A Finite-Time Ensemble Method for Mixed Layer Model Comparison

Leah Johnson*, Baylor Fox-Kemper, Qing Li, Hieu T. Pham, Sutanu Sarkar

*Corresponding author for this work

Research output: Contribution to journalJournal Articlepeer-review

7 Citations (Scopus)

Abstract

This work evaluates the fidelity of various upper-ocean turbulence parameterizations subject to realistic monsoon forcing and presents a finite-time ensemble vector (EV) method to better manage the design and numerical principles of these parameterizations. The EV method emphasizes the dynamics of a turbulence closure multimodel ensemble and is applied to evaluate 10 different ocean surface boundary layer (OSBL) parameterizations within a single-column (SC) model against two boundary layer large-eddy simulations (LES). Both LES include realistic surface forcing, but one includes wind-driven shear turbulence only, while the other includes additional Stokes forcing through the wave-average equations that generate Langmuir turbulence. The finite-time EV framework focuses on what constitutes the local behavior of the mixed layer dynamical system and isolates the forcing and ocean state conditions where turbulence parameterizations most disagree. Identifying disagreement provides the potential to evaluate SC models comparatively against the LES. Observations collected during the 2018 monsoon onset in the Bay of Bengal provide a case study to evaluate models under realistic and variable forcing conditions. The case study results highlight two regimes where models disagree 1) during wind-driven deepening of the mixed layer and 2) under strong diurnal forcing.

Original languageEnglish
Pages (from-to)2211-2230
Number of pages20
JournalJournal of Physical Oceanography
Volume53
Issue number9
DOIs
Publication statusPublished - 1 Sept 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 American Meteorological Society.

Keywords

  • Boundary layer
  • Langmuir circulation
  • Mixed layer
  • Monsoons
  • Numerical analysis/modeling
  • Ocean
  • Single column models
  • Subgrid-scale processes

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