Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach

Author:

Umbert Marta12ORCID,De-Andrés Eva13ORCID,Gonçalves-Araujo Rafael4ORCID,Gutiérrez Marina5,Raj Roshin6,Bertino Laurent6,Gabarró Carolina12ORCID,Isern-Fontanet Jordi12ORCID

Affiliation:

1. Department of Physical and Technological Oceanography, Institut de Ciències del Mar, CSIC, 08003 Barcelona, Spain

2. Barcelona Expert Center on Remote Sensing, CSIC-UPC, 08003 Barcelona, Spain

3. Department of Applied Mathematics, Universidad Politécnica de Madrid, 28040 Madrid, Spain

4. National Institute of Aquatic Resources, Technical University of Denmark, DTU Aqua, 2800 Lyngby, Denmark

5. Departamento de Física, Universidad Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain

6. Nansen Environmental and Remote Sensing Center (NERSC) and Bjerknes Center for Climate Research, 5007 Bergen, Norway

Abstract

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.

Funder

H2020 Marie Skłodowska-Curie Actions

Margarita Salas Fellowship

AEI

Spanish government

ESA Arctic + Salinity Project

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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