1. Introduction
The development of tropical cyclones (TCs) strongly depends on the air–sea interactions that include heat fluxes and momentum transfer (Emanuel 1991, 1999). The TC’s strong wind and associated wave forcing drives upper-ocean currents that generate vigorous turbulence. Turbulent eddies erode the thermocline by entraining deep cool water into the warmer upper layer, resulting in ocean surface boundary layer (OSBL) deepening and sea surface cooling (Price 1981). In turn, sea surface cooling reduces air–sea heat fluxes that drive the TC, resulting in a negative feedback between TC winds and sea surface temperature (Bender and Ginis 2000; Ginis 2002). The inertial resonance between the turning wind stress and surface currents is also a critical dynamical process under TCs because it increases the shear at the mixed layer base, leading to stronger mixing on the right-hand side of TCs (Price 1981; Skyllingstad et al. 2000; Sanford et al. 2011; Sullivan et al. 2012; Reichl et al. 2016b).
Recent studies indicate that wave-driven Langmuir turbulence (LT) plays an important role in upper-ocean turbulence under TCs (Sullivan et al. 2012; Rabe et al. 2015; Reichl et al. 2016b,a). LT was originally observed in moderate wind conditions as parallel bands of floating material on the sea surface, which are due to strong surface current convergences of horizontal roll vortices in the OSBL (Langmuir 1938). Over the last decades, comprehensive field observations, mostly conducted in moderate wind conditions, have revealed characteristic features of LT, such as strong surface convergence regions, downwelling jets, and the spacing of roll vortices from several meters to kilometers (Thorpe 2004; Weller and Price 1988; Farmer and Li 1995; Plueddemann et al. 1996; Smith 1992; Gargett et al. 2004; Gargett and Grosch 2014). A systematic mathematical theory of LT is based on the wave-averaged Navier–Stokes equation, the so-called Craik–Leibovich (CL) equation, and suggests that LT is driven by the CL vortex force, which is the cross-product of Stokes drift and vorticity vectors (Craik and Leibovich 1976). Physically, the Stokes drift shear tilts vertical vorticity into the direction of wave propagation, generating LT. Today LT is recognized as a fundamental upper-ocean turbulent process (McWilliams et al. 1997; Thorpe 2004; Li et al. 2005; Sullivan and McWilliams 2010; Belcher et al. 2012; D’Asaro 2014) that contributes significantly to turbulent transport and wind- and wave-driven mixed layer deepening (Kukulka et al. 2009, 2010; Grant and Belcher 2011).
Previous investigations of LT involve turbulence-resolving large-eddy simulation (LES) that is based on the filtered CL equations with explicit wave effects, which resolves LT and associated relatively large vortical structures (Skyllingstad and Denbo 1995; McWilliams et al. 1997). LES studies show that LT enhances the vertical fluxes of momentum and heat, inducing stronger vertical velocity variance and mixed layer deepening (McWilliams et al. 1997; Li et al. 2005; Polton and Belcher 2007; Grant and Belcher 2009; Kukulka et al. 2009; Noh et al. 2009). Direct comparisons of LES results with ocean observations reveal that LES captures in detail many of the observed LT characteristics (Skyllingstad et al. 1999; Gargett et al. 2004; Li et al. 2009; Kukulka et al. 2009, 2013; D’Asaro et al. 2014). However, most LES studies are conducted in moderate wind conditions with monochromatic waves, and only a few of them examine LT in extreme TC conditions.
Sullivan et al. (2012) explored LT dynamics under a TC by forcing an LES model with realistic TC winds and waves, which were simulated by a spectral wave model. They contrasted time series of OSBL turbulence statistics at two stations: one on the right-hand side (rhs) with strong inertial resonance and the other one on the left-hand side (lhs) with weak inertial resonance. The intensity of LT strongly depends on location and time because of the TC’s complex wind and wave forcing, yielding more energetic LT on the rhs of the TC. Furthermore, their results indicate that the direction of roll vortices due to LT is aligned with the wind direction and tracks the Lagrangian shear direction. As a result of the TC’s transient forcing conditions, wind vector and wave propagation directions are misaligned, reducing LT intensity.
