Time Scales of Submesoscale Flow Inferred from a Mooring Array

Abstract

While the distribution of kinetic energy across spatial scales in the submesoscale range (1–100 km) has been estimated from observations, the associated time scales are largely unconstrained. These time scales can provide important insight into the dynamics of submesoscale turbulence because they help quantify to what degree the flow is subinertial and thus constrained by Earth’s rotation. Here a mooring array is used to estimate these time scales in the northeast Atlantic. Frequency-resolved structure functions indicate that energetic wintertime submesoscale turbulence at spatial scales around 10 km evolves on time scales of about 1 day. While these time scales are comparable to the inertial period, the observed flow also displays characteristics of subinertial flow that is geostrophically balanced to leading order. An approximate Helmholtz decomposition shows the order 10-km flow to be dominated by its rotational component, and the root-mean-square Rossby number at these scales is estimated to be 0.3. This rotational dominance and Rossby numbers below one persist down to 2.6 km, the smallest spatial scale accessible by the mooring array, despite substantially superinertial Eulerian evolution. This indicates that the Lagrangian evolution of submesoscale turbulence is slower than the Eulerian time scale estimated from the moorings. The observations therefore suggest that, on average, submesoscale turbulence largely follows subinertial dynamics in the 1–100-km range, even if Doppler shifting produces superinertial Eulerian evolution. Ageostrophic motions become increasingly important for the evolution of submesoscale turbulence as the scale is reduced—the root-mean-square Rossby number reaches 0.5 at a spatial scale of 2.6 km.

Document Details

Document Type
Pub Defense Publication
Publication Date
Apr 01, 2020
Source ID
10.1175/jpo-d-19-0254.1

Entities

People

  • Alberto Naveira Garabato
  • Jörn Callies
  • Roy Barkan

Organizations

  • California Institute of Technology
  • Division of Ocean Sciences
  • Israel Science Foundation
  • Natural Environment Research Council
  • Office of Naval Research Global
  • Tel Aviv University
  • University of Southampton

Tags

Fields of Study

  • Environmental science

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Atmospheric Science/Meteorology
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms