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AOML_ET: Partial pressure of CO2 (pCO2) and sea-air CO2 fluxes for the global ocean, along with the predictor variables from 1998-01-01 to 2023-12-30, using an Extra Trees (extremely randomized trees) machine learning (NCEI Accession 0298989)

Published by NOAA National Centers for Environmental Information | National Oceanic and Atmospheric Administration, Department of Commerce | Metadata Last Checked: February 28, 2026 | Last Modified: 2024-11-21T00:00:00.000+00:00
This dataset contains global surface water partial pressure of CO2 data, pCO2w data, and sea-air CO2 fluxes based on the extremely randomized trees or extra trees, ET machine learning approach. The products are labeled as AOML_ET. The outputs are monthly pCO2 and sea-air CO2 flux fields at 1°x1° resolution covering the global ocean from 1998 to 2023. The results of several different permutations of AOML-ET are provided. They all use the following predictor variables at monthly and at 1°x1° resolution. The variables are time, location, sea surface temperature, SST, sea surface salinity, SSS, mixed layer depth, MLD, and chlorophyll-a, chl-a. The training is performed on the v2020 and v2023 releases of the Surface Ocean CO2 Atlas (www.SOCATinfo). The sea-air CO2 fluxes are computed from the air-sea CO2 partial pressure difference, ∆pCO2, and a bulk gas transfer formulation with windspeed. This data holding includes files of the monthly 1°x1° predictor variables. Several AOML_ET products are provided in this accession. SOCATv2020 and SOCATv2023 are used for training; pCO2w and ∆pCO2 are target variables; two different datasets for air CO2 are used for the calculation of sea-air CO2 fluxes, and three different gas exchange parameterizations are applied to determine the sea-air CO2 fluxes. Details are found in the readme file.

Resources

16 resources available

  • Project Metadata

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  • NCEI Dataset Landing Page

    PLACEHOLDER/VALUE
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  • Descriptive Information

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  • HTTPS

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  • FTP

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  • https://doi.org/10.1029/2023gb007780

    PLACEHOLDER/VALUE
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  • https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system

    PLACEHOLDER/VALUE
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  • https://doi.org/10.5281/zenodo.7990823

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  • https://doi.org/10.1016/j.dsr2.2008.12.005

    PLACEHOLDER/VALUE
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  • https://doi.org/10.1016/j.dsr2.2008.12.009

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  • https://doi.org/10.4319/lom.2014.12.351

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  • https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/SPCO2_1982_present_ETH_SOM_FFN.html

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    Visit Page

  • https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/ndp_111/ndp111.html

    FILE
    Visit Page

  • https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/

    PLACEHOLDER/VALUE
    Download

  • GCMD Keyword Forum Page

    PLACEHOLDER/VALUE
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  • NCEI Contact Information

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0298989 partial pressure of carbon dioxide - water carbon dioxide (CO2) gas analyzer chemical Lamont-Doherty Earth Observatory NOAA Atlantic Oceanographic and Meteorological Laboratory University of Colorado Boulder, Cooperative Institute for Research In Environmental Sciences NOAA Atlantic Oceanographic and Meteorological Laboratory Ocean Carbon and Acidification Data System (OCADS) World-Wide Distribution oceanography COLUMBIA/LDEO > Lamont-Doherty Earth Observatory, Columbia University DOC/NOAA/OAR/AOML > Atlantic Oceanographic and Meteorological Laboratory, OAR, NOAA, U.S. Department of Commerce UCO/CIRES > Cooperative Institute for Research in Environmental Sciences, University of Colorado Ocean Carbon and Acidification Data System (OCADS) Project The NOAA office of Oceanic and Atmospheric Research is acknowledged for financial support in particular the Global Ocean Monitoring and Observations program (fund reference 100007298) EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON DIOXIDE Data Product pCO2w CO2 ANALYZERS > CO2 ANALYZERS GEOGRAPHIC REGION > GLOBAL OCEAN World-Wide Distribution

