Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach
This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surfa...
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ftmdpi:oai:mdpi.com:/2072-4292/15/7/1722/ 2023-08-20T04:03:51+02:00 Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach Marta Umbert Eva De-Andrés Rafael Gonçalves-Araujo Marina Gutiérrez Roshin Raj Laurent Bertino Carolina Gabarró Jordi Isern-Fontanet agris 2023-03-23 application/pdf https://doi.org/10.3390/rs15071722 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs15071722 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 7; Pages: 1722 ocean currents Arctic ocean dynamics Surface Quasi-Geostrophy sea surface height remote sensing physical oceanography Text 2023 ftmdpi https://doi.org/10.3390/rs15071722 2023-08-01T09:23:52Z This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 m. In the second part, we apply the SQG approach validated with the reanalysis outputs to satellite-derived sea level anomalies and validate the results against in-situ measurements. Due to lower water column stratification, the SQG approach’s performance is better in fall and winter than in spring and summer. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct the upper ocean dynamics in the Arctic and Subarctic Seas up to 400 m. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocity observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 m with higher resolutions and lower uncertainties. Text Arctic Arctic Ocean Sea ice Subarctic MDPI Open Access Publishing Arctic Arctic Ocean Remote Sensing 15 7 1722 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
ocean currents Arctic ocean dynamics Surface Quasi-Geostrophy sea surface height remote sensing physical oceanography |
spellingShingle |
ocean currents Arctic ocean dynamics Surface Quasi-Geostrophy sea surface height remote sensing physical oceanography Marta Umbert Eva De-Andrés Rafael Gonçalves-Araujo Marina Gutiérrez Roshin Raj Laurent Bertino Carolina Gabarró Jordi Isern-Fontanet Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach |
topic_facet |
ocean currents Arctic ocean dynamics Surface Quasi-Geostrophy sea surface height remote sensing physical oceanography |
description |
This study assesses the capability of Surface Quasi-Geostrophy (SQG) to reconstruct the three-dimensional (3D) dynamics in four critical areas of the Arctic Ocean: the Nordic, Barents, East Siberian, and Beaufort Seas. We first reconstruct the upper ocean dynamics from TOPAZ4 reanalysis of sea surface height (SSH), surface buoyancy (SSB), and surface velocities (SSV) and validate the results with the geostrophic and total TOPAZ4 velocities. The reconstruction of upper ocean dynamics using SSH fields is in high agreement with the geostrophic velocities, with correlation coefficients greater than 0.8 for the upper 400 m. SSH reconstructions outperform surface buoyancy reconstructions, even in places near freshwater inputs from river discharges, melting sea ice, and glaciers. Surface buoyancy fails due to the uncorrelation of SSB and subsurface potential vorticity (PV). Reconstruction from surface currents correlates to the total TOPAZ4 velocities with correlation coefficients greater than 0.6 up to 200 m. In the second part, we apply the SQG approach validated with the reanalysis outputs to satellite-derived sea level anomalies and validate the results against in-situ measurements. Due to lower water column stratification, the SQG approach’s performance is better in fall and winter than in spring and summer. Our results demonstrate that using surface information from SSH or surface velocities, combined with information on the stratification of the water column, it is possible to effectively reconstruct the upper ocean dynamics in the Arctic and Subarctic Seas up to 400 m. Future remote sensing missions in the Arctic Ocean, such as SWOT, Seastar, WaCM, CIMR, and CRISTAL, will produce enhanced SSH and surface velocity observations, allowing SQG schemes to characterize upper ocean 3D mesoscale dynamics up to 400 m with higher resolutions and lower uncertainties. |
format |
Text |
author |
Marta Umbert Eva De-Andrés Rafael Gonçalves-Araujo Marina Gutiérrez Roshin Raj Laurent Bertino Carolina Gabarró Jordi Isern-Fontanet |
author_facet |
Marta Umbert Eva De-Andrés Rafael Gonçalves-Araujo Marina Gutiérrez Roshin Raj Laurent Bertino Carolina Gabarró Jordi Isern-Fontanet |
author_sort |
Marta Umbert |
title |
Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach |
title_short |
Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach |
title_full |
Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach |
title_fullStr |
Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach |
title_full_unstemmed |
Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach |
title_sort |
surface and interior dynamics of arctic seas using surface quasi-geostrophic approach |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15071722 |
op_coverage |
agris |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Sea ice Subarctic |
genre_facet |
Arctic Arctic Ocean Sea ice Subarctic |
op_source |
Remote Sensing; Volume 15; Issue 7; Pages: 1722 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs15071722 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15071722 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
7 |
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1722 |
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1774714274343550976 |