Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling
The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling i...
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Copernicus Publications
2024
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00073311 2024-06-02T08:14:39+00:00 Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling Heimdal, Thea H. McKinley, Galen A. Sutton, Adrienne J. Fay, Amanda R. Gloege, Lucas 2024-04 electronic https://doi.org/10.5194/bg-21-2159-2024 https://noa.gwlb.de/receive/cop_mods_00073311 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071484/bg-21-2159-2024.pdf https://bg.copernicus.org/articles/21/2159/2024/bg-21-2159-2024.pdf eng eng Copernicus Publications Biogeosciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2158181 -- http://www.copernicus.org/EGU/bg/bg.html -- 1726-4189 https://doi.org/10.5194/bg-21-2159-2024 https://noa.gwlb.de/receive/cop_mods_00073311 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071484/bg-21-2159-2024.pdf https://bg.copernicus.org/articles/21/2159/2024/bg-21-2159-2024.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2024 ftnonlinearchiv https://doi.org/10.5194/bg-21-2159-2024 2024-05-07T02:17:27Z The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean. Article in Journal/Newspaper Southern Ocean Niedersächsisches Online-Archiv NOA Southern Ocean Biogeosciences 21 8 2159 2176 |
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Niedersächsisches Online-Archiv NOA |
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ftnonlinearchiv |
language |
English |
topic |
article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Heimdal, Thea H. McKinley, Galen A. Sutton, Adrienne J. Fay, Amanda R. Gloege, Lucas Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling |
topic_facet |
article Verlagsveröffentlichung |
description |
The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean. |
format |
Article in Journal/Newspaper |
author |
Heimdal, Thea H. McKinley, Galen A. Sutton, Adrienne J. Fay, Amanda R. Gloege, Lucas |
author_facet |
Heimdal, Thea H. McKinley, Galen A. Sutton, Adrienne J. Fay, Amanda R. Gloege, Lucas |
author_sort |
Heimdal, Thea H. |
title |
Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling |
title_short |
Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling |
title_full |
Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling |
title_fullStr |
Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling |
title_full_unstemmed |
Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling |
title_sort |
assessing improvements in global ocean pco2 machine learning reconstructions with southern ocean autonomous sampling |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/bg-21-2159-2024 https://noa.gwlb.de/receive/cop_mods_00073311 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071484/bg-21-2159-2024.pdf https://bg.copernicus.org/articles/21/2159/2024/bg-21-2159-2024.pdf |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
Biogeosciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2158181 -- http://www.copernicus.org/EGU/bg/bg.html -- 1726-4189 https://doi.org/10.5194/bg-21-2159-2024 https://noa.gwlb.de/receive/cop_mods_00073311 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00071484/bg-21-2159-2024.pdf https://bg.copernicus.org/articles/21/2159/2024/bg-21-2159-2024.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/bg-21-2159-2024 |
container_title |
Biogeosciences |
container_volume |
21 |
container_issue |
8 |
container_start_page |
2159 |
op_container_end_page |
2176 |
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1800738580162674688 |