A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

Sharp, Jonathan D.; Jiang, Li-Qing; Carter, Brendan R.; Lavin, Paige D.; Yoo, Hyelim; Cross, Scott L.

doi:10.1038/s41597-024-03530-7

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

2024
By Sharp, Jonathan D. ; Jiang, Li-Qing ; Carter, Brendan R. ; ...
Source: Scientific Data, 11(1)

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Journal Title:

Scientific Data
Personal Author:

Sharp, Jonathan D. ; Jiang, Li-Qing ; Carter, Brendan R. ; Lavin, Paige D. ; Yoo, Hyelim ; ... More +

Sharp, Jonathan D. ; Jiang, Li-Qing ; Carter, Brendan R. ; Lavin, Paige D. ; Yoo, Hyelim ; Cross, Scott L. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CICOES (Cooperative Institute for Climate, Ocean and Ecosystem Studies) ; PMEL (Pacific Marine Environmental Laboratory) ; CISESS (Cooperative Institute for Satellite Earth System Studies) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; ... More +

OAR (Oceanic and Atmospheric Research) ; CICOES (Cooperative Institute for Climate, Ocean and Ecosystem Studies) ; PMEL (Pacific Marine Environmental Laboratory) ; CISESS (Cooperative Institute for Satellite Earth System Studies) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ; STAR (Center for Satellite Applications and Research) Less -
Description:

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.
Source:

Scientific Data, 11(1)
DOI:

https://doi.org/10.1038/s41597-024-03530-7
ISSN:

2052-4463
Format:

PDF
Publisher:

Springer Science and Business Media LLC
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

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urn:sha-512:b2ccc56732139cfe546bf1147945cb72bb8e5bd45002a28abaa11f79af5379bf68a4a665da7e0d243d2b428b3a3429dba65d647b4a9467984821631a2dce75f8
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https://repository.library.noaa.gov/view/noaa/61538/noaa_61538_DS1.pdf
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A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

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