Motivated by OSBL observations of depth-averaged vertical velocity variance (VVV) obtained from Lagrangian floats under Hurricane Gustav (2008), Rabe et al. (2015) investigated OSBL turbulence with LES experiments forced by Gustav (2008)’s winds and waves. Simulated VVV is only consistent with the observed VVV with LT, that is, for simulations with CL vortex force, indicating LT’s significant role in OSBL dynamics. LES results demonstrate that LT enhances VVV and varies with complex sea states found under TCs. Misaligned wind and wave fields near the TC eye are associated with an observed suppression of VVV, which is also predicted by the LES. Thus, wind-wave misalignment can reduce VVV and suppress LT to the levels close to shear turbulence (ST).
Building on this previous work (Sullivan et al. 2012; Rabe et al. 2015), we recently designed a series of LES experiments for the full spatial TC extent to develop a turbulence closure scheme with explicit sea-state-dependent LT effects (Reichl et al. 2016b) and to investigate the role of sea-state-dependent LT in the OSBL response to TCs (Reichl et al. 2016a). In regional ocean models under TCs, which are commonly based on the Reynolds-averaged Navier–Stokes (RANS) equations, LT cannot be resolved, so that smaller-scale turbulent transport processes in the OSBL have to be parameterized. We modified the K-profile parameterization (KPP) model (Large et al. 1994) to match mean current and temperature profiles obtained from the LES model. In the KPP model, we replaced the Eulerian current with the Lagrangian current (Eulerian current plus Stokes drift) to compute the turbulent momentum flux following McWilliams et al. (2012) and also introduced turbulence enhancement factors following McWilliams and Sullivan (2000). Our new KPP model with explicit sea-state-dependent LT significantly improves estimations of LES temperature and currents compared to results of the standard (unmodified) KPP model (Reichl et al. 2016b). We next introduced this new KPP model in a regional three-dimensional coupled wave–ocean RANS model to demonstrate that sea-state-dependent LT substantially modifies the three-dimensional OSBL response (Reichl et al. 2016a). Results indicate that LT reduces upwelling and horizontal advection as a result of enhanced near-surface mixing and that simulations without sea-state-dependent LT cannot accurately reproduce the sea surface cooling and horizontal transport. More recently, Blair et al. (2017) investigated the upper-ocean response under Hurricane Edouard (2014) with our new KPP model and satellite observations, suggesting the importance of sea-state-dependent LT on the mixed layer depth evolution.
In this study, we use the same LES approach as in Reichl et al. (2016b) to comprehensively investigate LT dynamics, OSBL energetics, and the influence of inertial currents on the OSBL evolution for a wide range of realistic TC conditions. We first review our basic numerical approach and provide an overview of complex wind and wave conditions under TCs (section 2) and then illustrate that the LT-driven OSBL is not only sea-state dependent but also influenced by inertial currents in TC conditions (section 3).
2. OSBL turbulence model
To analyze LT in TC conditions, we use the same wind, wave, and turbulence modeling approaches and datasets as in our previous study (Reichl et al. 2016b), which are briefly reviewed in the following subsections.
a. Wind model
The TC wind field is constructed based on a Holland wind model (Holland 1980, 2008) with the radius of maximum wind (RMW) of 50 km, maximum wind speed at 10-m height of 65 m s−1, and a translation speed of 5 m s−1, which represent typical TC parameters (Reichl et al. 2016b). The output of the wind model is the wind velocity at 10-m height with speed
b. LES model and numerical experiments setups
LES experiments are performed for 18 stations across the TC translation direction along Y from −200 to 200 km, with a minimum and maximum spacing between stations of 20 and 50 km, respectively (Table 1). The coordinate Y is zero at the TC center, and
Locations of 18 stations in LES experiments.
