Dynamics of turbulence production by attenuating interfacial gravity waves observed in air–water coupled wave-resolving simulation

Abstract

Even without breaking or wind influence, ocean surface waves are observed to produce turbulence in the water, possibly influencing ocean surface dynamics and air–sea interactions. Based on the water-side free-surface simulations, recent studies suggest that such turbulence is produced through the interaction between the waves and the near-surface Eulerian current associated with the viscous attenuation of waves. To clarify the dynamical role of the air–water interface in the turbulence production, the attenuating interfacial gravity waves were simulated directly using a newly developed two-phase wave-resolving numerical model. The air–water coupling enhanced the wave energy dissipation through the formation of a strong shear at the air-side viscous boundary layer. This led to an enhancement of the wave-to-current momentum transfer and the formation of the down-wave Eulerian mean sheared current, which is favourable for the CL2 instability responsible for the production of Langmuir circulations. As a result, the water-side turbulence grew stronger compared with the corresponding free surface (water-only) wave-resolving simulation. The evolution of the wave-averaged field was well reproduced with the Craik–Leibovich equation with the upper boundary condition provided with the virtual wave stress based on linear theory. The wave energy dissipation by air–water coupling plays a significant role in the quantitative understanding of the wave-induced turbulence at the laboratory and field scales.

1. Introduction

Ocean surface waves induce surface turbulent mixing and moderate the exchange of heat, materials and momentum between the atmosphere and the ocean (Sullivan & McWilliams 2010). Waves can produce turbulence even without breaking, a representative process being Langmuir circulations (LCs) (Langmuir 1938). LCs are roll-shaped flow structures aligned with wave direction, often turbulent, and visualised through the streaks of flotsam accumulated on the convergence zones. They are considered to arise through the instability of a vertically sheared current under the influence of the wave-associated Stokes drift. Its dynamics are described in a wave-averaged framework called the Craik–Leibovich (CL) equation (Craik & Leibovich 1976), and the instability is called the CL2 mechanism (Leibovich 1983). The instability condition requires that the Eulerian current shear ${\partial \bar {u}^{E}}/{\partial z }$ and the Stokes drift shear ${\partial u ^{St}}/{\partial z }$ have the same sign. Here, $\bar {u}^{E}$ is wave-averaged Eulerian current, $u^{{\textit{St}}}$ is the Stokes drift and $z$ is the vertical coordinate with positive upwards. This sheared Eulerian current is commonly considered to originate from wind stress, which is aligned with the Stokes drift direction in most cases. Belcher et al. (2012) estimated the LCs’ contributions to the ocean surface mixing based on the CL theory and suggested that the Langmuir mixing plays a major role in many parts of the ocean.

Although the turbulence production by the CL2 mechanism requires the influence of wind, some studies report that waves produce turbulence even without wind, a phenomenon often termed ‘non-breaking wave-induced turbulence’. To distinguish from LCs, it is hereinafter called ‘windless’ (WL) turbulence in this article. Multiple laboratory studies support the presence of WL turbulence, demonstrating turbulence growth under waves generated by a wavemaker (Babanin & Haus 2009; Dai et al. 2010; Savelyev, Maxeiner & Chalikov 2012). The mechanism for this phenomenon was originally proposed to be the turbulence transition of the wave orbital motion (Babanin 2006), and parameterisations were developed for large-scale ocean models based on this idea (Pleskachevsky et al. 2011). Wu, Rutgersson & Sahlée (2015) estimated the contribution of WL turbulence to the ocean surface mixing based on this parameterisation and concluded that it has a major influence on the total turbulence production. However, the validation of the WL turbulence production mechanism has been limited in both theoretical and experimental aspects, leading to questions raised against the validity of the turbulence transition hypothesis (e.g. D'Asaro 2014). Furthermore, Villas Bôas et al. (2019) points out the unclarity in the distinction between WL turbulence and LCs. Clarifying the WL turbulence production mechanism is crucial for developing a parameterisation scheme widely accepted by the modelling community and integrating the two phenomena branches to accurately model wave-induced mixing.

The advancement of numerical modelling and computational resources have enabled us to directly solve the Navier–Stokes equation in free-surface configurations. In such numerical studies, we can strictly control the wind and wave conditions and analyse the three-dimensional structure of turbulence, both of which are difficult in laboratory and field measurements. Tsai et al. (2013) studied the water turbulence under wind- and wave-forced conditions and its contribution to gas exchange across the water surface, using the fully nonlinear free surface model described in Tsai & Hung (2007). A similar situation is also studied by Tsai & Lu (2023) with an even more sophisticated analysis of the multiscale vortex structures in the water. Yang & Shen (2011a) similarly developed a fully nonlinear free-surface model, and in the accompanying paper (Yang & Shen 2011b) they proposed a method to dynamically couple multiple domains to simulate two-phase flow with a deformable interface. Xuan & Shen (2019) proposed an improved scheme for the water-side simulation using flux-form formulation. Guo & Shen (2013, 2014) studied the vorticity kinematics and the turbulence dynamics of turbulence produced by external forcing in the subsurface layer and its interaction with the surface wave motions. Xuan, Deng & Shen (2019) also conducted a detailed dynamical analysis of Langmuir turbulence driven by the surface shear stress and wave motions. Wang & Özgökmen (2018), Fujiwara, Yoshikawa & Matsumura (2018) and Fujiwara & Yoshikawa (2020) compared the wave-resolving simulation results with the CL equation to study the dynamics of LCs.

Among such studies, Tsai et al. (2017) and Fujiwara, Yoshikawa & Matsumura (2020) have provided new insights into the connection between the mechanisms of the WL turbulence and the LCs. These studies suggest that the near-surface shear flow, known as the Eulerian mean drift, plays a central role in turbulence production. Longuet-Higgins (1953) considered the mass transport (Lagrangian) velocity of the waves under an influence of weak viscosity and demonstrated that the vertical shear of the mass transport velocity near the surface would be twice as much as the Stokes drift shear: ${\partial (\bar {u}^{E}+u^{{\textit {St}}})}/{\partial z }=2{\partial u ^{{\textit {St}}}}/{\partial z }$. This Eulerian shear formation is understood through the momentum transfer from the surface waves attenuating due to water viscosity. Consider a small-amplitude, monochromatic deep-water wave with gravitational acceleration $g$, water density $\rho _{o}$, water kinematic viscosity $\nu _{o}$, amplitude $a(t)$, wavenumber $k$ and angular frequency $\sigma$, where the viscous influence is sufficiently small that $\sigma =(gk)^{1/2}$ and $a^{-1}{\textrm {d} a}/{\textrm {d} t}\ll \sigma$. Lamb (1932) showed that the amplitude attenuation rate $\gamma \equiv -a^{-1}{\textrm {d} a}/{\textrm {d} t}$ for such a wave would be

(1.1)\begin{equation} \gamma_{o} = 2\nu_{o} k^2 = \frac{2}{{\textit{Re}}_{o}}\sigma, \end{equation}

where $ {\textit {Re}}_{o}\equiv \sigma k^{-2}/\nu _{o}$ is the water-side Reynolds number based on wavenumber and wave phase speed. The potential motion of the wave conveys horizontal momentum of $M_{o}\equiv \rho _{o} g \sigma a^2/2$ per unit horizontal area. When the waves attenuate following (1.1), $-{\textrm {d} M_{o}}/{\textrm {d} t}$ per unit area must be transformed to the Eulerian current. This can be only achieved via the viscous diffusion from the surface boundary layer. Therefore, the following amount of horizontal momentum is received by the Eulerian current (e.g. Phillips 1966):

(1.2)\begin{equation} \tau^{vws}_{o}=\rho_{o}\nu_{o}\dfrac{\partial\bar{u}^{E}}{\partial{z}}= 2\gamma_{o}\times \frac{1}{2}\rho_{o}\sigma a^2=\frac{2}{{\textit{Re}}_{o}}\rho_{o} a^2 \sigma^2. \end{equation}

This viscous momentum flux was called ‘virtual wave stress’ by Longuet-Higgins (1969), providing a boundary condition for $\bar {u}^{E}$ as ${\partial \bar {u}^{E}}/{\partial z }={\partial u ^{{\textit {St}}}}/{\partial z }$.

The importance of the Eulerian mean drift was elucidated by the wave-resolving direct numerical simulations (DNS). Tsai, Chen & Lu (2015); Tsai et al. (2017) and Fujiwara et al. (2020) used free-surface models that explicitly solve the deformable surface motion under the fully nonlinear boundary conditions, considering that the surface does not overturn. They all reproduced elongated streamwise vortices as was observed in the tank experiment by Savelyev et al. (2012). First, Tsai et al. (2015) conducted a simulation of propagating surface waves over a turbulence field and observed a significant growth of initial turbulence. The resulting flow field showed vortical structures with a growth rate consistent with the model of Teixeira & Belcher (2002) based on the rapid distortion theory. Tsai et al. (2017) further investigated the streamwise vortices with an increased number of simulations and compared their structure with the linear stability analysis of the CL equation. When the Eulerian current shear is provided by the theory of Longuet-Higgins (1953), the theoretically predicted spanwise wavenumber of the fastest growth mode was close to that of the simulated streamwise vortices. Then, Fujiwara et al. (2020) compared a low-Reynolds number wave-resolving DNS and its wave-averaged counterpart using the CL equation with and without virtual wave stress effect. As a result, the temporal evolution of vertical circulation in wave-resolving DNS was very well-reproduced with the CL equation using the virtual wave stress effect. The results of Tsai et al. (2017) and Fujiwara et al. (2020) suggested that the simulated WL turbulence was driven by the CL2 mechanism associated with the virtual wave stress-driven sheared current. The latest numerical study by Imamura, Yoshikawa & Fujiwara (n.d.) observed that such vortical flows actually induce turbulent mixing. Through a detailed enstrophy budget analysis, they demonstrated that the turbulence was not locally produced from the orbital motion but non-locally through the sheared current near the surface.

These studies considered the problem in the water-side-only framework, but the water surface is in contact with air in reality. In the water-side-only problem, the viscous energy dissipation occurs over the bulk of water volume (Phillips 1966), and the momentum lost from the wave is received by the vortical current of water. In the presence of the air above, the two fluids can exchange momentum and energy, which can make a difference in the resulting flow fields. Thus, it is important to understand the roles played by the air–water coupling in the attenuating interfacial gravity waves and associated wave-induced turbulence. For this purpose, the wave-resolving numerical simulation is a promising approach because it enables us to evaluate the dynamical interaction between the two phases in detail, which is extremely difficult in laboratory and field measurements. Because the exchange of momentum and energy between water and air is central in this phenomenon, the simulations need to be conducted in air–water coupled configurations, rather than air-side only simulations (e.g. Sullivan, McWilliams & Moeng 2000; Sullivan et al. 2008; Cao & Shen 2021), where the water surface serves as the infinite reservoir of energy.

