World Ocean Atlas 2023 (WOA23) 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 seven decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, 2005-2014, and 2015-2022), for three thirty year "climate normal" periods (1971-2000, 1981-2010, and 1991-2020), and for an average of the seven individual decades (1955-2022). Oxygen fields (as well as AOU and percent oxygen saturation) are available using all quality controlled bottle, CTD, and profiling float data from 1965-2022 as well as a thirty year "climate normal" using bottle and CTD data for 1971-2000. Nutrient fields are available using all quality controlled bottle data from the entire sampling period between 1965-2022. 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 2023.

The World Ocean Database (WOD) has global ocean observed and standard depth data profiles. The World Ocean Atlas (WOA) has objectively analyzed climatological mean fields on both a quarter-degree and on a one-degree longitude by latitude grids. The gridded analyses are for annual, seasonal, and monthly means of six oceanographic variables: temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate on standard levels (typically at 102 depths, dependent on data availability). The products were derived at the Ocean Climate Laboratory (OCL) of the National Oceanographic Data Center (NODC). Development of the World Ocean products began in 1982 and they have been updated in 1994, 1998, 2001, 2005, 2009, 2013, 2018 and 2023. The featured products are now based on the 2023 versions. Some products from older version are still unique.

The WOD is the world's largest profile collection covering the global ocean and contains measurements from 1800 through 2023. The WOA are climatological grids at fixed depths derived from quality checked WOD profiles. This is a recognized world-wide standard and is often referenced in oceanographic research.

🇺🇸 United States English World Ocean Atlas 2023 (WOA23) 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 seven decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, 2005-2014, and 2015-2022), for three thirty year "climate normal" periods (1971-2000, 1981-2010, and 1991-2020), and for an average of the seven individual decades (1955-2022). Oxygen fields (as well as AOU and percent oxygen saturation) are available using all quality controlled bottle, CTD, and profiling float data from 1965-2022 as well as a thirty year "climate normal" using bottle and CTD data for 1971-2000. Nutrient fields are available using all quality controlled bottle data from the entire sampling period between 1965-2022. 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 2023.

CORRECTED AND UPDATED VERSION: may break previous downloaded versions. World Ocean Atlas 2023 (WOA23) 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 seven decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, 2005-2014, and 2015-2022), for three thirty year "climate normal" periods (1971-2000, 1981-2010, and 1991-2020), and for an average of the seven individual decades (1955-2022). Oxygen f

World Ocean Atlas 2023 (WOA23) 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 seven decades (1955-1964, 1965-1974, 1975-1984, 1985-1994, 1995-2004, 2005-2014, and 2015-2022), as well as for three thirty year "climate normal" periods (1971-2000, 1981-2010, and 1991-2020). Oxygen fields (as well as AOU and percent oxygen saturation) are available using all quality controlled bottle, CTD, and profiling float data from 1965-2022 as well as a thirty year "climate normal" using bottle and CTD data for 1971-2000. Nutrient fields are available using all quality controlled bottle data from the entire sampling period between 1965-2022. 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 2023.

Climatological mean salinity for the global ocean from in situ profile data _NCProperties=version=2,netcdf=4.9.2,hdf5=1.14.3 cdm_data_type=Grid comment=Global Climatology as part of the World Ocean Atlas Project.Modified at NOAA/AOML to keep only the upper 500m s_an values contributor_name=Ocean Climate Laboratory contributor_role=Calculation of climatologies Conventions=CF-1.6, ACDD-1.3, COARDS Easternmost_Easting=179.875 geospatial_lat_max=89.875 geospatial_lat_min=-89.875 geospatial_lat_resolution=0.25 geospatial_lat_units=degrees_north geospatial_lon_max=179.875 geospatial_lon_min=-179.875 geospatial_lon_resolution=0.25 geospatial_lon_units=degrees_east id=woa23_decav_s12_04.nc infoUrl=https://www.nodc.noaa.gov/OC5/woa18/ institution=NCEI keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=Unidata Dataset Discovery v1.0 metadata_link=https://www.ncei.noaa.gov/products/world-ocean-atlas naming_authority=gov.noaa.ncei ncei_template_version=NCEI_NetCDF_Grid_Template_v1.0 Northernmost_Northing=89.875 processing_level=processed project=World Ocean Atlas references=Reagan, J.R., D. Seidov, Z. Wang, D. Dukhovskoy, T.P. Boyer, R.A. Locarnini, O.K. Baranova, A.V. Mishonov, H.E. Garcia, C. Bouchard, S.L. Cross, and C.R. Paver (2023). World Ocean Atlas 2023, Volume 2: Salinity. A. Mishonov Technical Editor, NOAA Atlas NESDIS 90, https://doi.org/10.25923/70qt-9574. sourceUrl=(local files) Southernmost_Northing=-89.875 standard_name_vocabulary=CF-1.6 time_coverage_duration=P68Y time_coverage_end=1988-12-16T00:00:00Z time_coverage_resolution=P01M time_coverage_start=1988-01-16T00:00:00Z Westernmost_Easting=-179.875