Complete Metadata
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description This dataset contains global surface water partial pressure of CO2 data, pCO2w data, and sea-air CO2 fluxes based on the extremely randomized trees or extra trees, ET machine learning approach. The products are labeled as AOML_ET. The outputs are monthly pCO2 and sea-air CO2 flux fields at 1°x1° resolution covering the global ocean from 1998 to 2023. The results of several different permutations of AOML-ET are provided. They all use the following predictor variables at monthly and at 1°x1° resolution. The variables are time, location, sea surface temperature, SST, sea surface salinity, SSS, mixed layer depth, MLD, and chlorophyll-a, chl-a. The training is performed on the v2020 and v2023 releases of the Surface Ocean CO2 Atlas (www.SOCATinfo). The sea-air CO2 fluxes are computed from the air-sea CO2 partial pressure difference, ∆pCO2, and a bulk gas transfer formulation with windspeed. This data holding includes files of the monthly 1°x1° predictor variables. Several AOML_ET products are provided in this accession. SOCATv2020 and SOCATv2023 are used for training; pCO2w and ∆pCO2 are target variables; two different datasets for air CO2 are used for the calculation of sea-air CO2 fluxes, and three different gas exchange parameterizations are applied to determine the sea-air CO2 fluxes. Details are found in the readme file.
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identifier gov.noaa.nodc:0298989
issued 2024-11-12T00:00:00.000+00:00
keyword 
                                            [
  "0298989",
  "partial pressure of carbon dioxide - water",
  "carbon dioxide (CO2) gas analyzer",
  "chemical",
  "Lamont-Doherty Earth Observatory",
  "NOAA Atlantic Oceanographic and Meteorological Laboratory",
  "University of Colorado Boulder, Cooperative Institute for Research In Environmental Sciences",
  "NOAA Atlantic Oceanographic and Meteorological Laboratory",
  "Ocean Carbon and Acidification Data System (OCADS)",
  "World-Wide Distribution",
  "oceanography",
  "COLUMBIA/LDEO > Lamont-Doherty Earth Observatory, Columbia University",
  "DOC/NOAA/OAR/AOML > Atlantic Oceanographic and Meteorological Laboratory, OAR, NOAA, U.S. Department of Commerce",
  "UCO/CIRES > Cooperative Institute for Research in Environmental Sciences, University of Colorado",
  "Ocean Carbon and Acidification Data System (OCADS) Project",
  "The NOAA office of Oceanic and Atmospheric Research is acknowledged for financial support",
  "in particular the Global Ocean Monitoring and Observations program (fund reference 100007298)",
  "EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON DIOXIDE",
  "Data Product",
  "pCO2w",
  "CO2 ANALYZERS > CO2 ANALYZERS",
  "GEOGRAPHIC REGION > GLOBAL OCEAN",
  "World-Wide Distribution"
]
landingPage https://www.ncei.noaa.gov/contact
language 
                                            []
license https://creativecommons.org/publicdomain/zero/1.0/
modified 2024-11-21T00:00:00.000+00:00
publisher 
                                            {
  "name": "NOAA National Centers for Environmental Information",
  "@type": "org:Organization"
}
references 
                                            [
  "https://doi.org/10.1029/2023gb007780",
  "https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system",
  "https://doi.org/10.5281/zenodo.7990823",
  "https://doi.org/10.1016/j.dsr2.2008.12.005",
  "https://doi.org/10.1016/j.dsr2.2008.12.009",
  "https://doi.org/10.4319/lom.2014.12.351",
  "https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/SPCO2_1982_present_ETH_SOM_FFN.html",
  "https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/ndp_111/ndp111.html",
  "https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/"
]
rights otherRestrictions
spatial 180.0,-70.0,-180.0,78.0
temporal 1998-01-01T00:00:00+00:00/2023-12-30T00:00:00+00:00
title AOML_ET: Partial pressure of CO2 (pCO2) and sea-air CO2 fluxes for the global ocean, along with the predictor variables from 1998-01-01 to 2023-12-30, using an Extra Trees (extremely randomized trees) machine learning (NCEI Accession 0298989)

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