The numerical study of air–water two-phase flow has a long history, with diverse topics of interest and numerical techniques to represent this complex system. For example, the main interest of studies using the interface-following coordinate (as in the present study) includes wind wave generation (Fulgosi et al. 2003; Lin et al. 2008; Zonta, Soldati & Onorato 2015; Li & Shen 2022), transient adjustment of waves to wind in high-wave-age (Zonta, Onorato & Soldati 2016), and the wind turbulence over waves and the evolution of wave spectrum under wind influence (Hao & Shen 2019). Nevertheless, wind is present in all of these simulations, which makes it difficult to separately discuss the role of air–water coupling in WL turbulence production.

One important theoretical prediction is that the Eulerian mean current is intensified in the presence of air if its mean horizontal motion (mean wind) is zero. Dore (1978) studied the linear theory of interfacial gravity waves under the influence of weak viscosity in both air and water. The horizontal orbital velocity of the irrotational waves is discontinuous at the interface, so the viscous boundary layer (Stokes layer) develops on both sides to match the air- and water-side velocity. Due to the large density ratio, a very sharp shear layer arises in the air side, where a strong energy dissipation occurs. As a result, the leading-order amplitude decay rate is expressed as the sum of the dissipation in the bulk of the water and in the Stokes layer of the air. The result of Dore (1978) can be rewritten for deep-water interfacial waves as follows:

(1.3)\begin{equation} \gamma_{ao} = 2\nu_{o} k^2+\frac{\rho_{a}}{\rho_{o}}(2\nu_{a} k^2\sigma)^{1/2} = \left[\frac{2}{{\textit{Re}}_{o}}+\frac{\rho_{a}}{\rho_{o}}\left(\frac{2}{{\textit{Re}}_{a}}\right)^{1/2}\right]\sigma, \end{equation}

where $\rho _{a}$ is the density of air, $\nu _{a}$ is the kinematic viscosity of air and $ {\textit {Re}}_{a}\equiv \sigma k^{-2}/\nu _{a}$ is the air-side Reynolds number based on wavenumber and wave phase speed. The relative importance of the two terms depends on scale. Based on physical parameters at $10\,^\circ {\rm C}$, $\nu _{a}=1.4\times 10^{-5}\,{\rm m}^2\,{\rm s}^{-1}$, $\nu _{o}=1.3\times 10^{-6}\,{\rm m}^2\,{\rm s}^{-1}$, $\rho _{a}=1.2\,{\rm kg}\,{\rm m}^{-3}$, and $\rho _{o}=1.0\times 10^3\,{\rm kg}\,{\rm m}^{-3}$ for reference, and using the free surface dispersion relation $\sigma =(gk)^{1/2}$, the ratio of the second to the first term is 0.46, 2.56 and 14.4 for wavelengths of $\lambda =0.3, 3, 30$ m, respectively. For waves longer than $\lambda \approx 0.9$ m, the effect of viscous dissipation in the air dominates over the dissipation in the water. Dore (1978) further investigated the second-order stress at just outside the viscous boundary layer; the results are consistent with the following Eulerian form:

(1.4)\begin{align} \rho_{o}\nu_{o}\dfrac{\partial\bar{u}^{E}}{\partial{z}}-\rho_{a}\nu_{a}\dfrac{\partial\bar{u}^{E}}{\partial{z}}=\tau^{vws}_{ao}= 2\gamma_{ao}\times \frac{1}{2}\rho_{o}\sigma a^2=\left[\frac{2}{{\textit{Re}}_{o}}\rho_{o} + \left(\frac{2}{{\textit{Re}}_{a}}\right)^{1/2}\rho_{a}\right]a^2\sigma^2. \end{align}

Since $\rho _{o}\nu _{o}\gg \rho _{a}\nu _{a}$, the virtual wave stress is mostly received by the water side term on the left-hand side. Therefore, the theory predicts that the wave momentum is transferred to water with a rate higher than that of the water-only case, possibly leading to stronger WL turbulence production than the free surface simulations.

In this study, the viscous attenuation of air–water interfacial gravity waves and associated WL turbulence production were investigated using two-phase wave-resolving DNS. We aimed to elucidate the effect of air–water coupling on the Eulerian mean current and WL turbulence generation by comparing wave-resolving simulations with and without coupling. Here, the mean airflow is assumed to be zero to clarify the comparison against the water-only simulation, because the wind–wave interaction (e.g. Miles 1957) would complicate the process. However, the knowledge about the boundary layer structure and its role in air–water coupling should still be valid even in the presence of the mean wind. We also investigated the reproducibility of the phenomena in the wave-averaged framework using the wave-averaged simulations incorporating virtual wave stress as the boundary condition. We first extend the wave-resolving numerical model developed by Fujiwara et al. (2020) to the two-phase configuration. Its numerical procedure is described in § 2. Then the problem setting for the simulation of attenuating interfacial waves are introduced in § 3, and its results are presented in § 4. Finally, a discussion and conclusions are provided in § 5.

2. Problem settings and numerical scheme

2.1. Framework

Here, we describe the numerical scheme of the two-phase flow solver. Various approaches have been used to simulate the air–water interface problems, such as the marker-and-cell method (Harlow & Welch 1965), the volume-of-fluid method (Popinet 2003), the level-set method (Sussman et al. 1999), the smoothed particle hydrodynamics (Colagrossi & Landrini 2003), the direct method using the surface-following coordinate and interface tracking (Komori et al. 2010; Yang & Shen 2011b) and modified or hybrid versions of these algorithms. In the present problem of non-breaking waves, we employ the surface-following coordinate approach, which can retain the sharpness of the interface and easily cluster the grid points to resolve the thin boundary layer. This approach also has an advantage in the conservation of mass, momentum and energy with a moderate computational cost, if a proper numerical method is employed. Here we assume that the air–water interface would not turn over and the interface can be represented with a one-valued function of horizontal position and time. This assumption allows us to prescribe a simple mapping from the physical to the computational domain (vertical coordinate transformation) and to avoid the regridding in each time step. Such a strategy has been adopted for simulating free-surface water-side flows (Tsai & Hung 2007; Yang & Shen 2011a; Xuan & Shen 2019), the air-side flows (Sullivan et al. 2000, 2008) and the air–water coupled flows (Yang & Shen 2011b). The numerical scheme is formulated as an extension of the framework of the free-surface (water-side) model of Fujiwara et al. (2020).

Consider a three-dimensional rectangular domain, where the horizontal boundaries are doubly periodic, and the top and bottom boundaries are rigid walls. The Cartesian coordinates are denoted by $x, y$ (horizontal) and $z$ (vertical). The top and bottom boundaries are denoted by $z=H_{a}$ and $z=-H_{o}$, respectively. Here, for simplicity, we assume that the top and bottom walls are flat, but the numerical method described in the following can be easily extended to spatially varying $H_{a}$ and $H_{o}$.

The domain is filled with two incompressible fluids with different densities, which are hereinafter called ‘air’ and ‘water’. The densities of each fluid are $\rho _{a}$ (air) and $\rho _{o}$ (water), and $\rho _{a} < \rho _{o}$. Here $z=0$ is taken as the interface location at the state of rest, so the mean vertical thickness of each phase is $H_{a}$ (air) and $H_{o}$ (water). The air and water occupy the region $\eta (x,y,t)\leq z \leq H_{a}$ and $-H_{o}\leq z \leq \eta (x,y,t)$, respectively, where $\eta$ is assumed to be a one-valued function of $x$, $y$ and $t$ (time). With this assumption, we disregard the situation where the interfacial overturn (plunging breaker) occurs.

The fluids follow the incompressible Navier–Stokes equation:

(2.1a)$$\begin{gather} \dfrac{\partial u}{\partial{t}}+u\dfrac{\partial u}{\partial{x}}+v\dfrac{\partial u}{\partial{y}}+w\dfrac{\partial u}{\partial{z}} ={-} \dfrac{\partial}{\partial{x}}\left(g\eta+p_{nh}\right) +\dfrac{\partial {\mathsf{T}}^{xx}}{\partial{x}}+\dfrac{\partial {\mathsf{T}}^{yx}}{\partial{y}}+\dfrac{\partial {\mathsf{T}}^{zx}}{\partial{z}}, \end{gather}$$

(2.1b)$$\begin{gather}\dfrac{\partial v}{\partial{t}}+u\dfrac{\partial v}{\partial{x}}+v\dfrac{\partial v}{\partial{y}}+w\dfrac{\partial v}{\partial{z}} ={-} \dfrac{\partial}{\partial{y}}\left(g\eta+p_{nh}\right) +\dfrac{\partial {\mathsf{T}}^{xy}}{\partial{x}}+\dfrac{\partial {\mathsf{T}}^{yy}}{\partial{y}}+\dfrac{\partial {\mathsf{T}}^{zy}}{\partial{z}}, \end{gather}$$

(2.1c)$$\begin{gather}\dfrac{\partial w}{\partial{t}}+u\dfrac{\partial w}{\partial{x}}+v\dfrac{\partial w}{\partial{y}}+w\dfrac{\partial w}{\partial{z}} ={-} \dfrac{\partial p_{nh}}{\partial{z}} +\dfrac{\partial {\mathsf{T}}^{xz}}{\partial{x}}+\dfrac{\partial {\mathsf{T}}^{yz}}{\partial{y}}+\dfrac{\partial {\mathsf{T}}^{zz}}{\partial{z}}, \end{gather}$$

(2.1d)$$\begin{gather}\dfrac{\partial u}{\partial{x}}+\dfrac{\partial v}{\partial{y}}+\dfrac{\partial w}{\partial{z}} = 0. \end{gather}$$

Here, $u$, $v$ and $w$ are $x$-, $y$-, and $z$-component velocities, respectively, ${\mathsf{T}}^{xx}$ and so on are the components of kinematic viscous stress tensor $\boldsymbol{\mathsf{T}}$ (i.e. viscous stress tensor divided by density) and $g$ is gravitational acceleration. In (2.1), the total pressure $\tilde {p}$ is decomposed into the hydrostatic and non-hydrostatic components, $\tilde {p}=\rho g (\eta -z)+\tilde {p}_{nh}=\rho g (\eta -z)+\rho p_{nh}$, where $\tilde {p}_{nh}\equiv \rho p_{nh}$ and $\rho$ is the density ($\rho _{a}$ or $\rho _{o}$). The viscous stress can be written as

(2.2)\begin{equation} {\mathsf{T}}^{x_ix_j} = \nu\left(\dfrac{\partial u_i}{\partial{x_j}}+\dfrac{\partial u_j}{\partial{x_i}}\right), \end{equation}

where $i,j=1,2,3$, $(x_1, x_2, x_3)=(x,y,z)$ and $(u_1, u_2, u_3)=(u,v,w)$. The kinematic viscosity $\nu$ can be either constant or some eddy viscosity modelled with the velocity field.