The World Ocean Atlas 2023 (WOA23) climate normals of temperature and salinity are a set of objectively analyzed (one degree grid and quarter degree grid) climatological fields of in situ temperature and salinity at standard depth levels for annual, seasonal, and monthly compositing periods for the World Ocean over the 1991-2020 time period. Additionally, the WOA23 climate normals of temperature and salinity include associated statistical fields of observed oceanographic profile data interpolated to standard depth levels on quarter degree and one degree grids. The temperature and salinity 1991-2020 climate normal fields are an average of the 1991-2000, 2001-2010, and 2011-2020 decadal climatologies. 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. The temperature and salinity climate normals were the first fields released as part of the World Ocean Atlas 2023 on October 18, 2022.

Acoustics is most effective in undersea detection, localization, and communication. Establishment of a global climatological dataset of undersea acoustic parameters becomes urgent. In building such a dataset, first we use the Thermodynamic Equation of Seawater-2010 (TEOS-10) to calculate the sound speed (SS) from the gridded temperature and salinity fields of the NOAA/NCEI World Ocean Atlas 2023. Second, we determine the depth of overall minimum from SS profile as the deep sound channel (DSC) axis depth, the depth of overall maximum between the surface and DSC axis as the sonic layer depth (SLD), the depth of the local minimum between SLD and DSC axis as the second sound channel (SSC) depth, and the depth with the SS equalling the maximum SS as the critical depth. Third, we obtain the SS at the surface, SLD, DSC axis, and SSC axis. Fourth, we determine the other acoustic parameters such as In-layer gradient, below layer gradient, DSC strength, SSC strength, depth excess, and surface duct cut-off frequency. The dataset is publicly available.

This is the global ocean climatological dataset of 17 thermohaline parameters along with 2 identity-indices for determining isothermal layer (ITL) depth (ILD) and mixed layer depth (MLD) from gridded (0.25 degree, 1 degree, 5 degree) temperature and salinity fields of the National Centers for Environmental Information (NCEI) world ocean atlas 2023. The exponential leap-forward gradient method is used to identify (ILD, MLD) as well as mean thermocline and pycnocline gradients at each horizontal grid point. With the given (ILD, MLD) and mean thermocline and pycnocline gradients, other parameters such as ITL heat content, mixed layer fresh-water content, ITL temperature, mixed layer density, temperature jump, density jump, maximum thermocline gradient, thermocline depth, temperature at thermocline depth, maximum density gradient, pycnocline depth, density at pycnocline depth, ILD minus MLD. The quality measure (I- index) is provided. The data of (ILD, MLD, as well as their derived parameters) are flagged out with the I-index smaller than 0.5. All derived parameters are calculated from WOA-2023 objectively analyzed profile data. Results are in NetCDF.

Acoustics is most effective in undersea detection, localization, and communication. However, there is no global climatology of underwater acoustic parameters. A multiple gradient method is used to analyze the sound speed profile, which is calculated from temperature and salinity profiles using the Thermodynamic Equation of Seawater-2010 (TEOS-10) to get the sonic layer depth (SLD), deep sound channel (DSC) axis depth, and second sound channel (SSC) axis depth from the gridded (0.25 degree, 1 degree, 5 degree) temperature and salinity fields of the National Centers for Environmental Information (NCEI) world ocean atlas 2023. With the given (SLD, DSC axis depth, SSC axis depth), the other acoustic parameters are calculated such as sound speed at the surface, sound speed at the SLD, sound speed at the DSC axis, sound speed at the SSC axis, in-layer gradient, below-layer gradient, surface duct cut-off frequency, deep sound channel strength, critical depth of the DSC, depth excess, and second sound channel strength. Data are in NetCDF.