c. Wave simulation
1) Wave model
The third-generation wave model WAVEWATCH III (Tolman 2009) is used to simulate the directional frequency spectra of surface gravity waves in TC conditions following Reichl et al. (2016b). Its computational domain is 3000 km long in the TC’s translation direction and 1800 km wide across the TC’s translation direction. The wave model has a horizontal grid spacing of 8.33 km, and the wave spectrum is discretized into 48 evenly spaced directions and 40 logarithmically spaced frequencies. The simulated wave field is stationary in a coordinate system translating with the TC. The skill of WAVEWATCH III physics parameterizations to simulate complex features of the wave field on the left of the translation direction remains a topic of research (see Hsu et al. 2018). However, comparison of the version of WAVEWATCH III employed for this study with wave observations shows good skill on average to predict the sea state under extreme hurricane conditions (Fan et al. 2009).
The most energetic wave fields with large
2) Wave parameters controlling LT
3. Results
We first investigate the response of the OSBL to tropical cyclones and identify regions of relatively large entrainment of deep cold water into the OSBL for the LT and ST cases (section 3a). Then we investigate different mechanisms that induce greater entrainment in the LT case (section 3b) and the ST case (section 3c).
a. The response of OSBL to tropical cyclones
1) OSBL depth
The cold wake under the TC is caused by upper-ocean mixing accompanied by mixed layer deepening (Price 1981; Sullivan et al. 2012). The mixed layer depth
Because of the highly transient forcing conditions under TCs, it is useful to introduce an OSBL depth characterized by active turbulent mixing. Following previous approaches for atmospheric boundary layer turbulence (Zilitinkevich et al. 2007), we specify a turbulent boundary layer depth
As expected,
Behind the TC eye, we observe active turbulence (
To further examine the effect of TC’s transient forcing on the OSBL depth over the extent of the whole TC domain, we compare
2) Entrainment
To understand how turbulence drives OSBL deepening, we first examine the evolution of profiles of the resolved turbulent buoyancy fluxes
For both the LT and ST cases, the greatest buoyant fluxes occur below
For the transect that is tangential to the RMW, the buoyancy fluxes in the LT case are larger than in the ST case near the maximum wind period (location A), suggesting that LT enhances buoyancy fluxes that contribute to enhanced mixed layer deepening (Fig. 4, top left). However, buoyancy fluxes in the ST case increase behind the TC eye and are about one order larger than in the LT case at
For the transect crossing the TC’s eye region, we observe two periods with strong buoyancy fluxes for both the LT and ST case, which corresponds to the timing of two maximum winds as the TC eye passes (Fig. 4, bottom). The peaks of the buoyancy fluxes in the LT case are greater than in the ST case, indicating LT’s significant role in enhancing the turbulent mixing even under transient winds. Greater buoyancy fluxes in the ST case are not found until
The greatest depth-integrated buoyancy flux occurs around the maximum wind radius where the maximum mixed layer deepening rates are also found (Fig. 5, left). The stronger turbulent buoyancy transport on the rhs agrees with the larger mixed layer deepening on the rhs (Fig. 2, top left). Pronounced differences of
To explore the relation of wind forcing to entrainment, we scale the depth-integrated buoyancy fluxes
In the LT case, differences are due to LT that is more vigorous on the rhs due to stronger wave forcing and weaker on the lhs due to wind-wave misalignment (refer to Fig. 1). To examine the influence of LT on entrainment under the TC’s complicated wind and wave forcing, we consider a similar scaling approach as Grant and Belcher (2009) but replace
b. Enhanced entrainment due to LT
For aligned wind-wave conditions and relatively shallow mixed layers, LT enhances turbulent entrainment through a three-step process: first the large coherent structures in LT facilitate the transport of momentum through the boundary layer; then the shear-instability near the mixed layer base is locally enhanced, inducing greater erosion of thermocline; finally, the eroded colder water is transported upward and mixed by LT near the surface (Kukulka et al. 2010). In this section, we evaluate this process step by step in tropical cyclone conditions by examining the flow field at a location where enhanced mixed layer deepening and greater buoyancy fluxes due to LT are found (marked with A in the bottom of Fig. 2).