At the top and bottom ($z=H_{a}, -H_{o}$), $w=0$ is demanded as the kinematic boundary condition. When $\nu \neq 0$, dynamic boundary conditions must be provided there, such as the no-slip condition ($(u,v,w)_{air}=(u,v,w)_{water}$) or some momentum flux parameterisation ($\rho {\mathsf{T}}^{xz}=\tau ^x$, $\rho {\mathsf{T}}^{yz}=\tau ^y$). At the interface $z=\eta (x,y,t)$, the mass continuity is demanded as the kinematic boundary condition:

(2.3)\begin{equation} \dfrac{\partial\eta}{\partial{t}}=w-u\dfrac{\partial\eta}{\partial{x}}-v\dfrac{\partial\eta}{\partial{y}}, \quad \text{at} \ z=\eta+0, \eta-0. \end{equation}

Here, this condition is satisfied at both the air ($z=\eta +0$) and water ($z=\eta -0$) sides. By vertically integrating the continuity equation (2.1d) from the top or bottom to the interface and using this condition, one obtains the column mass conservation equation:

(2.4a)$$\begin{gather} \dfrac{\partial\eta}{\partial{t}}=\dfrac{\partial}{\partial{x}}\int_\eta^{H_{a}}u\,{\rm d} z +\dfrac{\partial}{\partial{y}}\int_\eta^{H_{a}}v\,{\rm d} z , \end{gather}$$

(2.4b)$$\begin{gather}\dfrac{\partial\eta}{\partial{t}}={-}\dfrac{\partial}{\partial{x}}\int_{{-}H_{o}}^\eta u\,{\rm d} z -\dfrac{\partial}{\partial{y}}\int_{{-}H_{o}}^\eta v\,{\rm d} z. \end{gather}$$

As the dynamic boundary conditions, the continuity of interfacial stress $(-\tilde {p}\boldsymbol{\mathsf{I}}+\rho \boldsymbol{\mathsf{T}})\boldsymbol {\cdot } \boldsymbol {n}$ is demanded. Here, ${\boldsymbol {n}} = (1+\eta _x^2+\eta _y^2)^{-1/2}(-\eta _x, -\eta _y, 1)$, denotes the upwards-looking unit normal vector at the interface, where $\eta _x\equiv {\partial \eta }/{\partial x }$ and $\eta _y\equiv {\partial \eta }/{\partial y }$, and $\boldsymbol{\mathsf{I}}$ denotes the unit tensor. We formulated the stress continuity via $\tau _{i}^{tx}$, $\tau _{i}^{ty}$, and $-\tilde {p}_{nh}+\tau _{i}^n$ (subscript i denotes ‘interface’ and not the coordinate index) in the following equations:

(2.5a)$$\begin{gather} \tau_{i}^{tx}\equiv \rho\boldsymbol{t}^x\boldsymbol{\cdot}\boldsymbol{\mathsf{T}}\boldsymbol{\cdot}\boldsymbol{n}=\rho\frac{(1-\eta_x^2){\mathsf{T}}^{zx}-\eta_x\eta_y{\mathsf{T}}^{zy}+\eta_x({\mathsf{T}}^{zz}-{\mathsf{T}}^{xx})-\eta_y {\mathsf{T}}^{xy}}{\left[(1+\eta_x^2+\eta_y^2)(1+\eta_x^2)\right]^{1/2}}, \end{gather}$$

(2.5b)$$\begin{gather}\tau_{i}^{ty}\equiv \rho\boldsymbol{t}^y\boldsymbol{\cdot}\boldsymbol{\mathsf{T}}\boldsymbol{\cdot}\boldsymbol{n}=\rho\frac{-\eta_x\eta_y{\mathsf{T}}^{zx}+(1-\eta_y^2){\mathsf{T}}^{zy}-\eta_x {\mathsf{T}}^{xy}+\eta_y({\mathsf{T}}^{zz}-{\mathsf{T}}^{yy})}{\left[(1+\eta_x^2+\eta_y^2)(1+\eta_y^2)\right]^{1/2}}, \end{gather}$$

(2.5c)$$\begin{gather}-\tilde{p}_{nh}+\tau^n_{i}\equiv\boldsymbol{n}\boldsymbol{\cdot}\left(-\tilde{p}\boldsymbol{\mathsf{I}}+\rho\boldsymbol{\mathsf{T}}\right)\boldsymbol{\cdot}\boldsymbol{n}. \end{gather}$$

Here, $\boldsymbol {t}^x\equiv (1+\eta _x^2)^{-1/2}(1,0,\eta _x)$ and $\boldsymbol {t}^y\equiv (1+\eta _y^2)^{-1/2}(0,1,\eta _y)$ represent the unit vectors tangential to the interface that lie in $x$–$z$ and $y$–$z$ planes, respectively. The tangential stress components $\tau _{i}^{tx}$ and $\tau _{i}^{ty}$ need to be modelled from the neighbouring velocity field, such as the no-slip boundary condition or the bulk formula. Once these are obtained, the normal viscous stress component $\tau ^n_{i}\equiv \rho (1+\eta _x^2+\eta _y^2)^{-1}({\mathsf{T}}^{zz}-2\eta _x{\mathsf{T}}^{xz}-2\eta _y{\mathsf{T}}^{yz}+\eta _x^2{\mathsf{T}}^{xx}+2\eta _x\eta _y{\mathsf{T}}^{xy}+\eta _y^2{\mathsf{T}}^{yy})$ was computed explicitly at each side of the interface. To simplify the continuity condition of the non-hydrostatic pressure $(\tilde {p}_{nh}-\tau ^n_{i})_{air}=(\,\tilde {p}_{nh}-\tau ^n_{i})_{water}$, we introduced the pseudo-pressure variable $p\equiv \rho ^{-1}[\tilde {p}_{nh}(x,y,z)-\tau ^n_{i}(x,y)]$ and transformed the pressure gradient terms in (2.1) as follows:

(2.6)\begin{equation} {-}\dfrac{\partial p_{nh}}{\partial{x}} ={-}\dfrac{\partial}{\partial{x}}\left(p+\frac{\tau^n_{i}}{\rho}\right), \quad -\dfrac{\partial p_{nh}}{\partial{y}} ={-}\dfrac{\partial}{\partial{y}}\left(p+\frac{\tau^n_{i}}{\rho}\right), \quad -\dfrac{\partial p_{nh}}{\partial{z}} ={-}\dfrac{\partial p}{\partial{z}}. \end{equation}

The pseudo-pressure $p$ multiplied with density is continuous at the interface: $(\rho p)_{air}=(\rho p)_{water}$.

We employed the curvilinear coordinate that follows the interfacial deformation. The independent variables $(x,y,z,t)$ were transformed to $(x^*,y^*,z^*,t^*)$ as

(2.7a)$$\begin{gather} x=x^*,\ y=y^*,\ z=z^*+(1-z^*/H_{a})\eta,\ t=t^*\quad \text{(air side)}, \end{gather}$$

(2.7b)$$\begin{gather}x=x^*,\ y=y^*,\ z=z^*+(1+z^*/H_{o})\eta,\ t=t^*\quad \text{(water side)}. \end{gather}$$

This transformation maps the physical air (water) domain $\eta \leq z \leq H_{a}$ ($-H_{o}\leq z \leq \eta$) to the computational domain $0\leq z^*\leq H_{a}$ ($-H_{o}\leq z^* \leq 0$).

After coordinate transformation, the governing equations (2.1) can be written in the following form:

(2.8a) $$\begin{gather} \begin{aligned}\dfrac{\partial U}{\partial{t^*}}&={-}\dfrac{\partial}{\partial{x^*}}\left(\frac{UU}{h}+gh\eta +\frac{h\tau^n_{i}}{\rho} + P - hT^{xx}\right)-\dfrac{\partial}{\partial{y^*}}\left(\frac{VU}{h} - hT^{yx}\right)\nonumber\\ &\quad-\dfrac{\partial}{\partial{z^*}}\left(\frac{\varOmega U}{h}-gz_{x^*}\eta-\frac{z_{x^*}\tau^n_{i}}{\rho} -\frac{z_{x^*}P}{h}+z_{x^*}T^{xx}+z_{y^*}T^{yx}-T^{zx}\right),\end{aligned}\end{gather}$$

(2.8b) $$\begin{gather} \begin{aligned}\dfrac{\partial V}{\partial{t^*}}&={-}\dfrac{\partial}{\partial{x^*}}\left(\frac{UV}{h} - hT^{xy}\right)-\dfrac{\partial}{\partial{y^*}}\left(\frac{VV}{h} +gh\eta+\frac{h\tau^n_{i}}{\rho} + P - hT^{yy}\right)\nonumber\\ &\quad -\dfrac{\partial}{\partial{z^*}}\left(\frac{\varOmega V}{h}-gz_{y^*}\eta-\frac{z_{y^*}\tau^n_{i}}{\rho}-\frac{z_{y^*}P}{h}+z_{x^*}T^{xy}+z_{y^*}T^{yy}-T^{zy}\right),\end{aligned}\end{gather}$$

(2.8c) $$\begin{gather} \begin{aligned}\dfrac{\partial W}{\partial{t^*}}&={-}\dfrac{\partial}{\partial{x^*}}\left(\frac{UW}{h}-hT^{xz}\right)-\dfrac{\partial}{\partial{y^*}}\left(\frac{VW}{h}-hT^{yz}\right)\nonumber\\ &\quad -\dfrac{\partial}{\partial{z^*}}\left(\frac{\varOmega W}{h}+\frac{P}{h}+z_{x^*}T^{xz}+z_{y^*}T^{yz}-T^{zz}\right),\end{aligned}\end{gather}$$

(2.8d)$$\begin{gather}\dfrac{\partial U}{\partial{x^*}}+\dfrac{\partial V}{\partial{y^*}}+\dfrac{\partial}{\partial{z^*}}\left(\frac{W}{h}-\frac{z_{x^*}U}{h}-\frac{z_{y^*}V}{h}\right) = 0. \end{gather}$$

Here, subscripts $t^*$, $x^*$, $y^*$ and $z^*$ denote partial derivatives, $h\equiv {\partial z }/{\partial z ^*}$ is the layer thickness, $U\equiv hu$, $V\equiv h v$, $W\equiv hw$ and $P\equiv hp$ are the layer thickness-weighted variables and $\varOmega \equiv w-z_{t^*}-uz_{x^*}- v z_{y^*}$ is the layer thickness-weighted $z^*$-velocity. The apparent form of (2.8) is identical for both air and water. However, the expressions for $h$ and other variables vary because the definition of $z$ (2.7) depends on the fluid type. The column-mass conservation equation (2.4) can be transformed as follows:

(2.9a)$$\begin{gather} \dfrac{\partial\eta}{\partial{t^*}}=\dfrac{\partial}{\partial{x^*}}\int_0^{H_{a}}U\,{\rm d} z^* +\dfrac{\partial}{\partial{y^*}}\int_0^{H_{a}}V\,{\rm d} z^* , \end{gather}$$

(2.9b)$$\begin{gather}\dfrac{\partial\eta}{\partial{t^*}}={-}\dfrac{\partial}{\partial{x^*}}\int_{{-}H_{o}}^0 U\,{\rm d} z^* -\dfrac{\partial}{\partial{y^*}}\int_{{-}H_{o}}^0 V\,{\rm d} z^*. \end{gather}$$

2.2. Spatial discretisation

Taking advantage of the doubly periodic horizontal boundary condition, we discretise the variables in equally spaced grid points in $x^*$ and $y^*$ and take the pseudo-spectral approach to approximate horizontal derivatives. Hereinafter, ${\partial }/{\partial x ^*}$ and ${\partial }/{\partial y ^*}$ represent the evaluation of horizontal derivatives using the fast Fourier transform algorithm. To avoid nonlinear instability, spectral coefficients above two-thirds of Nyquist frequency are filled with zeros.