The World Ocean Database (WOD) is the world's most comprehensive and largest collection of world wide in situ oceanographic measurements covering the time period 1772 to present (Mishonov et al. 2024, Garcia et al. 2024). The observations are in an uniform format, quality-controlled, and reproducible from the original data archived at NOAA's NCEI. The data in WOD are searchable via extensive granular metadata including quality control flags. The metadata preserves quality control flags added from data originators if available. Since its inception in 1994, WOD functions as an international FAIR-compliant data sharing resource by the broader global ocean and coastal communities. WOD has been an activity of the International Oceanographic Data and Information Exchange (IODE) as well as the World Data Service for Oceanography of the World Data System (WDS).

The WOD is continuously updated with new or updated data and metadata shared over time by 97 countries. As of December 2024, WOD includes ~3.6 billion observations collected on ~20.6 million casts with up to 27 profiles of the most commonly measured physical and chemical essential ocean variables and plankton. WOD includes in situ measurements of temperature, salinity (conductivity), dissolved oxygen, dissolved inorganic nutrients (phosphate, nitrate, nitrate + nitrite, nitrite, silicate), chlorophyll, alkalinity, pH, partial pressure of carbon dioxide, dissolved inorganic carbon, Tritium, Carbon-13, Carbon-14, Helium-3, Oxygen-18, chlorofluorocarbons (CFC-11, CFC-12, CFC-113), Argon, Helium, Neon, Transmissivity (Beam Attenuation Coefficient), and plankton. Casts include meteorological and sea state observations. The data are web and cloud accessible in interoperable formats including Climate and Forecast compliant ragged-array NetCDF. WOD enables multiuse of the global in situ oceanographic instrumental data record critical for quantifying ocean variability as well as in the development of value-added scientific and socioeconomic data products, information, and services.

The WOD consists of periodic major releases and quarterly updates to those releases. Each major release is associated with a concurrent release of a WOA release, and contains final quality control flags used in the corresponding WOA, which includes manual as well as automated steps. Each quarterly update release includes additional historical and recent data and preliminary quality control. The latest major release was WOD 2023 (WOD23), which includes more than 18.8 million oceanographic casts, from the second voyage of Captain Cook (1772) to the modern Argo floats (end of 2022).

This dataset contains a global gridded data product of observation-based ocean interior dissolved oxygen concentrations. The data product is called GOBAI-O2 for Gridded Ocean Biogeochemistry from Artificial Intelligence - Oxygen. The dissolved oxygen fields were constructed by training machine learning algorithms with observations from shipboard analyses and autonomous profiling floats, then applying those trained algorithms to global gridded fields of temperature and salinity. Those temperature and salinity fields were calculated from a long-term mean field and monthly anomaly fields constructed from the global array of Argo floats (Roemmich and Gilson, 2009), and are presented alongside GOBAI-O2 for easy analysis. Also presented are uncertainty fields for dissolved oxygen, which were calculated by combining three separate sources of uncertainty as described in Sharp et al. (2023), see the Documentation. The scope and resolution of GOBAI-O2 are as follows: geographically, from -179.5 to 179.5 degrees longitude and -64.5 to 79.5 degrees latitude at 1-degree resolution; with respect to pressure, from 2.5 to 1975 decibars on 58 levels that become incrementally further spaced; and temporally, from January 2004 to December 2024 at monthly resolution. The algorithms used to produce GOBAI-O2 have been validated using real observations and synthetic data from model output, and the data product itself has been compared against the World Ocean Atlas and selected discrete measurements. Results of these validation and comparison exercises for GOBAI-O2-v2.1 are detailed in Sharp et al. (2023). Some updates to the methodology have been introduced for GOBAI-O2-v2.3, which will be described in an upcoming manuscript (Sharp et al., in prep): Observational O2 data from floats is still adjusted based on a crossover comparison with bottle O2 data, however, the adjustment equation is now a linear fit of the percent difference (Argo - bottle) in oxygen saturation as a function of oxygen saturation. Model-based experimentation has revealed some spatial and temporal discontinuities in GOBAI-O2 introduced by the Random Forest Regression models. For this reason, GOBAI-O2-v2.3 is based only on feed-forward neural networks. Rather than basin-specific clusters for algorithm training and application (as in GOBAI-O2-v2.1 and v2.2), clusters are now developed based on unsupervised learning (Gaussian mixture modeling) with temperature, salinity, and depth data. Algorithm-based uncertainty is now calculated from an ensemble of five model simulation experiments, rather than just one. This provides a more robust estimate of uncertainty in GOBAI-O2. Due to limited data in the Mediterranean Sea, the neural network for the cluster covering mostly the upper water column in the Mediterranean has been trained and applied without year as a predictor variable. Data are in netCDF, Figures are in PNG.

