Ocean Bottom Pressure Variability: Can It Be Reliably Modeled?
Ocean bottom pressure (OBP) variability serves as a proxy of ocean mass variability, the knowledge of which is needed in geophysical applications. The question of how well it can be modeled by the present general ocean circulation models on time scales in excess of 1 day is addressed here by compari...
Published in: | Journal of Geophysical Research: Oceans |
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ftsubggeo:oai:e-docs.geo-leo.de:11858/9032 2023-05-15T18:25:33+02:00 Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? Androsov, Alexey Boebel, Olaf Schröter, Jens Danilov, Sergey Macrander, Andreas Ivanciu, Ioana 2020 https://doi.org/10.1029/2019JC015469 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9032 eng eng doi:10.1029/2019JC015469 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9032 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY ddc:551.46 in situ ocean bottom pressure pressure inverted echo sounder PIES modeling OBP variability daily and monthly scales atmospheric loading doc-type:article 2020 ftsubggeo https://doi.org/10.1029/2019JC015469 2022-11-09T06:51:38Z Ocean bottom pressure (OBP) variability serves as a proxy of ocean mass variability, the knowledge of which is needed in geophysical applications. The question of how well it can be modeled by the present general ocean circulation models on time scales in excess of 1 day is addressed here by comparing the simulated OBP variability with the observed one. To this end, a new multiyear data set is used, obtained with an array of bottom pressure gauges deployed deeply along a transect across the Southern Ocean. We present a brief description of OBP data and show large-scale correlations over several thousand kilometers at all time scales using daily and monthly averaged data. Annual and semiannual cycles are weak. Close to the Agulhas Retroflection, signals of up to 30 cm equivalent water height are detected. Further south, signals are mostly intermittent and noisy. It is shown that the models simulate consistent patterns of bottom pressure variability on monthly and longer scales except for areas with high mesoscale eddy activity, where high resolution is needed to capture the variability due to eddies. Furthermore, despite good agreement in the amplitude of variability, the in situ and simulated OBP show only modest correlation. Article in Journal/Newspaper Southern Ocean GEO-LEOe-docs (FID GEO) Southern Ocean Journal of Geophysical Research: Oceans 125 3 |
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Open Polar |
collection |
GEO-LEOe-docs (FID GEO) |
op_collection_id |
ftsubggeo |
language |
English |
topic |
ddc:551.46 in situ ocean bottom pressure pressure inverted echo sounder PIES modeling OBP variability daily and monthly scales atmospheric loading |
spellingShingle |
ddc:551.46 in situ ocean bottom pressure pressure inverted echo sounder PIES modeling OBP variability daily and monthly scales atmospheric loading Androsov, Alexey Boebel, Olaf Schröter, Jens Danilov, Sergey Macrander, Andreas Ivanciu, Ioana Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? |
topic_facet |
ddc:551.46 in situ ocean bottom pressure pressure inverted echo sounder PIES modeling OBP variability daily and monthly scales atmospheric loading |
description |
Ocean bottom pressure (OBP) variability serves as a proxy of ocean mass variability, the knowledge of which is needed in geophysical applications. The question of how well it can be modeled by the present general ocean circulation models on time scales in excess of 1 day is addressed here by comparing the simulated OBP variability with the observed one. To this end, a new multiyear data set is used, obtained with an array of bottom pressure gauges deployed deeply along a transect across the Southern Ocean. We present a brief description of OBP data and show large-scale correlations over several thousand kilometers at all time scales using daily and monthly averaged data. Annual and semiannual cycles are weak. Close to the Agulhas Retroflection, signals of up to 30 cm equivalent water height are detected. Further south, signals are mostly intermittent and noisy. It is shown that the models simulate consistent patterns of bottom pressure variability on monthly and longer scales except for areas with high mesoscale eddy activity, where high resolution is needed to capture the variability due to eddies. Furthermore, despite good agreement in the amplitude of variability, the in situ and simulated OBP show only modest correlation. |
format |
Article in Journal/Newspaper |
author |
Androsov, Alexey Boebel, Olaf Schröter, Jens Danilov, Sergey Macrander, Andreas Ivanciu, Ioana |
author_facet |
Androsov, Alexey Boebel, Olaf Schröter, Jens Danilov, Sergey Macrander, Andreas Ivanciu, Ioana |
author_sort |
Androsov, Alexey |
title |
Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? |
title_short |
Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? |
title_full |
Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? |
title_fullStr |
Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? |
title_full_unstemmed |
Ocean Bottom Pressure Variability: Can It Be Reliably Modeled? |
title_sort |
ocean bottom pressure variability: can it be reliably modeled? |
publishDate |
2020 |
url |
https://doi.org/10.1029/2019JC015469 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9032 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
doi:10.1029/2019JC015469 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9032 |
op_rights |
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1029/2019JC015469 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
125 |
container_issue |
3 |
_version_ |
1766207075702013952 |