In vertical, the second-order finite-difference method is used. The air-side (water-side) domain $0 \leq z^* \leq H_{a}$ ($-H_{o}\leq z^* \leq 0$) is discretised as layers with variable thickness, and variables are placed in a staggered grid layout (figure 1). Horizontal velocity components and scalars are defined at layer centres, and vertical velocity components are defined at layer interfaces. Both air- and water-side $W$ are defined at the air–water interface because the velocity discontinuity may arise there unless the no-slip boundary condition is assumed. The thickness-weighted pseudo-pressure $P$ is defined not only at layer centres but also at the air–water interface. Denoting the numbers of layers with $K_{a}$ (air) and $K_{o}$ (water), $P$ is discretised into $K_{a}+K_{o}+1$ points in vertical. This additional degree of freedom allows us to ensure the mass continuity at the interface (2.3), as discussed further in § 2.3. Hereinafter, ${\partial }/{\partial z ^*}$ represents the second-order finite-difference approximation of vertical derivatives. Horizontal velocity components $U$ and $V$ are defined at the cell centres. When they are required at the air–water interface as in (2.8d), the values at the grid centres just above or below the interface is consistently used to represent the interfacial value. Evaluating (2.9) in the discretised domain and using (2.8d), we obtain

(2.10a)$$\begin{gather} \dfrac{\partial\eta}{\partial{t^*}}=\sum_k \left(\dfrac{\partial U_k}{\partial{x^*}}+\dfrac{\partial V_k}{\partial{y^*}}\right)\Delta z^*_k = w_{1/2} - u_1\eta_x- v_1\eta_y, \end{gather}$$

(2.10b)$$\begin{gather}\dfrac{\partial\eta}{\partial{t^*}}={-}\sum_k \left(\dfrac{\partial U_k}{\partial{x^*}}+\dfrac{\partial V_k}{\partial{y^*}}\right)\Delta z^*_k = w_{{-}1/2} - u_{{-}1}\eta_x- v_{{-}1}\eta_y. \end{gather}$$

Here, the subscript $k$ represents vertical coordinate index, where $k=\pm 1$ denote the cell centres next to the interface and $k=\pm 1/2$ denote the variables on each side of the interface (see figure 1). This is the kinematic boundary condition (2.3) represented in the present framework. This approximation contains truncation error of $O(\Delta z^*)$, but it is typically overwhelmed by other discretisation errors (e.g. vertical finite-difference error of $O(\Delta z^{* 2})$), as is observed in Appendix A.1.

Figure 1. Layout of discretised variables in the vertically staggered grid. Blue, orange and green markers represent the properties of water, air and interface, respectively. Numbers $0$, $\pm 1/2$ and $\pm 1$ denote the indices of vertical grid points used in § 2.3.

Note that the continuity of the normal stress is achieved implicitly by introducing the pseudo-pressure at the interface and using the common value for calculating the pressure-gradient force at the air and water side. Similar strategy is adopted to achieve the continuity of the tangential stress: we evaluated $\tau ^{tx}_{i}$ and $\tau ^{ty}_{i}$ at the interface and used that common value at the air and water side, thereby ensuring the momentum conservation. The method for evaluating the tangential stress is detailed in the following subsection.

2.3. Temporal integration

To achieve high accuracy in mass, momentum and energy conservation, the fourth-order Adams–Bashforth (AB4) scheme was adopted for the time integration of velocity field and interface elevation (Fujiwara et al. 2020). Hereinafter, we describe the time-advancement method from the known fields of $U^{(n)}$, $V^{(n)}$, $W^{(n)}$ and $\eta ^{(n)}$, where superscript $(n)$ denotes the timesteps. Since we employ a fully explicit scheme, all the right-hand side terms of (2.8a–c) and (2.9) are evaluated at timestep $n$ and used for temporal integration. The terms at past three timesteps ($n-1,n-2,n-3$) are stored for the AB4 scheme, but when the stability limit of some terms (e.g. viscosity) is much looser than that required by the interfacial waves, lower-order explicit schemes can be used to reduce computational cost without much truncation error.

The viscous stress tensor $T^{ij}$ is diagnostically evaluated from $(U^{(n)},V^{(n)},W^{(n)})$. At the air–water interface, the tangential components $(\tau ^{tx}_{i}, \tau ^{ty}_{i})$ are required to be continuous across the interface. They are evaluated referring to the velocity at the bottom layer of the air and the top layer of the water. In the case of the no-slip boundary condition, the velocity at the interface $z^*=0$ is approximated with the value at $z^*=-0.5\Delta z^*$ (top layer of water) due to the large density ratio. The tangential stress $(\tau ^{tx}_{i}, \tau ^{ty}_{i})$ is evaluated from the velocity difference between the lowest layer of the air and the interface. Then $(\tau ^{tx}_{i}, \tau ^{ty}_{i})$ is used to determine the boundary values of the water-side viscous stress tensor, together with $\tau ^n_{i}$, which is independently evaluated at each side. Due to the approximation of interfacial velocity using the first grid point, the consistency between the tangential stress and the surrounding velocity is not strictly satisfied. However, the relative error induced by this approximation (difference between ‘true’ interfacial velocity and the top layer of water) would be small as discussed in the following. The error can be roughly estimated from the leading-order stress balance $\rho _{a} \nu _{a} ({\textrm {d} u}/{\textrm {d} z})_{a}=\rho _{o} \nu _{o} ({\textrm {d} u}/{\textrm {d} z})_{o}$. The difference between the true interfacial velocity $u_{i}$ and approximated velocity is estimated by

(2.11)\begin{align} u({-}0.5\Delta z^*)-u_{i}= 0.5\Delta z^*\left(\dfrac{{\rm d} u}{{\rm d} z}\right)_{o} \approx 0.5\Delta z^*\frac{\rho_{a} \nu_{a}}{\rho_{o} \nu_{o}}\left(\dfrac{{\rm d} u}{{\rm d} z}\right)_{a}\approx\frac{\rho_{a} \nu_{a}}{\rho_{o} \nu_{o}} (u(0.5\Delta z^*)-u_{i}), \end{align}

where the grid thicknesses at the air and water sides are assumed similar. Therefore, the relative error is estimated as $O(\rho _{a}\nu _{a}/\rho _{o}\nu _{o})$, which is typically of $O(10^{-2})$, assuming $\rho _{a}\nu _{a}/\rho _{o}\nu _{o}\ll 1$ and $u(-0.5\Delta z^*)\sim u(0.5\Delta z^*)$. Furthermore, as long as the tangential stress imposed for each phase is identical, local momentum conservation is satisfied. Therefore, the present approximation saves us from an iterative approach for velocity–stress consistency without much disadvantage in accuracy. The error caused by this approximation is evaluated in Appendix C.

In the present model with variable vertical layer thickness, the temporal integration of the viscosity term using an explicit scheme would impose a severe limitation on the time step for a stable computation. However, the CFL condition regarding the interfacial gravity wave mode, which need to solve accurately, is similarly strict, even with the vertical resolution high enough to resolve the Stokes boundary layer (SBL) with several layers. Therefore, we employ the explicit scheme for the integration of viscous terms and do not employ the implicit scheme that requires iteration.

To integrate the pressure gradient terms in (2.8) using the AB4 scheme, the instantaneous pressure field $P^{(n)}$ is needed. The pressure field must satisfy the normal stress continuity equation (2.5c) and retain the velocity incompressibility. Such pressure field must be obtained through a Poisson equation with non-constant coefficients that require an iterative approach to solve. In some preceding air–water coupled numerical models, the air–water coupling was achieved by alternatively solving for each phase, where the single-phase solution is used as the boundary condition for the other phase (see the detailed discussion given in, e.g. Lombardi, De Angelis & Banerjee 1996; Yang & Shen 2011b). In the present model, we derive the pressure field based on the air–water coupled pressure Poisson equation, which is solved in a single-loop iteration, since the numerical accuracy of the pressure field matters in the problems of our concern.