The ocean absorbs one quarter of the global CO2 emissions from human activity. The community-led Surface Ocean CO2 Atlas (www.socat.info) is key for the quantification of ocean CO2 uptake and its variation, now and in the future. SOCAT version 2023 has quality-controlled in situ surface ocean fCO2 (fugacity of CO2) measurements on ships, moorings, autonomous and drifting surface platforms for the global oceans and coastal seas from 1957 to 2023. The main synthesis and gridded products contain fCO2 values with an estimated accuracy of better than 5 μatm. Sensor fCO2 data with an estimated accuracy of 5 to 10 μatm are separately available. During quality control, marine scientists assign a flag to each data set, as well as WOCE flags of 2 (good), 3 (questionable) or 4 (bad) to individual fCO2 values. Data sets are assigned flags of A and B for an estimated accuracy of better than 2 μatm, flags of C and D for an accuracy of better than 5 μatm and a flag of E for an accuracy of better than 10 μatm. Bakker et al. (2016) describe the quality control criteria used in SOCAT versions 3 to 2023. Quality control comments for individual data sets can be accessed via the SOCAT Data Set Viewer (www.socat.info). All data sets, where data quality has been deemed acceptable, have been made public. The main SOCAT synthesis files and the gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Access to data sets with an estimated accuracy of 5 to 10 (flag of E) and fCO2 values with flags of 3 and 4 is via additional data products and the Data Set Viewer (Table 8 in Bakker et al., 2016). SOCAT publishes a global gridded product with a 1° longitude by 1° latitude resolution. A second product with a higher resolution of 0.25° longitude by 0.25° latitude is available for the coastal seas. The gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Gridded products are available monthly, per year and per decade. Two powerful, interactive, online viewers, the Data Set Viewer and the Gridded Data Viewer (www.socat.info), enable investigation of the SOCAT synthesis and gridded data products. SOCAT data products can be downloaded. Matlab code is available for reading these files. Ocean Data View also provides access to the SOCAT data products (www.socat.info). SOCAT data products are discoverable, accessible and citable. The SOCAT Data Use Statement (www.socat.info) asks users to generously acknowledge the contribution of SOCAT scientists by invitation to co-authorship, especially for data providers in regional studies, and/or reference to relevant scientific articles. The SOCAT website (www.socat.info) provides a single access point for online viewers, downloadable data sets, the Data Use Statement, a list of contributors and an overview of scientific publications on and using SOCAT. Automation of data upload and initial data checks allows annual releases of SOCAT from version 4 onwards. SOCAT is used for quantification of ocean CO2 uptake and ocean acidification and for evaluation of climate models and sensor data. SOCAT products inform the annual Global Carbon Budget since 2013. The annual SOCAT releases by the SOCAT scientific community are a Voluntary Commitment for United Nations Sustainable Development Goal 14.3 (Reduce Ocean Acidification) (#OceanAction20464). More broadly the SOCAT releases contribute to UN SDG 13 (Climate Action) and SDG 14 (Life Below Water), and to the UN Decade of Ocean Science for Sustainable Development. Hundreds of peer-reviewed scientific publications and high-impact reports cite SOCAT. The SOCAT community-led synthesis product is a key step in the value chain based on in situ inorganic carbon measurements of the oceans, which provides policy makers with critical information on ocean CO2 uptake in climate negotiations. The need for accurate knowledge of global ocean CO2 uptake and its (future) variation makes sustained funding of in situ surface ocean CO2 observations imperative.

We employed an algorithm for estimating the monthly average sea surface nitrate (SSN) on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1° × 1° grid and the estimated monthly sea surface temperature (SST) and photosynthetically active radiation (PAR) datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and mixed layer depth (MLD) from the Hybrid Coordinate Ocean Model (HYCOM). This dataset contains (1) the predictor variables used to construct the models; (2) the dependent variables used in model development; (3) the local multivariate linear regression models; (4) the global monthly SSN products from 2003 to 2023, generated by local multivariate linear regression models; (5) the validation dataset containing measured and model predictions for 2018-2023. The predictor variables of the method include SST, MLD and PAR. The spatial resolution of the simulated dataset is 1° by 1°. The units of SSN concentration are µmol/l. The relevant data describing paper has been published in the Journal 'Science of the Total Environment' in 2024.

