Scale-aware deterministic and stochastic parametrizations of eddy-mean flow interaction

Laure Zanna, Pier Gian Luca Porta Mana, James Anstey, Tomos David, Thomas Bolton

Research output: Contribution to journalArticlepeer-review

Abstract

The role of mesoscale eddies is crucial for the ocean circulation and its energy budget. The sub-grid scale eddy variability needs to be parametrized in ocean models, even at so-called eddy permitting resolutions. Porta Mana and Zanna (2014) propose an eddy parametrization based on a non-Newtonian stress which depends on the partially resolved scales and their variability. In the present study, we test two versions of the parametrization, one deterministic and one stochastic, at coarse and eddy-permitting resolutions in a double gyre quasi-geostrophic model. The parametrization leads to drastic improvements in the mean state and variability of the ocean state, namely in the jet rectification and the kinetic-energy spectra as a function of wavenumber and frequency for eddy permitting models. The parametrization also appears to have a stabilizing effect on the model, especially the stochastic version. The parametrization possesses attractive features for implementation in global models: very little computational cost, it is flow aware and uses the properties of the underlying flow. The deterministic coefficient is scale-aware, while the stochastic parameter is scale- and flow-aware with dependence on resolution, stratification and wind forcing.

Original languageEnglish (US)
Pages (from-to)66-80
Number of pages15
JournalOcean Modelling
Volume111
DOIs
StatePublished - Mar 1 2017

Keywords

  • Eddy-permitting models
  • Energy backscatter
  • Mesoscale eddies
  • Stochastic parametrization
  • Upgradient fluxes

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Oceanography
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science

Fingerprint Dive into the research topics of 'Scale-aware deterministic and stochastic parametrizations of eddy-mean flow interaction'. Together they form a unique fingerprint.

Cite this