To obtain the instantaneous pressure field, we constructed a pressure Poisson equation for $P^{(n)}$ as follows. The time derivative of incompressibility relation (2.8d) is

(2.12) $$\begin{align} &\dfrac{\partial U_{t^*}}{\partial{x^*}}+\dfrac{\partial V_{t^*}}{\partial{y^*}}+\dfrac{\partial}{\partial{z^*}}\left(\frac{W_{t^*}}{h}-\frac{z_{x^*}U_{t^*}}{h}-\frac{z_{y^*}V_{t^*}}{h}\right) \nonumber\\ &\quad +\dfrac{\partial}{\partial{z^*}}\left[\left(\frac{1}{h}\right)_{t^*}W-\left(\frac{z_{x^*}}{h}\right)_{t^*}U-\left(\frac{z_{y^*}}{h}\right)_{t^*}V\right]= 0. \end{align}$$

By substituting (2.8a–c), we obtain

(2.13)\begin{align} &\left(\dfrac{\partial^2}{\partial{x^*}^2}+\dfrac{\partial^2}{\partial{y^*}^2}+\dfrac{\partial^2}{\partial{z^*}^2}\right)P = \left(1-\frac{1}{h^2}\right)\dfrac{\partial^2P}{\partial{z^*}^2} \nonumber\\ &\quad +\frac{1}{h^2}\dfrac{\partial}{\partial{z^*}}\left[h(z_{x^*}P)_{x^*}+h(z_{y^*}P)_{y^*}+hz_{x^*}P_{x^*}+hz_{y^*}P_{y^*}-z_{x^*}(z_{x^*}P)_{z^*}-z_{y^*}(z_{y^*}P)_{z^*}\right]\nonumber\\ &\quad +\dfrac{\partial G^x}{\partial{x^*}}+\dfrac{\partial G^y}{\partial{y^*}}+\frac{1}{h}\dfrac{\partial}{\partial{z^*}}(G^z-z_{x^*}G^x-z_{y^*}G^y) +\dfrac{\partial}{\partial{z^*}}\left[\left(\frac{1}{h}\right)_{t^*}W-\left(\frac{z_{x^*}}{h}\right)_{t^*}U-\left(\frac{z_{y^*}}{h}\right)_{t^*}V\right], \end{align}

where $G^x$, $G^y$ and $G^z$ denote the right-hand-side terms in (2.8a–c) that can be evaluated explicitly, i.e. terms that do not include $P$, and the timestep index $(n)$ is omitted. We separated the constant-coefficient terms in the Laplacian of $P$ into the left-hand side, and the time derivatives of the metric variables $h$ and $z$ were explicitly evaluated using (2.9). This relation holds for each discretised layer, so the number of equations per vertical column was $K_{a}+K_{o}$. One more equation per column was needed to obtain $P$, which has $K_{a}+K_{o}+1$ degrees of freedom in vertical. For this, we employed the mass continuity equation at the interface (2.3). Equating the right-hand sides of (2.10) and taking a time derivative, we obtained

(2.14)$$\begin{align} &\frac{1}{h_{a}}\left(\dfrac{\partial W}{\partial{t^*}}\right)_{{1}/{2}}-\frac{\eta_{x^*}}{h_{a}}\left(\dfrac{\partial U}{\partial{t^*}}\right)_1-\frac{\eta_{y^*}}{h_{a}}\left(\dfrac{\partial V}{\partial{t^*}}\right)_1+\left(\frac{1}{h_{a}}\right)_{t^*}W_{{1}/{2}}-\left(\frac{\eta_{x^*}}{h_{a}}\right)_{t^*}U_1-\left(\frac{\eta_{y^*}}{h_{a}}\right)_{t^*}V_1 \nonumber\\ &=\frac{1}{h_{o}}\left(\dfrac{\partial W}{\partial{t^*}}\right)_{-{1}/{2}}-\frac{\eta_{x^*}}{h_{o}}\left(\dfrac{\partial U}{\partial{t^*}}\right)_{{-}1}-\frac{\eta_{y^*}}{h_{o}}\left(\dfrac{\partial V}{\partial{t^*}}\right)_{{-}1}+\left(\frac{1}{h_{o}}\right)_{t^*}W_{-{1}/{2}}-\left(\frac{\eta_{x^*}}{h_{o}}\right)_{t^*}U_{{-}1}-\left(\frac{\eta_{y^*}}{h_{o}}\right)_{t^*}V_{{-}1}. \end{align}$$

Variables with subscripts $1$ and $-1$ were evaluated at the layer centres next to the interface in the air and water side, respectively (see figure 1). Similarly, subscripts $1/2$ and $-1/2$ denote the variables on the air and water side, respectively, of the interface $z^*=0$. By substituting (2.8a–c) to (2.14), we obtained the following equation for $P$:

(2.15)\begin{align} &\left(-\frac{1}{h_{a}^2}\dfrac{\partial P}{\partial{z^*}}+\frac{G^z}{h_{a}}\right)_{{1}/{2}} -\frac{\eta_{x^*}}{h_{a}}\left[-\dfrac{\partial P}{\partial{x^*}}+\left(\frac{z_{x^*}P}{h_{a}}\right)_{z^*}+G^x\right]_1 -\frac{\eta_{y^*}}{h_{a}}\left[-\dfrac{\partial P}{\partial{y^*}}+\left(\frac{z_{y^*}P}{h_{a}}\right)_{z^*}+G^y\right]_1\nonumber\\ &\qquad +\left(\frac{1}{h_{a}}\right)_{t^*}W_{{1}/{2}}-\left(\frac{\eta_{x^*}}{h_{a}}\right)_{t^*}U_1-\left(\frac{\eta_{y^*}}{h_{a}}\right)_{t^*}V_1 \nonumber\\ &\quad =\left(-\frac{1}{h_{o}^2}\dfrac{\partial P}{\partial{z^*}}+\frac{G^z}{h_{o}}\right)_{-{1}/{2}} -\frac{\eta_{x^*}}{h_{o}}\left[-\dfrac{\partial P}{\partial{x^*}}+\left(\frac{z_{x^*}P}{h_{o}}\right)_{z^*}+G^x\right]_{{-}1} -\frac{\eta_{y^*}}{h_{o}}\left[-\dfrac{\partial P}{\partial{y^*}}+\left(\frac{z_{y^*}P}{h_{o}}\right)_{z^*}+G^y\right]_{{-}1}\nonumber\\ &\qquad +\left(\frac{1}{h_{o}}\right)_{t^*}W_{-{1}/{2}}-\left(\frac{\eta_{x^*}}{h_{o}}\right)_{t^*}U_{{-}1}-\left(\frac{\eta_{y^*}}{h_{o}}\right)_{t^*}V_{{-}1}. \end{align}

The pressure variable notation in this equation requires clarification. From the definition, $P\equiv h_{a} p$ and $P\equiv h_{o} p$ in the air side (positive indices) and water side (negative indices), respectively. Since $\rho p$ is continuous at the interface, we introduced $P_0\equiv \rho p/{\sqrt {\rho _{a}\rho _{o}}}$ for numerical implementation. With these definitions of discretised $P$, the $z^*$-derivatives of $P$ in (2.15) should be read as

(2.16)\begin{equation} \left(\dfrac{\partial P}{\partial{z^*}}\right)_{{1}/{2}} = \frac{h_{a} p_1-h_{a} p_0}{\Delta z^*_1/2} = \frac{1}{\Delta z^*_1/2}\left(P_1 - h_{a}\sqrt{\frac{\rho_{o}}{\rho_{a}}}P_0\right), \end{equation}

and so on. Similarly, this definition of $P_0$ was used when evaluating (2.13) at the layers directly above and below the interface.

For each vertical column, (2.13) and (2.15) provided $(K_{a}+K_{o}+1)$ equations with $(K_{a}+K_{o}+1)$ unknown variables $P_{-K_{o},\ldots,0,\ldots,K_{a}}$. This system was solved by the fixed-point iteration method as described in Sullivan et al. (2000) and Fujiwara et al. (2020). When the vertical index is denoted with $k$, (2.13) and (2.15) can be written as

(2.17a)$$\begin{gather} \left(\dfrac{\partial^2}{\partial{x^*}^2}+\dfrac{\partial^2}{\partial{y^*}^2}\right)P_k + \left(\dfrac{\partial^2P}{\partial{z^*}^2}\right)_k = \epsilon(P_{k-1},P_k,P_{k+1}) + Q_k \quad (k\neq 0), \end{gather}$$

(2.17b)$$\begin{gather}\left(\dfrac{\partial^2P}{\partial{z^*}^2}\right)_k = \epsilon(P_{k-1},P_k,P_{k+1}) + Q_k \quad (k=0). \end{gather}$$

Here, $\epsilon$ is the linear term of $P$ whose coefficients vary in space, and $Q$ represents the terms that do not include $P$. Denoting the left-hand side terms with $\mathcal {L}(P)$ and the iteration index with superscript $[m]$, we iteratively solved for $P^{[m]}$ in the following equation:

(2.18)\begin{equation} \mathcal{L}(P_{k-1}^{[m]},P_k^{[m]},P_{k+1}^{[m]}) = \epsilon(P_{k-1}^{[m-1]},P_k^{[m-1]},P_{k+1}^{[m-1]}) + Q_k. \end{equation}

The inversion of the left-hand side can be achieved by using horizontal fast Fourier transform and tridiagonal solver. Since both the top and bottom boundaries are rigid-lid, one can add an arbitrary constant to the pressure field. We constrained this degree of freedom by demanding that the horizontal average of $P_0$ is zero. This equation was iteratively solved until all of the normalised residuals

(2.19)\begin{equation} \frac{\max|P_{1,\ldots,K_{a}}^{[m]}-P_{1,\ldots,K_{a}}^{[m-1]}|}{\textrm{r.m.s.}(P_{1,\ldots,K_{a}}^{[m]})}, \frac{\max|P_{0}^{[m]}-P_{0}^{[m-1]}|}{\textrm{r.m.s.}(P_{0}^{[m]})}, \frac{\max|P_{{-}K_{o},\ldots,-1}^{[m]}-P_{{-}K_{o},\ldots,-1}^{[m-1]}|}{\textrm{r.m.s.}(P_{{-}K_{o},\ldots,-1}^{[m]})} \end{equation}

become smaller than a certain value (typically $10^{-8}$), where r.m.s. denotes root mean square over all grid points. This way, the instantaneous pressure field is accurately solved without nested iteration. For the AW-ctrl case introduced in § 3, which is a typical problem with wave slope $ak=0.1$, the number of iteration required for convergence was seven.

Once the pressure field was obtained, the prognostic equations (2.8a–c) and (2.9b) were integrated with the AB4 scheme. Due to the nonlinearity of the incompressibility equation, the velocity field after the time integration contains a small compressibility of $O(\Delta t^5)$. To fix this, the gradient of a scalar potential was added to the velocity field as in Fujiwara et al. (2020). The Poisson equation for the potential was obtained by demanding the incompressibility of the final velocity field. The procedure is very similar to the pressure equations (2.13) and (2.15), so it is not detailed here.

The performance of the numerical model was examined in several benchmark cases, and its results are described in Appendix A. The model well-reproduced the analytic behaviours of interfacial gravity waves and the Miles instability problem with a reasonable spatial resolution. Notably, the flux form configuration and the fully coupled pressure-solving strategy led to small numerical errors in the energy, momentum and mass conservation, as demonstrated in § 3. This feature is favourable in considering a delicate problem such as the WL turbulence production.

The numerical procedure can be extended easily to the air-side-only configuration where $\eta (x,y,t)$ is externally provided. Although such a problem is not considered in this article, the numerical procedure is briefly introduced in Appendix B for future reference.

3. Problem set-up

Numerical simulations of attenuating interfacial waves are conducted to clarify the difference in wave-induced turbulence between the air–water coupled flow and the free-surface flow. We consider both the air–water coupled (simulated with the model developed in this study, labelled ‘AW’) and the water-only (simulated with the free surface model of Fujiwara et al. (2020), labelled ‘W’) configurations. In each configuration, a basic three-dimensional set-up labelled ‘ctrl’ is considered, and several variants are also designed to study the dynamical roles of particular processes and sensitivity to problem set-up. In table 1, all the set-ups are listed.

