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RFR-LME Ocean Acidification Indicators from 1998 to 2024 (NCEI Accession 0287551)
Dataset Description: Gridded monthly data products of surface ocean acidification indicators from 1998 to 2024 and on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. Large Marine Ecosystems (LMEs) using a machine learning algorithm called random forest regression (RFR). The data products are called RFR-LMEs, and were constructed using observations from the Surface Ocean CO2 Atlas — co-located with surface ocean properties from various satellite, reanalysis, and observational products — with an approach that utilized two types of machine learning algorithms: (1) Gaussian mixture models to cluster the data into subregions with similar environmental variability and (2) RFRs that were trained and applied separately in each cluster to interpolate the observational data in space and time. RFR-LMEs also rely on previously published seawater property estimation routines to obtain the full suite of ocean acidification indicators. The products show a domain-wide carbo
n dioxide partial pressure increase of 1.6 ± 0.4 μatm yr−1 and pH decrease of 0.0015 ± 0.0004 yr−1. More information on the creation and validation of RFR-LMEs is available in the following publication: Sharp, J.D., Jiang, L., Carter, B.R., Lavin, P.D., Yoo, H., Cross, S.L., 2024. A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States. Scientific Data, 11, 715, 10.1038/s41597-024- 03530-7.
Resources
9 resources available
-
Project Metadata
FILE -
NCEI Dataset Landing Page
PLACEHOLDER/VALUE -
Descriptive Information
PLACEHOLDER/VALUE -
HTTPS
PLACEHOLDER/VALUE -
FTP
PLACEHOLDER/VALUE -
https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html
FILE -
https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system
PLACEHOLDER/VALUE -
GCMD Keyword Forum Page
PLACEHOLDER/VALUE -
NCEI Contact Information
PLACEHOLDER/VALUE
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | non-public |
| contactPoint |
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|
| describedByType | application/octet-steam |
| description | Dataset Description: Gridded monthly data products of surface ocean acidification indicators from 1998 to 2024 and on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. Large Marine Ecosystems (LMEs) using a machine learning algorithm called random forest regression (RFR). The data products are called RFR-LMEs, and were constructed using observations from the Surface Ocean CO2 Atlas — co-located with surface ocean properties from various satellite, reanalysis, and observational products — with an approach that utilized two types of machine learning algorithms: (1) Gaussian mixture models to cluster the data into subregions with similar environmental variability and (2) RFRs that were trained and applied separately in each cluster to interpolate the observational data in space and time. RFR-LMEs also rely on previously published seawater property estimation routines to obtain the full suite of ocean acidification indicators. The products show a domain-wide carbo n dioxide partial pressure increase of 1.6 ± 0.4 μatm yr−1 and pH decrease of 0.0015 ± 0.0004 yr−1. More information on the creation and validation of RFR-LMEs is available in the following publication: Sharp, J.D., Jiang, L., Carter, B.R., Lavin, P.D., Yoo, H., Cross, S.L., 2024. A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States. Scientific Data, 11, 715, 10.1038/s41597-024- 03530-7. |
| distribution |
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|
| identifier | gov.noaa.nodc:0287551 |
| issued | 2024-01-23T00:00:00.000+00:00 |
| keyword |
[
"0287551",
"DISSOLVED INORGANIC CARBON (DIC)",
"partial pressure of carbon dioxide - water",
"pH",
"carbon dioxide (CO2) gas analyzer",
"pH sensor",
"titrator",
"chemical",
"model output",
"VARIOUS CHARTERED VESSELS",
"NOAA Center for Satellite Applications and Research",
"NOAA National Centers for Environmental Information",
"University of Maryland, College Park",
"University of Washington",
"University of Washington",
"Ocean Carbon and Acidification Data System (OCADS)",
"US DOC; NOAA; Office of Oceanic and Atmospheric Research; Ocean Acidification Program (OAP)",
"Arctic Ocean",
"Gulf of America",
"Gulf of Mexico",
"North Atlantic Ocean",
"North Pacific Ocean",
"South Pacific Ocean",
"oceanography",
"DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce",
"DOC/NOAA/NESDIS/STAR > Center for Satellite Applications and Research, NESDIS, NOAA, U.S. Department of Commerce",
"Ocean Acidification Program (OAP)",
"Ocean Carbon and Acidification Data System (OCADS) Project",
"EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CARBON DIOXIDE",
"EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > INORGANIC CARBON",
"EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > PH",
"Model output",
"CO3",
"DIC",
"H",
"Omega_Aragonite",
"Omega_Calcite",
"RF",
"TA",
"fCO2",
"pCO2",
"pH",
"CO2 ANALYZERS > CO2 ANALYZERS",
"PH METERS > PH METERS",
"OCEAN > ARCTIC OCEAN",
"OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN",
"OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF AMERICA",
"OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF MEXICO",
"OCEAN > PACIFIC OCEAN > NORTH PACIFIC OCEAN",
"OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN",
"Arctic Ocean",
"Atlantic Ocean",
"Pacific Ocean"
]
|
| landingPage | https://www.ncei.noaa.gov/contact |
| language |
[]
|
| license | https://creativecommons.org/publicdomain/zero/1.0/ |
| modified | 2025-09-17T00:00:00.000+00:00 |
| publisher |
{
"name": "NOAA National Centers for Environmental Information",
"@type": "org:Organization"
}
|
| references |
[
"https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html",
"https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system"
]
|
| rights | otherRestrictions |
| spatial | -58.0,-18.0,140.0,82.0 |
| temporal | 1998-01-01T00:00:00+00:00/2024-12-31T00:00:00+00:00 |
| title | RFR-LME Ocean Acidification Indicators from 1998 to 2024 (NCEI Accession 0287551) |
