Lagrangian transport across the upper Arctic waters in the Canada Basin

Abstract

The goal of this paper is to study transport, from a Lagrangian perspective, across selected circulation patterns in the upper Arctic Ocean waters. To this end, we apply the methodology of Lagrangian descriptors, using the function M, to the velocity field dataset provided by the Copernicus Marine Environment Monitoring Service. We focus our analysis on the Arctic region in the halocline (top 30 m depth), which is based on particular events occurring over the 2012–2016 time period. The advantage of the Lagrangian descriptor is that it highlights large‐scale persistent dynamical structures relating to mathematical objects known as invariant manifolds, which determine fluid transport and mixing processes. These geometric flow structures play a crucial role in the evolution of the salinity content observed over the Arctic Basin. In this work, we identify these dynamical structures in the Beaufort Sea and show how they mediate transport processes according to a clockwise circulating pattern related to the Beaufort Gyre. Additionally, this approach highlights the importance of the Transpolar Drift Stream (TDS) as a transport barrier which maintains the salinity gradient between the Canada Basin and the Atlantic waters. Our approach also illustrates the variability of the intensity of the TDS during the analysed period and identifies secondary currents that feed it.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 06, 2018
Source ID
10.1002/qj.3404

Entities

People

  • Ana María Mancho
  • Francisco Balibrea‐iniesta
  • Jiping Xie
  • Laurent Bertino
  • Stephen Wiggins
  • Víctor J. García‐garrido

Organizations

  • Autonomous University of Madrid
  • Ministry of Economy, Industry and Competitiveness
  • Nansen Environmental and Remote Sensing Center
  • Office of Naval Research
  • University of Alcalá
  • University of Bristol

Tags

Fields of Study

  • Environmental science

Readers

  • Coastal Oceanography
  • Neural Network Machine Learning.
  • Polar and Arctic Studies