Table 1. List of cases considered in this study.

We first introduce the air–water coupled configuration. The following description pertains to the AW-ctrl case, whereas the AW-noturb case is derived by assuming homogeneity in the $y$ direction ($\partial /\partial y=0$). Consider a wavelength $\lambda$ and non-dimensionalise all the variables based on the spatial scale of $k^{-1}=\lambda /2{\rm \pi}$ and the temporal scale of $\sigma ^{-1}=[gk(\rho _{o}-\rho _{a})/(\rho _{o}+\rho _{a})]^{-1/2}$ (angular frequency of the small-amplitude deep-water wave). For density, the air-side density is used as the reference. Hereinafter, all the symbols denote non-dimensionalised variables. Under this non-dimensionalised system, a three-dimensional domain of $(L_x, L_y, H_{a}, H_{o})=(2{\rm \pi}, 2.4{\rm \pi}, 1.5{\rm \pi}, 1.5{\rm \pi} )$ was considered. The mean depths of air and water layers are 75 % of a wavelength, which is still in a deep-water regime. Therefore, the wave period is roughly $2{\rm \pi}$, with small modifications by nonlinearity and finite-depth effects.

This system is characterised by three non-dimensional numbers, namely, the density ratio $r \equiv \rho _{o}/\rho _{a}$ and the Reynolds numbers $({\textit {Re}}_{a}, {\textit {Re}}_{o}) = (\sigma k^{-2}/\nu _{a},\sigma k^{-2}/\nu _{o})$. We choose the density ratio of $r=1.0\times 10^3$ and the Reynolds numbers of $({\textit {Re}}_{a}, {\textit {Re}}_{o})=(1.5\times 10^4,1.5\times 10^5)$. This parameter set does not precisely correspond to a particular physical condition because we are mainly focused on the dynamical understanding of the phenomena. Nevertheless, the present parameter choice roughly corresponds to the actual air–water interfacial wave with $\lambda \approx 1.0$ m ($ {\textit {Re}}_{a}=1.4\times 10^4$, $ {\textit {Re}}_{o}=1.5\times 10^5$ and $r=8.1\times 10^2$ at $10\,^\circ {\rm C}$). Here we neglect the effect of surface tension to further simplify the dynamics.

The top and bottom boundaries are flat and free-slip. The no-slip boundary condition is imposed at the air–water interface. Then the domain was discretised with 128 and 320 grid points in $x$ and $y$ directions, respectively. Higher spatial resolution is assumed in $y$ direction because the resulting flow shows a finer structure in $y$. In the vertical, the air and water domains are discretised with 128 and 160 grid points, respectively. The grid points are clustered near the interface, such that the layer thickness directly above and below the interface would be half of the viscous SBL thickness $(2/{\textit {Re}}_{{a},{o}})^{1/2}$, which is thicker in the air side. The layer thickness increased exponentially away from the interface, with ratios of 1.0239 and 1.0270 on the air and water side, respectively.

For the water-only case (W-ctrl and W-noturb), we consider a horizontally rectangular domain topped with a free-surface boundary at its upper surface, $z=\eta (x,y,t)$. The upper surface satisfies the kinematic boundary condition $\partial \eta /\partial t=w-u\partial \eta /\partial x - v \partial \eta /\partial y$ and the no-stress boundary condition $(-\tilde {p}\boldsymbol{\mathsf{I}}+\rho \boldsymbol{\mathsf{T}})\boldsymbol {\cdot } \boldsymbol {n}=\boldsymbol {0}$. The bottom boundary is the flat wall with the free-slip condition as in the AW cases. Because the dispersion relation of the surface waves differs from the air–water coupled cases, the reference time scale of $\sigma ^{-1}=(gk)^{-1/2}$ was used for the non-dimensionalisation instead. Otherwise, the geometric set-up and parameters are identical to the water side of the AW cases.

In both the AW and W configurations, we initialised the flow field with the orbital velocity of the fifth-order Stokes wave (Tsuji & Nagata (1973) for the AW and Fenton (1985) for the W cases) that propagates towards the $x$ direction, with wavelength $2{\rm \pi}$ and initial wave amplitude of $a=0.1$ (wave slope of $0.1$). The asymptotic solution of Tsuji & Nagata (1973) assumes a deep-water limit, so its use as the initial condition can introduce a spurious response with an interfacial displacement of $O(10^{-3})$. The analysis conducted here is insensitive to the spurious waves of this amount. In the AW-ctrl and W-ctrl cases, a Gaussian noise with a standard deviation of $10^{-3}$ is added to $u$ at water-side grid points near the interface (only in one layer centred at $z^*=-0.0086$ and $\Delta z^*=0.0020$ thick) to seed the horizontal inhomogeneity. Before adding to the orbital velocity, the noise field is horizontally low-pass filtered at a cutoff wavenumber of 16. Then, the divergent component of the noise field is removed and made solenoidal in the projection procedure during the initialisation of the simulation. We expect that the resulting turbulence is insensitive to this noise structure because of inherent instability (Tsai et al. 2017; Fujiwara et al. 2020). To examine the dependence of the result on initial noise structure, we have conducted an AW case with the same amplitude noise added to the grid points of all levels in the water side, which is named the ‘AW-deepnoise’ case. In addition, to study the phenomenon under larger wave amplitude, an AW case with doubled initial wave amplitude (AW-a2) was conducted. We also conducted the sensitivity study to the domain size (AW-long and AW-narrow), which is described in Appendix E.

Starting from this initial velocity field, the temporal integration was conducted with $\Delta t = 2{\rm \pi} /240$ until $t=2400{\rm \pi}$, which is about 1200 wave periods. During the simulation, it was confirmed that the waves barely change shape and do not break, as monitored by the time series of maximum interface slope $\max (\sqrt {\eta _x^2+\eta _y^2})$. In all cases listed in table 1, the maximum slope decreased over time and did not exceed its initial value (about 0.2 for AW-ak2 and 0.1 for the rest). In fact, in the AW-ctrl case, the primary harmonic component (wavenumber (1,0) in $(x,y)$ direction) of $\eta$ explains more than 99.7 % of the total variance throughout the simulation period, and with the addition of the second harmonic (wavenumber $(2,0)$), it reaches above 99.998 %.

To demonstrate the accuracy of the present numerical model, the numerical errors are monitored in various ways in the AW-ctrl case, and their time series is shown in figure 2. In figure 2(a), the temporal evolution of error in conserved properties is shown. Water mass conservation can be monitored by the horizontal average of interface elevation, $\bar {\eta }$, and its error is of $O(10^{-16})$. The total $x$-component momentum $M$ should be conserved in the present set-up because of the free-slip boundary condition at the top and bottom, and the horizontally periodic boundary condition. It is defined as follows:

(3.1)\begin{equation} M(t) = r\overline{\int_{{-}H_{o}}^\eta u \,{\rm d} z} + \overline{\int_{\eta}^{H_{a}} u \,{\rm d} z}. \end{equation}

Here, the overline denotes the horizontal average. The momentum error $\Delta M\equiv M(t)-M(0)$ normalised by initial total momentum is as small as $O(10^{-7})$ after 1200 periods of integration thanks to the full-flux form configuration (2.8). The total energy is calculated as the sum of the kinetic energy (KE) and potential energy (PE):

(3.2)$$\begin{align} E(t) &= r\overline{\int_{{-}H_{o}}^\eta \frac{1}{2}(u^2+ v^2+w^2) \,{\rm d} z} \nonumber\\ &\quad + \overline{\int_{\eta}^{H_{a}} \frac{1}{2}(u^2+ v^2+w^2) \,{\rm d} z} + \frac{1}{2}(r-1)g \overline{\eta^2}. \end{align}$$

There is no energy flux through the free-slip top and bottom boundaries, so the temporal change of total energy can be monitored by the viscous dissipation. Therefore, the energy error $\Delta E$ can be computed as follows (Tsai & Hung 2007):

(3.3) $$\begin{align} \Delta E(t) &= E(t) - E(0) \nonumber\\ &\quad + \int_0^t \left[ \overline{\int_{{-}H_{o}}^\eta r(uF^x+ v F^y+wF^z)\,{\rm d} z} +\overline{\int_{\eta}^{H^{a}} (uF^x+ v F^y+wF^z)\,{\rm d} z} \right] \,{\rm d} t. \end{align}$$

Here, $(F^x, F^y, F^z)$ denotes the viscous force divided by density (as denoted in (2.1a–c)). The time series of energy error normalised with $E(0)$ shows energy growth of 0.01 % per 1000 wave period, which is sufficiently small even for a long-term integration as in the present problem.

Figure 2. Time series of numerical error in the AW-ctrl case: (a) error in total mass, horizontal momentum and energy, as defined in the text; and (b) maximum velocity divergence and mass discontinuity at the interface.

To monitor the mass continuity, the maximum velocity divergence over air and water domains is shown in figure 2(b). It is about $10^{-6\sim -5}$ smaller than the characteristic strain rate by the wave orbital motion. There is a jump of velocity divergence at around $t/2{\rm \pi} =150$, which corresponds to the onset of the small-scale flow structure by wave-induced turbulence (§ 4.2). Similarly, the continuity of interface $(w-u\eta _x-v\eta _y)_{z=\eta +0} - (w-u\eta _x-v\eta _y)_{z=\eta -0}$ is confirmed to be small. The numerical error shown in figure 2 is also monitored for the AW-a2 and AW-deepnoise cases and presented in appendix D.

We have also estimated the numerical diffusivity using the W-noturb configuration. At the moment, the tracer equation is not implemented in the two-phase model, so the free-surface model of (Fujiwara et al. 2020) that employs identical discretisation was used. Passive tracer with various diffusivity $\kappa$ and corresponding Péclet number $ {\textit {Pe}} = \sigma k^{-2}/ \kappa$ was placed in the initial field as a vertical water column and was advected and diffused following the wave motion. The same simulation was conducted for a configuration with doubled horizontal and vertical grid points, and the tracer fields at $t=100{\rm \pi}$ were compared for the two resolutions. For simulations with $ {\textit {Pe}}\gtrsim O(10^6)$, the tracer fields were different between the two resolutions, which is dominated by the numerical dispersion and diffusion. However, at $ {\textit {Pe}} \lesssim O(10^6)$, the simulation results are nearly identical between the two resolutions. Therefore, in the present simulations with ${\textit {Re}}_{o}=1.5\times 10^5$, the influence of numerical dispersion/diffusion is negligible.

Finally, to justify the approximation of interfacial velocity by the water-side top layer, the resulting error in interfacial velocity and stress is evaluated in Appendix C between the simulation results of the AW-noturb case. The error between the exact and approximated velocity and the resulting error in momentum and energy exchange between air and water was at most $O(1)\%$. Therefore, its effect on the dynamics reported in the following section is minor.