Meteorological data acquired on board the R/V Hespérides with an AANDERAA Scanning Unit 3010 Station in continuous mode during the ELEFANTE-12 cruise

Scripts and datasets used for creating the results of a submitted work :

R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak: A unified energy-constrained mesoscale parameterisation for ocean climate models. (submitted in JAMES).

Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").

The reference EKE of Torres et al. (2023) is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. World Ocean Atlas 2018, Tsujino et al. (2020) and RAPID)

IPython notebooks for computing and plotting metrics are provided :

• james-eke-heat_budget.ipynb : plots for heat transport and global heat storage (section 4.1)
• james-eke-southern_ocean.ipynb : plots for Southern Ocean (section 4.2) analysis
• james-eke-north_atlantic.ipynb : plots for North Atlantic and Labrador Sea (section 4.3) analysis
• james-eke-timeseries.ipynb : plot 0D metric timeseries for simulations (including spin-up)

Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.

Net primary production (mg C m-2 d-1) calculated from the Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HYCOM using the density criterion of 0.030 kg m-3.

Data on a regular 25km grid at a 8 day resolution.

Version 1.1

Fixed minor issues with:

Conversion of bbp(443) to phytoplankton carbon.

Missing values at ~180W.

Version 1.2

Updated to include 2023.

Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.

Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.

File structure compressed using Zlib.

Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.

No further updates are planned for this data product - please email tryankeogh@csir.co.za if you have interest in further updates.

Net primary production (mg C m^-2 d^-1) calculated from the Eppley-VGPM, Behrenfeld-VGPM, Behrenfeld-CbPM, Westberry-CbPM and Silsbe-CAFE algorithms. MLD data taken from HADLEY EN4.2.2 using the density criterion of 0.03 kg m^-3.

Data on a regular 25km grid at a 8 day resolution.

Version 1.1

Fixed minor issues with:

1. Conversion of bbp(443) to phytoplankton carbon.
2. Missing values at ~180W.

Version 1.2

1. Updated to include 2023.
2. Westberry-CbPM Nitracline depth updated with World Ocean Atlas 2023 Nitrate Data.
3. Silsbe-CAFE bbwater calculations updated with World Ocean Atlas 2023 Salinity Data.
4. File structure compressed using Zlib.

Please note for Westberry-CbPM and Silsbe-CAFE all years were reprocessed.

this product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: surface ocean carbon atlas socatv2023 and global ocean data analysis project glodapv2.2023.the socatv2023-obs dataset contains >25 million observations of fugacity of co2 of the surface global ocean from 1957 to early 2023. the quality control procedures are described in bakker et al. (2016). these observations form the basis of the gridded products included in socatv2023-gridded: monthly, yearly and decadal averages of fco2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean.glodapv2.2023-obs contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and ph from 1972 to 2021. these data were subjected to an extensive quality control and bias correction described in olsen et al. (2020). glodapv2-gridded contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and ph over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of glodap, glodapv2.socat and glodap are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see references below) in order to support future sustainability of the data products.

This dataset contains a global gridded dataset of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification (v2023). The full marine carbonate system is calculated from machine learning estimates of Total Alkalinity (TA) and the fugacity of carbon dioxide (fCO2). The surface-ocean fCO2 presented here is the ensemble mean of 16 two-step clustering-regression machine learning estimates. The ensemble is a combination of eight clustering instances and two regression methods. For the clustering, we use K-means clustering (21 clusters) repeated with different initiations, resulting in slightly different clusters. Two machine learning regression methods are applied to each of these clustering instances. These machine learning methods are feed-forward neural-network (FFNN), and gradient boosted machine using decision trees (GBDT). The average of the ensemble members is used as the final estimate. Further, the standard deviation of the ensemble members is an analog of the uncertainty. The same two-step clustering-regression approach is used to estimate TA. The final estimate is the mean of 16 ensemble members. Eight of the ensemble members estimate standard TA while the other half estimate salinity normalized TA (S0 ≈ 34.0). Each ensemble member has 12 clusters. Support vector regression (SVR) is used as the regression method. Again, the standard deviation of the ensemble members is an analog of the uncertainty. Total alkalinity and pCO2 are then used to solve for the remaining parameters of the marine carbonate system using the PyCO2SYS software. The temperature and salinity products used in this calculation are provided in the file. Phosphate and silicate from the interpolated World Ocean Atlas (2018) product were used. We use the following total scale for pH. The product extends from the start of 1982 to the end of 2022.