements include gravity, ocean winds, sea surface temperature, ocean surface topography, sea surface salinity, and circulation. The data support a wide range of applications including climate research, weather prediction, resource management, policy, and the stewardship of ocean data resources.

The Surface Ocean CO₂ Atlas (SOCAT) is a synthesis activity for quality-controlled, surface ocean fCO₂ (fugacity of carbon dioxide) observations by the international marine carbon research community (>100 contributors). SOCAT data is publicly available, discoverable and citable. SOCAT enables quantification of the ocean carbon sink and ocean acidification and evaluation of ocean biogeochemical models. SOCAT, which celebrated its 10th anniversary in 2017, represents a milestone in biogeochemical and climate research and in informing policy. SOCAT data are released in versions. Each succeeding version contains new data sets as well as updates of older ones. The first version of SOCAT was released in 2011, the second and third version followed biennially. Automation allowed annual public releases since version 4. The latest SOCAT version (version 2022) has 35.6 million observations from 1957 to 2022 for the global oceans and coastal seas. 7.2 million calibrated sensor observations are also available. SOCAT version 2023 was released on the 20th of June 2023, containing data submitted on or before 15th of January 2023. New data submissions are welcome at any time, and will be included in the next SOCAT release. The submission deadline for v2024 is 15 January 2024. SOCAT is a core Global Ocean Observing System data product for biogeochemistry endorsed by the Global Ocean Observing System GOOS.

World Ocean Atlas 2018 (WOA18) is a set of objectively analyzed (one degree grid and quarter degree grid) climatological fields of in situ temperature, salinity, dissolved oxygen, Apparent Oxygen Utilization (AOU), percent oxygen saturation, phosphate, silicate, and nitrate at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean. Quarter degree fields are for temperature and salinity only. It also includes associated statistical fields of observed oceanographic profile data interpolated to standard depth levels on quarter degree, one degree, and five degree grids. Temperature and salinity fields are available for six decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, and 2005-2017) an average of all decades representing the period 1955-2017, as well as a thirty year "climate normal" period 1981-2010. Oxygen fields (as well as AOU and percent oxygen saturation) are available using all quality controlled data 1960-2017, nutrient fields using all quality controlled data from the entire sampling period 1878-2017. This accession is a product generated by the National Centers for Environmental Information's (NCEI) Ocean Climate Laboratory Team. The analyses are derived from the NCEI World Ocean Database 2018.

Several climate indices, regarding Atlantic Basin: - North Atlantic Oscillation - Southern Oscillation Index - Bivariate ENSO Timeseries - Tropical Northern Atlantic Index - Tropical Southern Atlantic Index - Oceanic Niño Index - Multivariate ENSO Index (MEI V2) - North Tropical Atlantic SST Index - ENSO precipitation index - Northeast Brazil Rainfall Anomaly - Solar Flux (10.7cm) - Global Mean Lan/Ocean Temperature

The Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology Observing Programmes Support Centre, provides technical coordination at international level for the sustained elements of the Global Ocean Observing System. The Centre monitors in real-time the status of the observing networks and provides a toolbox to evaluate their performance and optimize their implementation and data flow. Currently OceanOPS monitors the Argo profiling floats, the DBCP surface drifters, coastal and tropical moorings, ice buoys, tsunami buoys, the OceanSITES moorings time-series, the GO-SHIP hydrographic reference lines, the SOT mat/ocean ship based observations and the GLOSS sea level tide gauges. A number of other observing systems are being added gradually, including ocean gliders, polar systems, marine mammals and potentially HF radars.

Data and imagery from the Atlantic basin: - Climate - Cloud Profiling Radars - Air-Sea & Air-Land Fluxes - Wind Profiling Radars - Satellite - Local Weather and Climate PSL archives a wide range of data ranging from gridded climate datasets extending hundreds of years to real-time wind profiler data at a single location. The data or products derived from this data, organized by type, are available to scientists and the general public at the links in the website. The third-party data appearing on this web site may be reformatted from their original form, but not altered as to the informational content contained therein. It is provided as a public service. Further, this data does not reflect an official view or position of NOAA.

This dataset comprises the global frequency, classification and distribution of marine heat waves (MHWs) from 1996-2020, in Chauhan et al. 2023 (https://doi.org/10.3389/fmars.2023.1177571). The classification was done based on their attributes and using different baselines. Daily SST values were extracted from the NOAA-OISST v2 high-resolution (0.25°) dataset from 1982-2020. MHWs were detected using the method presented by Hobday et al. 2016 (https://doi.org/10.1016/j.pocean.2015.12.014), and by using the 95th percentile of the accumulated temperature distribution to flag the extreme events. A shifting baseline of 8-year rolling period was selected between the years 1982-1996, since this period shows relatively stable maximum values of temperature across different ocean regions. The shifting baseline aims to account for the decadal changes of westerly winds, temperatures and ocean gyres circulations. The classification was done using the KMeans clustering algorithm to identify the relevant features of MHWs and classify them into separate groups based on feature similarities. This algorithm takes MHW features, namely duration, maximum intensity, rate onset and rate decline, as input vectors and applies clustering in the 4-dimensional feature space where each data point represents an MHW event. Note that all the MHWs features are standardized because unequal variances can put more weight on variables with smaller variances. This record comprehends the geospatial datasets of: Average number of MHW days per year (i.e., the sum of all MHW days divided by the total number of years, 1996-2020). Average cumulative intensity per year (i.e., the sum of cumulative intensity divided by the total number of years, 1996-2020). Total number of MHW events across the different periods averaged on the total number of years (1989-2020). The period 1982-1988 was only used as an initial baseline without calculating MHWs. Spatial distribution of three MHW categories: moderate MHWs, abrupt and Intense MHWs and extreme MHWs; displaying the total number of MHW days normalized by the number of years considered (i.e., 1989-2020). Distribution of Extreme MHWs across the different periods (A) 1989-1996, (B) 1997-2004, (C) 2005-2012, (D) 2013-2020. The relative frequency (γ) is a ratio of extreme MHWs in a specific period and all extreme MHWs in the same cluster for all periods.

Classification of the Atlantic Ocean seabed into broad-scale benthic habitats employing a hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. For ease of use, a layer is provided for each level. Level 1 has 4 classes. Level 2 has 15 classes nested within level 1. Layers indices are 2 digits (1[level1 class index]1[level 2 class index]). Level 3 has 157 classes nested within level 2 and class names have 4 digits (1digit[level1 class index]1[level 2 class index]2[level 3 class index]). Note that the classification was performed for the whole world and thus it has more classes than in the presented layer.

Classification of the seabed in the Atlantic Ocean into broad-scale benthic habitats employing a non-hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. The numbers in the raster layer correspond to individual classes. Description of these classes is given in McQuaid, K.A. et al. (2023).

Climatological monthly means output (physical variables) from the global hydrodynamic-biogeochemical model (NEMO-ERSEM) by the Plymouth Marine Laboratory (PML) within the framework of the project Mission Atlantic (https://missionatlantic.eu/). This 40-year monthly means netcdf file of 1 degree regular grid resolution is a sample aiming to show the results of the model in the geonode. The variables included in this netcdf are: sea water absolute salinity (so_abs, units: psu), sea water conservative temperature (thetao_con, units: C°), mixed layer depth (mldr10_1, units: m), latitude (lat, units: degrees), longitude (lon, units: degrees), time (time, units: seconds since 1900-01-01 00:00:00), depth [height] (z, units: m). The original model output files are stored with the data provider at the Plymouth Marine Laboratory.