4. Results

4.1. Overview of flow structure

An overview of the simulated wave motion and the resulting turbulence field is presented. Figure 3 is the three-dimensional plot of the streamwise ($x$ component) vorticity component in the AW-ctrl case at $t=1200{\rm \pi}$ showing the elongated vortex structure. This is consistent with the streak structure observed in the laboratory experiment by Savelyev et al. (2012). The vortices are present both in the water and air domains, but their typical spatial scale is larger in the air side because of the larger kinematic viscosity value there. These turbulent vortices developed over the time scale of $O(100)$ wave periods, as described in § 4.2.

Figure 3. Three-dimensional plot of the turbulence field of the AW-ctrl case at $t=1200{\rm \pi}$. The curved grey surfaces denote the interface, and the air domain is artificially lifted for visibility. Red and blue surfaces denote contours of $x$-component vorticity ${\partial w }/{\partial x }-{\partial v }/{\partial z }$ ($\pm 0.013$ in the air and $\pm 0.020$ in the water), and the red (blue) surfaces show the positive (negative) contours. Waves propagate towards the $x$ direction.

The velocity field in the AW-ctrl case is shown in figures 4 and 5. The instantaneous velocity field is dominated by the wave orbital motion, which is nearly uniform in the $y$ direction, so the deviation from the $y$ average along constant $x^*$ and $z^*$ lines is considered as the turbulence field. Hereinafter, prime is used to denote the turbulence component:

(4.1)\begin{equation} \varphi'(x^*,y^*,z^*,t^*)\equiv \varphi(x^*,y^*,z^*,t^*) - \langle\varphi\rangle_y(x^*, z^*, t^*), \end{equation}

where $\varphi$ denotes an arbitrary field and

(4.2)\begin{equation} \langle\varphi\rangle_y(x^*, z^*, t^*)\equiv \frac{1}{L_y}\int_0^{L_y} \varphi(x^*,y^*,z^*,t^*) \,{\rm d} y^* \end{equation}

is the along-crest average.

Figure 4. Vertical cross-section of the flow field of the AW-ctrl case at $t=2400{\rm \pi} $: (a) $\langle u\rangle _y$; (b) $\langle v\rangle _y$; (c) $\langle w\rangle _y$; (d) $\langle u^{\prime 2}\rangle _y$; (e) $\langle v^{\prime 2}\rangle _y$; ( f) $\langle w^{\prime 2}\rangle _y$. Note that the colour range is different for air and water sides in the along-crest variance plots.

Figure 5. Top view of $u'$, $ v '$ and $w'$ obtained in the AW-ctrl case at $t=2400{\rm \pi}$: (a–c) distributions on $z^*=0.2$ (air) and (d–f) distributions on $-0.2$ (water).

Figure 4 shows the $x$–$z$ vertical section of the velocity field at $t/2{\rm \pi} =1200$. Since $\eta$ is almost uniform in the $y$ direction, $\langle \eta \rangle _y$ is used as the interfacial shape. Figure 4(a–c) show that the crest-averaged velocity field is dominated by the wave orbital motion. Although it is invisible in the present plotting, the air-side $\langle u\rangle _y$ sharply adjusts to the water-side value near the interface. This will be further demonstrated in § 4.3. The along-crest variance of velocity components shows that the horizontal components of the turbulence component are located near the interface. The vertical component $\langle w^{\prime 2}\rangle _y$ is also concentrated near the interface in the water side, but it is centred at the middle of the domain in the air, representing the large vertical scale of vortices in the air side.

Figure 5 shows the horizontal distribution of $u'$, $ v '$ and $w'$ at both air side (upper row, $z^*=0.2$) and water side (lower row, $z^*=-0.2$). The flow structure is elongated in the wave propagation direction in both the air and water sides, and its spatial scale is finer for the water side because of its smaller viscosity. On the water side, the downwelling part $w'<0$ is often associated with the down-wave velocity $u'>0$, which is consistent with the typical structures of the LCs (Leibovich 1983) and the turbulence produced in water-only wave resolving simulations (Tsai et al. 2017). The air-side flow shows an upsidedown LC-like structure in that the strong streamwise current region ($u'>0$) corresponds well with the convergence zone ${\partial v '}/{\partial y }<0$ and the upwelling zone $w'>0$. This is understandable because the wave-driven airflow (figure 7d) has the negative shear ${\partial \bar {u}^{E}}/{\partial z }<0$, and the shear of the air-side Stokes drift ${\partial u ^{St}}/{\partial z }=-2a^2 {\rm e}^{-2z} < 0$ has the same sign. This results in CL2 instability (Leibovich 1983), producing the roll structure in the air side as well. The W-ctrl case also showed a turbulent flow pattern similar to the water side of the AW-ctrl case, and their difference in turbulence intensity is discussed in § 4.2.

4.2. Temporal evolution of flow field

Here, the temporal evolution of the flow field is described. First, figure 6 shows the temporal variation of the wave amplitude. The wave amplitude is evaluated as $a(t)=(2\overline {\eta ^2})^{1/2}$, where the overline denotes horizontal average. Both in the AW and W cases, the decay rate is almost identical with and without turbulence. The simulated decay rate followed the linear theory of Dore (1978) (AW) and Lamb (1932) (W) well, indicating the prevalence of linear dynamics. The simulated decay rate of both the AW-ctrl and AW-noturb cases exceeded the theoretical prediction slightly, although this deviation diminished with a higher vertical resolution near the interface on the air side. This suggests that the air-side Stokes layer is playing a central role in the energy dissipation, and it needs to be properly resolved when we focus on its role. With that caveat in mind, we proceed with the present resolution, as the enhanced wave decay predicted by the linear theory is mostly reproduced.

Figure 6. Temporal change of wave amplitude $a(t)=\sqrt {2\overline {\eta ^{2}}}$ normalised with its initial value $a(0)$. Dashed lines denote the decay rate predicted with the linear theories (1.3) and (1.1).

As waves decay, they give up the horizontal momentum and produce a vortical current, namely, the Eulerian mean drift. The Eulerian mean drift is evaluated by first vertically interpolating the horizontal velocity field $u$ to fixed levels and then taking a horizontal average. The temporal evolution of the Eulerian mean horizontal velocity $\bar {u}^{E}(z, t)$ is shown in figure 7. The data at the vertical levels between wave crests and troughs are not plotted here. The comparison of the AW-noturb and W-noturb cases (figure 7b,c) shows that, due to the enhanced energy decay in the air–water coupling, the development of $\bar {u}^{E}$ is also accelerated in the AW case. In the air side of the AW-noturb case (figure 7a), the Eulerian mean drift develops in the down-wave direction. The drift near the interface is stronger than that in the water-side drift. However, its velocity scale is still on the order of the water-side Stokes drift $O(a^2k\sigma )$. In the realistic ocean, this is likely significantly smaller than that of the wind over the waves, which is typically on the order of the wave phase speed $O(\sigma /k)$.

Figure 7. Vertical profiles of the Eulerian mean horizontal velocity $\bar {u}^{E}$ of the AW-noturb, AW-ctrl, W-noturb and W-ctrl cases: (a) AW-noturb, air; (b) AW-noturb, water; (c) W-noturb; (d) AW-ctrl, air; (e) AW-ctrl, water; ( f) W-ctrl. Dashed lines in (b) show the result of the one-dimensional diffusion equation (4.3).

The evolution of water-side Eulerian mean drift in the AW-noturb case is compared with the virtual wave stress theory. The one-dimensional diffusion equation below was simulated for the velocity profile $u(z,t)$ in the water side ($-H_{o}\leq z\leq 0$):

(4.3)\begin{equation} \dfrac{\partial u}{\partial{t}} = \nu_{o}\dfrac{\partial^2u}{\partial{z}^2}. \end{equation}

The upper boundary condition is provided as the virtual wave stress, $\nu _{o}({\partial u }/{\partial z })=\tau ^{vws}_{ao}$ (1.4), and the bottom boundary is assumed as free-slip. Since the amplitude time series was well followed by the linear theory (figure 6), the wave amplitude $a(t)$ used in $\tau ^{vws}_{ao}$ is assumed to exponentially decay with time as $a(t) = a(0){\rm e}^{-\gamma _{ao} t}$. The result is shown as the dashed lines in figure 7(b), which mostly overlaps with the AW-noturb simulation result.

In the three-dimensional cases, turbulence alters the velocity profile by carrying horizontal momentum away from the interface. As a result, velocity profile $\bar {u}^{E}$ shows weaker vertical shear in both air and water sides (figure 7d–f).

Veron & Melville (2001) derived a similarity solution of the wind-driven sheared current in the water-side based on the one-dimensional diffusion equation (4.3) subject to a constant wind stress $\tau$. Wu & Deike (2021) conducted a two-dimensional DNS of the wind–wave growth under the influence of viscosity and confirmed that the wind-induced drift current $\bar {u}^{E}(z,t)$ falls into a single curve if the depth and the velocity are scaled with $\sqrt {\nu _{o} t/8}$ (under our non-dimensionalisation $\sqrt {t/8{\textit {Re}}_{o}}$) and $U_0(t)\equiv \bar {u}^{E}(0,t)$, respectively. Figure 8(b,c) show the temporal evolution of the Eulerian current profile scaled as previously. Here, the velocity scale $U_0(t)$ is evaluated as (Veron & Melville 2001; Wu & Deike 2021)

(4.4)\begin{equation} U_0(t) = \frac{2}{\sqrt{\rm \pi}}\tau\sqrt{\frac{t}{{\textit{Re}}_o}},\end{equation}

and $\tau =\tau ^{vws}_{ao}(0)$ and $\tau =\tau ^{vws}_{o}(0)$ for the AW-ctrl/noturb and W-ctrl/noturb cases, respectively. The AW-noturb and W-noturb cases follow the scaling well, but in the AW-ctrl and W-ctrl cases, the shear is reduced due to turbulence, and the drift current profiles deviate from the scaling solution.

Figure 8. Vertical profiles of the normalised Eulerian mean horizontal velocity $\bar {u}^{E}$ of the AW-noturb, AW-ctrl, W-noturb and W-ctrl cases: (a) air-side velocity profile in the AW cases; (b) water-side profile in the AW cases; and (c) water-side profile in the W cases. Solid (dashed) lines in each panel indicate the AW-noturb and W-noturb (AW-ctrl and W-ctrl) cases. Vertical coordinate is normalised with the viscous length scale $\sqrt {t/{\textit {Re}}}$. The horizontal velocity is normalised with $U_0(t)$ of (4.4) in (b,c).

On the air side, however, the drift velocity scales differently. The Eulerian current profile in the air with vertical coordinate scaled with $\sqrt {t/8 {\textit {Re}}_{a}}$ is shown in figure 8(a). Unlike on the water side, the profiles of the AW-noturb case fall into a single curve without scaling with $U_0(t)$. Therefore, the air-side drift current develops as if a Dirichlet-type boundary condition is imposed at the bottom.

