Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean
Total alkalinity (TA) is a key parameter to understand the dynamics of biogeochemical properties in the global ocean and the effects of climate change on ocean acidification, ocean carbon cycle, and carbonate chemistry. To date, global surface ocean distributions of TA were investigated using multip...
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ftarchimer:oai:archimer.ifremer.fr:94687 2023-12-24T10:23:54+01:00 Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean Krishna, Kande Vamsi Shanmugam, Palanisamy 2023 application/pdf https://archimer.ifremer.fr/doc/00835/94687/102100.pdf https://doi.org/10.1109/ACCESS.2023.3271516 https://archimer.ifremer.fr/doc/00835/94687/ eng eng Institute of Electrical and Electronics Engineers (IEEE) https://archimer.ifremer.fr/doc/00835/94687/102100.pdf doi:10.1109/ACCESS.2023.3271516 https://archimer.ifremer.fr/doc/00835/94687/ info:eu-repo/semantics/openAccess restricted use Ieee Access (2169-3536) (Institute of Electrical and Electronics Engineers (IEEE)), 2023 , Vol. 11 , P. 42824-42838 Oceans Sea surface Sea measurements Satellites Spatiotemporal phenomena Spatial resolution Ocean temperature Climate change Total alkalinity sea surface salinity carbon chemistry global ocean satellite data text Article info:eu-repo/semantics/article 2023 ftarchimer https://doi.org/10.1109/ACCESS.2023.3271516 2023-11-28T23:51:10Z Total alkalinity (TA) is a key parameter to understand the dynamics of biogeochemical properties in the global ocean and the effects of climate change on ocean acidification, ocean carbon cycle, and carbonate chemistry. To date, global surface ocean distributions of TA were investigated using multiple regional regression approaches which require smoothening techniques due to severe boundary effects in different oceanic regions/basins across latitudes/longitudes. To reduce the uncertainties and produce spatially and temporally consistent TA products, a novel single linear regression (SLR) approach was developed in this study to estimate TA fields in the global surface ocean waters. The SLR formulation was derived using the continuous in-situ measurements of sea surface salinity (SSS) collected from the different oceans. The performance of the SLR was assessed using independent in-situ/satellite derived TA data and the results from three existing algorithms. In general, the SLR-based global surface ocean TA fields from both in-situ and satellite data agreed well with in-situ measured TA data with a mean relative error less than 1%, which is much lower compared to the error with the existing algorithms. Studies were also conducted to examine the spatiotemporal variability and trends in the global surface ocean climatology of SSS and TA fields in the context of current climate change impacts. Article in Journal/Newspaper Ocean acidification Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) IEEE Access 11 42824 42838 |
institution |
Open Polar |
collection |
Archimer (Archive Institutionnelle de l'Ifremer - Institut français de recherche pour l'exploitation de la mer) |
op_collection_id |
ftarchimer |
language |
English |
topic |
Oceans Sea surface Sea measurements Satellites Spatiotemporal phenomena Spatial resolution Ocean temperature Climate change Total alkalinity sea surface salinity carbon chemistry global ocean satellite data |
spellingShingle |
Oceans Sea surface Sea measurements Satellites Spatiotemporal phenomena Spatial resolution Ocean temperature Climate change Total alkalinity sea surface salinity carbon chemistry global ocean satellite data Krishna, Kande Vamsi Shanmugam, Palanisamy Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean |
topic_facet |
Oceans Sea surface Sea measurements Satellites Spatiotemporal phenomena Spatial resolution Ocean temperature Climate change Total alkalinity sea surface salinity carbon chemistry global ocean satellite data |
description |
Total alkalinity (TA) is a key parameter to understand the dynamics of biogeochemical properties in the global ocean and the effects of climate change on ocean acidification, ocean carbon cycle, and carbonate chemistry. To date, global surface ocean distributions of TA were investigated using multiple regional regression approaches which require smoothening techniques due to severe boundary effects in different oceanic regions/basins across latitudes/longitudes. To reduce the uncertainties and produce spatially and temporally consistent TA products, a novel single linear regression (SLR) approach was developed in this study to estimate TA fields in the global surface ocean waters. The SLR formulation was derived using the continuous in-situ measurements of sea surface salinity (SSS) collected from the different oceans. The performance of the SLR was assessed using independent in-situ/satellite derived TA data and the results from three existing algorithms. In general, the SLR-based global surface ocean TA fields from both in-situ and satellite data agreed well with in-situ measured TA data with a mean relative error less than 1%, which is much lower compared to the error with the existing algorithms. Studies were also conducted to examine the spatiotemporal variability and trends in the global surface ocean climatology of SSS and TA fields in the context of current climate change impacts. |
format |
Article in Journal/Newspaper |
author |
Krishna, Kande Vamsi Shanmugam, Palanisamy |
author_facet |
Krishna, Kande Vamsi Shanmugam, Palanisamy |
author_sort |
Krishna, Kande Vamsi |
title |
Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean |
title_short |
Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean |
title_full |
Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean |
title_fullStr |
Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean |
title_full_unstemmed |
Robust estimates of the Total Alkalinity from Satellite Oceanographic data in the Global Ocean |
title_sort |
robust estimates of the total alkalinity from satellite oceanographic data in the global ocean |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
publishDate |
2023 |
url |
https://archimer.ifremer.fr/doc/00835/94687/102100.pdf https://doi.org/10.1109/ACCESS.2023.3271516 https://archimer.ifremer.fr/doc/00835/94687/ |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Ieee Access (2169-3536) (Institute of Electrical and Electronics Engineers (IEEE)), 2023 , Vol. 11 , P. 42824-42838 |
op_relation |
https://archimer.ifremer.fr/doc/00835/94687/102100.pdf doi:10.1109/ACCESS.2023.3271516 https://archimer.ifremer.fr/doc/00835/94687/ |
op_rights |
info:eu-repo/semantics/openAccess restricted use |
op_doi |
https://doi.org/10.1109/ACCESS.2023.3271516 |
container_title |
IEEE Access |
container_volume |
11 |
container_start_page |
42824 |
op_container_end_page |
42838 |
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1786198216187838464 |