1) Current and temperature profiles
To address the enhanced shear instability by LT, we first examine the horizontally averaged Lagrangian current and temperature profiles at a location A (Fig. 7, top). The Lagrangian current is the sum of the Eulerian current and Stokes drift. To better illustrate the relation between wind and current directions, we project the horizontal Eulerian currents and Stokes drift into the along-wind and crosswind direction denoted by subscripts
At location A, both the LT and the ST cases show predominant along-wind Eulerian currents compared to the crosswind currents. This is because the onset of the TC’s maximum wind mainly accelerates currents and the inertial currents have not fully spun up yet. With LT, the vertical profile of
2) Velocity variance and turbulent anisotropy
To further address the locally enhanced shear instability by LT, we examine the velocity variance profile whose anisotropy and magnitude characterize the turbulence’s type and intensity, respectively.
At location A with LT, the normalized
3) TKE budget
At location A with LT, the Stokes drift shear production is dominant down to
4) Scaling of LT in misaligned wind and wave conditions
c. Enhanced entrainment due to energetic inertial currents
To understand the influence of inertial currents on turbulence dynamics, we examine flow properties at location B that is 2.5 RMW behind the TC eye where the ST case shows more energetic buoyancy fluxes and greater mixed layer deepening (Fig. 5, right).
1) Current and temperature profiles
Unlike at location A, the magnitude of
At location B, the temperature differences between the ST and LT cases are smaller compared to location A (Fig. 7, middle) so that buoyancy fluxes for the ST case exceed those of the LT case in regions behind the TC eye (Fig. 5, right). Consistently, Ri profiles are similar for both cases in the shear layer with
2) Velocity variance
At location B with LT, the profiles of
3) TKE budgets
As for location A, the leading-order budget terms at location B are TKE production and dissipation (Fig. 10, bottom). Unlike location A, the normalized TKE production is now substantially enhanced for the ST case at greater depth, so that the relative magnitude of TKE production terms evolve over time (Fig. 11, bottom right). Near the surface, TKE production remains relatively large because of strong Lagrangian near-surface shear for both ST and LT cases. In the LT case, the total TKE production
LT plays two important roles in reducing TKE production at location B. First, Eulerian currents are uniform within
4) Total energy budgets and inertial resonance
Throughout the passage of the storm,
With LT, the total energy budget dynamics changes at about
For
4. Conclusions
Based on the analysis of high-resolution large-eddy simulation (LES) results, we have investigated the response of the ocean surface boundary layer (OSBL) with and without Langmuir turbulence (LT) to complex wind and wave forcing in tropical cyclone (TC) conditions. The Stokes drift vector that drives LT through the Craik–Leibovich vortex force is determined from spectral wave simulations obtained from the WAVEWATCH III model. Both the LES and the wave models are forced by winds from the TC Holland wind model. LES experiments are performed with and without LT (i.e., without Stokes drift) and for multiple, densely spaced stations across the TC translation direction, covering the full spatial extent of the TC.
The examination of OSBL depth and entrainment illustrates that LT substantially enhances entrainment with greater buoyancy fluxes, inducing rapid OSBL deepening during maximum TC winds. The mechanism that drives rapid OSBL deepening is that LT facilitates the downward momentum transport, locally enhancing shear instabilities near the mixed layer base, which occurs only when OSBL depth
After the TC passes,
Acknowledgments
We acknowledge the support of NSF Grants OCE-1130678, OCE-1634578, and OCE-1352422 for funding this work. We also thank the high-performance computing resources as well as information technologies support from NCAR large computation allocation grant, sponsored by National Science Foundation and University of Delaware’s high-performance computers Mills and Farber. Two anonymous reviewers provided helpful suggestions that have substantially improved the manuscript.
APPENDIX
Comparison of and
By comparing Eq. (A4) to Eq. (A3), we show that
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