Abstract:

The effects of Langmuir mixing on the surface ocean mixing may be parameterized by applying an enhancement factor which depends on wave, wind, and ocean state to the turbulent velocity scale in the K-Profile Parameterization (KPP). Diagnosing the appropriate enhancement factor online in global climate simulations is readily achieved by coupling with a prognostic wave model, but with significant computational and code development expenses. This dataset is the monthly mean enhancement factor climatology derived from an ocean-wave coupled simulation, which can be directly applied to KPP to represent the Langmuir mixing effect without coupling with a prognostic wave model. Two formats of the data set are provided: the Fortran binary format is for direct test in the CVMix framework and the netCDF format is for ease of incorporation and manipulation in more general framework. The paper describing this work in detail is: Li, Q., Fox-Kemper, B., Breivik, O., & Webb, A. (2017). Statistical models of global Langmuir mixing. Ocean Modelling, 113:95-114. doi:10.1016/j.ocemod.2017.03.016.

Suggested Citation:

Qing Li, Baylor Fox-Kemper. 2017. Dataset for: Statistical models of global Langmuir mixing. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7GX4909

Publications:

Li, Q., Fox-Kemper, B., Breivik, Ø., & Webb, A. (2017). Statistical models of global Langmuir mixing. Ocean Modelling, 113, 95–114. doi:10.1016/j.ocemod.2017.03.016

Purpose:

This data is useful for enhancing the effect of Langmuir (wave-driven) mixing over wind-only mixing. It allows estimation of this effect without running a wave model, based on the climatological state of waves' effects geographically-distributed throughout the year.

Data Parameters and Units:

latitude (degrees), longitude (degrees), time (climatological month), enhancement factor (dimensionless)

Individual Files: