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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ftdoajarticles:oai:doaj.org/article:baadad0456bb47fb82557650af8d5672 2023-06-11T04:15:41+02:00 Robust Estimates of the Total Alkalinity From Satellite Oceanographic Data in the Global Ocean Kande Vamsi Krishna Palanisamy Shanmugam 2023-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2023.3271516 https://doaj.org/article/baadad0456bb47fb82557650af8d5672 EN eng IEEE https://ieeexplore.ieee.org/document/10110916/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2023.3271516 https://doaj.org/article/baadad0456bb47fb82557650af8d5672 IEEE Access, Vol 11, Pp 42824-42838 (2023) Total alkalinity sea surface salinity carbon chemistry global ocean satellite data Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2023 ftdoajarticles https://doi.org/10.1109/ACCESS.2023.3271516 2023-05-07T00:32:07Z 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 Directory of Open Access Journals: DOAJ Articles IEEE Access 11 42824 42838 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Total alkalinity sea surface salinity carbon chemistry global ocean satellite data Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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Total alkalinity sea surface salinity carbon chemistry global ocean satellite data Electrical engineering. Electronics. Nuclear engineering TK1-9971 Kande Vamsi Krishna Palanisamy Shanmugam Robust Estimates of the Total Alkalinity From Satellite Oceanographic Data in the Global Ocean |
topic_facet |
Total alkalinity sea surface salinity carbon chemistry global ocean satellite data Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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 |
Kande Vamsi Krishna Palanisamy Shanmugam |
author_facet |
Kande Vamsi Krishna Palanisamy Shanmugam |
author_sort |
Kande Vamsi Krishna |
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 |
IEEE |
publishDate |
2023 |
url |
https://doi.org/10.1109/ACCESS.2023.3271516 https://doaj.org/article/baadad0456bb47fb82557650af8d5672 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
IEEE Access, Vol 11, Pp 42824-42838 (2023) |
op_relation |
https://ieeexplore.ieee.org/document/10110916/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2023.3271516 https://doaj.org/article/baadad0456bb47fb82557650af8d5672 |
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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1768372697355517952 |