The effect of Langmuir turbulence under complex real oceanic and meteorological forcing
Introduction
Langmuir turbulence (LT) is believed to be one of the leading order causes of turbulent mixing in the upper ocean, which is important for momentum and heat exchange across the air–sea interface and between the mixed layer and the thermocline. Both observational studies (D’Asaro, 2001, D’Asaro, 2014) and large-eddy simulation (LES) investigations (Li et al., 1995, Kukulka et al., 2009, Kukulka et al., 2010, Skylingstad and Denbo, 1995, McWilliams et al., 1997, Hamlington et al., 2014) have shown enhanced vertical mixing within the ocean surface boundary layer in the presence of LT through the enhanced vertical turbulent velocity variance.
The dynamical origin of Langmuir circulation is understood as wind-driven shear instability in combination with surface wave influences related to their mean Lagrangian motion, called Stokes drift. The prevailing theoretical interpretation of Langmuir circulation is derived by Craik and Leibovich (1976), where they introduced the effect of waves on Eulerian mean flow into the Navier–Stokes equations. Even though the theory was developed four decades ago, scientists were not able to adequately measure or simulate LT until thirty years ago. While LES models have been used to simulate Langmuir circulation in the upper ocean, yielding new insights that could not be obtained from field observations or turbulent closure models, most of these studies were conducted under idealized conditions with simplified oceanic and wind conditions (Skylingstad and Denbo, 1995, McWilliams et al., 1997, McWilliams et al., 2012, Sullivan et al., 2012, Hamlington et al., 2014, Reichl et al., 2016). Idealizing and isolating individual processes makes it easier to study their effects, but can also unrealistically magnify or underestimate their impact, due to the lack of complex and nonlinear interactions of multiple dynamical processes taking place in the real ocean. Thus, parameterizations that have developed from these idealized studies can have limited practical application in ocean modeling. For example, evaluation of three of the K profile parameterizations (KPP, developed by Large et al., 1994) with LT modifications (McWilliams and Sullivan, 2000, Smyth et al., 2002, Qiao et al., 2004) in the GFDL climate model have shown that none of the schemes give consistent improvement to ocean circulation models globally most likely due to their lack of interaction with ocean physics (Fan and Griffies, 2014). While in Li et al. (2016) and Li and Fox-Kemper (2017), substantial improvements are observed when more physical processes are considered in the scaling, such as Harcourt and D’Asaro (2008) and Van Roekel et al. (2012).
In this study, we expand the previous LES modeling investigations of LT to real ocean conditions. Model forcing and initial conditions are obtained from a multi-platform field campaign, “Coupled Air–Sea Processes and Electromagnetic (EM) ducting Research (CASPER-East)” that took place off the coast of North Carolina in late October to early November of 2015. The study location, approximately 63 km east of Duct, N.C., is frequently influenced by fresher and cooler water inflow from nearby rivers and bays and warmer and saltier water intrusion from the Gulf stream, and experienced several cooling events and dramatic turning of wind directions due to storm passage during the observation period. Temperature (T) and salinity (S) profiles, surface gravity wave spectra, and meteorological forcing data were collected during the CASPER-East campaign, providing a rich data set to study the effect of LT on the dynamics and structure of the oceanic mixed layer under complex oceanic and meteorological conditions. The outline of this paper is as follows. A brief description of the observations during the CASPER-East experiment, the LES model used for this study, and the experiment set up are given in Section 2. Results are analyzed in Section 3, and discussion and concluding remarks are presented in Section 4.
Section snippets
Observations
The field data used in this research was collected under the CASPER project aimed to improve the characterization of the propagation of radio frequency signals through the marine atmosphere (Wang et al., 2018a). CASPER-East, the first of two major field campaigns during the CASPER project, was conducted from October 10 to November 6 of 2015 off the coast of North Carolina, eastward from the US Army Corps of Engineers Field Research Facility pier at Duck.
Atmospheric and oceanic measurements used
The Turbulent Kinetic Energy (TKE) budget
The turbulent kinetic energy (TKE) budget is usually analyzed in previous studies (Grant and Belcher, 2009, McWilliams et al., 2012, Van Roekel et al., 2012) to examine the effect of LT on the mixing. The horizontal domain averaged TKE equation can be written as: where,
Summary and discussions
Langmuir turbulence (LT) is believed to be one of the leading causes of turbulent mixing in the upper ocean (Li et al., 1995, Skylingstad and Denbo, 1995, Kukulka et al., 2009, Kukulka et al., 2010, McWilliams et al., 1997, Hamlington et al., 2014). Large eddy simulation (LES) models that solve the Craik–Leibovich equations are used to study LT, yielding new insights that could not be obtained from field observations or turbulent closure models alone. However, these studies have been mostly
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors thank Dr. Adam Christman for providing the R/V air–seaflux data for this study. This work was funded by the Office of Naval Research, United States of America under program element 0601153N. This paper is a contribution of NRL/JA/7320-19-4508 and has been approved for public release. We would like to express our appreciation to the anonymous reviewers for their constructive comments. Observations presented in the manuscript are open-access data. Please contact Dr. Ivan Savelyev
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