Next, the temporal evolution of turbulence, characterised by deviation velocity from $y$ average $(u', v ',w')$ is compared among cases. Figure 9 shows the temporal evolution of water-side turbulent kinetic energy (TKE) and the spanwise wavelength $l_s$. TKE is defined as $(r/2)(u^{\prime 2}+ v ^{\prime 2}+w^{\prime 2})$. Temporal evolution of its vertical profile (horizontally averaged along $z^*$-surface) is shown for the AW-ctrl (figure 9a) and W-ctrl (figure 9b) cases. Turbulence arises from the near-interface layer and spreads into the water as time passes. The magnitude of turbulence is greater in AW-ctrl than in W-ctrl throughout the simulation period. Note that, even at $t/2{\rm \pi} =1200$, the turbulence has not fully reached the bottom of the water side. However, the turbulence near the interface has reached a mature stage, and the turbulence statistics no longer change rapidly. Therefore, we can expect that the flow field of AW-ctrl after $t/200{\rm \pi} \approx 500$ would well reflect the dynamics of turbulence produced by persistent wave forcing in the air–water coupled configuration.

Figure 9. Temporal evolution of water-side turbulence statistics in the AW-ctrl, W-ctrl, AW-deepnoise and AW-ak2 cases: (a,b) vertical-temporal distribution of horizontally averaged TKE in the AW-ctrl and W-ctrl cases, respectively; (c) time series of TKE averaged over $-0.4\leq z^*\leq 0$, where the TKE of the AW-2d case is multiplied by 0.1 for visibility; (d) time series of spanwise wavenumber $l_s$ defined in (4.5) evaluated at $-0.4\leq z^*\leq 0$. Horizontal white-dashed lines in (a,b) denote $z^*=-0.4$ above which the TKE and $l_s$ in (c,d) are evaluated.

To illustrate the turbulence development in different cases more quantitatively, let us examine TKE vertically averaged over $-0.4\leq z^*\leq 0$ shown in figure 9(c). In all cases, the TKE is initially of very small magnitude (e.g. $O(10^{-7})$ in AW-ctrl), rapidly grows in the earlier stage, and then saturates, reflecting some instability mechanism at work. The onset of instability is quicker in AW-ctrl ($t/2{\rm \pi} \approx 175$) than W-ctrl ($t/2{\rm \pi} \approx 250$), and TKE magnitude of the former remains stronger than the latter throughout the simulation period. Therefore, the air–water coupling enhances the wave-induced turbulence.

To characterise the spatial scale of the turbulence, we have evaluated the TKE-weighted mean spanwise wavenumber $l_s$ following Tsai et al. (2017), as follows:

(4.5)\begin{equation} l_s(t) = \frac{\int lS(l,t) \,{\rm d} l}{\int S(l,t)\,{\rm d} l}, \end{equation}

where $l$ is the wavenumber in the $y$ direction and $S(l,t)$ is the spanwise power spectral density of turbulence velocity averaged over $0\leq x^*\leq 2{\rm \pi}$ and $-0.4 \leq z^* \leq 0$,

(4.6)\begin{align} S(l,t) = \frac{1}{0.4}\frac{1}{2{\rm \pi}}\int_0^{2{\rm \pi}}\int_{{-}0.4}^0[S_u(x^*,l,z^*,t) + S_ v(x^*,l,z^*,t) +S_w(x^*,l,z^*,t)] \,{\rm d} z^* \,{\rm d}\kern0.7pt x^*, \end{align}

where $S_u$ and so on denote the power spectral density obtained by applying the Fourier transform in the $y^*$ direction.

The temporal evolution of $l_s$ is shown in figure 9(d). Before the onset of instability, the turbulence field is dominated by the spatial scale of $l_s\approx 7$, which corresponds to the initial noise field. As the disturbance grows, vortices with relatively high wavenumbers first develop, and then the dominant spanwise scale grows ($l_s$ decreases) rapidly after the instability is saturated. At a later stage of the simulation period, the temporal development of $l_s$ is small. The AW-ctrl and W-ctrl cases show the similar temporal evolution of $l_s$, except that the time series in W-ctrl is lagged behind AW-ctrl by about 50 periods, consistent with the delay of the instability onset.

The result of the AW-deepnoise case is shown in figure 9(c,d) with a blue dashed line. The rapid growth of TKE at the onset stage and the subsequent decrease of $l_s$ observed in the AW-deepnoise case precedes the corresponding changes in AW-ctrl. As a result of the initial noise injected into all the water grid points, the initial TKE level (about $1\times 10^{-5}$) is about 300 times greater than in the AW-ctrl case. This results in an enhancement of the initial amplitude of the fastest-growing mode, so we can understand that the onset of instability occurs earlier. However, after the turbulence reached maturity, there was no clear difference between AW-deepnoise and AW-ctrl. Therefore, the statistical features of the simulated turbulence are not dependent on the initial noise structure, and only the onset timing of instability is changed.

To examine the influence of wave amplitude on the phenomenon, we have conducted an air–water coupled case with greater initial wave amplitude, $a=0.2$ (AW-a2 case). The spatial structure of turbulent velocity was similar to AW-ctrl: the vortices elongated in wave-propagation direction was observed, and the temporal evolution of turbulence statistics is shown in figure 9. Overall, the temporal evolution of turbulence is similar to the AW-ctrl case, but the onset of instability is earlier ($t/2{\rm \pi} \approx 75$). This reflects the stronger Eulerian sheared current ($\propto \tau ^{vws}_{ao}\propto a^2$) than AW-ctrl, resulting in a greater growth rate of instability. The magnitude of TKE at the mature stage (average over $600\leq t/2{\rm \pi} \leq 1200$) is about eight times greater than that of AW-ctrl.

From these results, we can make a remark on the onset time scale of wave-induced turbulence (300 periods in the AW-ctrl case). The DNS by Tsai et al. (2017) required only 10 wave periods for turbulence to develop. In their set-up, the wave slope was 0.25, which is even greater than in our AW-a2 case, and they imposed the initial noise that contains 0.1 % of the total energy. The large amplitude should lead to a strongly unstable condition, and the fraction of disturbance energy is much greater than our set-up ($O(10^{-6})\%$ for the AW-ctrl and AW-a2 cases), so the initial instability is considered to be strongly seeded. Therefore, in the simulation set-up of Tsai et al. (2017), the instability is expected to arise at an early stage after the simulation starts. Notably, the tank experiment of Savelyev et al. (2012) also observed the turbulence growth at a similar time scale (ramp-up time of 7–10 periods followed by 6–9 periods with constant wave amplitude). Therefore, we consider that the initial noise field of our simulation design was too weak, leading to unrealistically long spinup period of $O(100)$ wave periods. However, our simulations demonstrate that the flow field is inherently unstable and the turbulence develops over time even with such a small initial disturbance.

4.3. Momentum and KE budget analysis

To examine the mechanism of vertical transfer of momentum and energy, the budget equations of the horizontal momentum and KE were explored. The analysis was conducted on the model coordinate $z^*$ as in preceding studies using wave resolving simulations (Sullivan et al. 2008; Cao & Shen 2021). In our framework, the horizontally averaged $x$-component momentum equation is written as follows:

(4.7)\begin{equation} \dfrac{\partial}{\partial{t^*}}(\overline{\rho hu})={-}\dfrac{\partial}{\partial{z^*}}(F_{p} + F_{v} + F_{a}), \end{equation}

where the overline denotes the horizontal average along constant $z^*$. Here $F_{p}$, $F_{v}$ and $F_{a}$ denote the vertical flux of horizontal momentum by form stress, viscous stress and advection, respectively, and are defined as follows:

(4.8a)$$\begin{gather} F_{p} = \overline{\tilde{p}z_{x^*}}, \end{gather}$$

(4.8b)$$\begin{gather}F_{v} ={-}\rho\overline{h ({\mathsf{T}}^{xz}-z_{x^*}{\mathsf{T}}^{xx}-z_{y^*}{\mathsf{T}}^{xy})}, \end{gather}$$

(4.8c)$$\begin{gather}F_{a} = \rho\overline{h \omega u}. \end{gather}$$

Here, $\omega \equiv h^{-1}(w-z_{t^*}-uz_{x^*}- v z_{y^*}) = \varOmega / h$ is the $z^*$ velocity. The density $\rho$ and total pressure $\tilde {p}$ are non-dimensionalised with the air density, i.e. $\rho =1$ in the air and $r$ in the water. Note that the forecast variable of (4.7) is the layer-thickness-weighted momentum $\overline {hu}$, which includes the momentum associated with both the wave motion and the vortical current. The budget equation of the KE $E\equiv \rho (u^2+ v ^2+w^2)/2$ can be obtained by taking an inner product of (2.8a–c) and $(u, v,w)$:

(4.9)\begin{equation} \dfrac{\partial}{\partial{t^*}}(\rho \overline{hE})={-}\dfrac{\partial}{\partial{z^*}}(W_{p} + W_{v} + W_{a}) - C - D. \end{equation}

Here $W_{p}$, $W_{v}$ and $W_{a}$ are the vertical flux of KE by pressure, viscous stress and advection, respectively, $C$ is the conversion term to PE, and $D$ is the dissipation. They are defined as follows:

(4.10a)\begin{align} W_{p} &= \overline{\tilde{p}(z_{t^*}+h\omega)}=\overline{\tilde{p}(w-z_{x^*}u-z_{y^*} v)}, \end{align}

(4.10b)\begin{align} W_{v} &={-}\rho \left[\overline{hu({\mathsf{T}}^{xz}-z_{x^*}{\mathsf{T}}^{xx}-z_{y^*}{\mathsf{T}}^{xy})}\right. \nonumber\\ &\quad +\overline{h v({\mathsf{T}}^{yz}-z_{x^*}{\mathsf{T}}^{yx}-z_{y^*}{\mathsf{T}}^{yy})} \nonumber\\ &\quad +\left.\overline{hw({\mathsf{T}}^{zz}-z_{x^*}{\mathsf{T}}^{zx}-z_{y^*}{\mathsf{T}}^{zy})}\right] , \end{align}

(4.10c)\begin{align} W_{a} &= \overline{h \omega E}, \end{align}

(4.10d)\begin{align} C &= \rho \overline{hw} = \rho \left[\dfrac{\partial}{\partial{t^*}}(\overline{hz})+\dfrac{\partial}{\partial{z^*}}(\overline{h\omega z})\right], \end{align}

(4.10e)\begin{align} D &= (2\rho/{\textit{Re}}) \overline{h{S^{ij}}^2}. \end{align}

Here, $S^{ij}=({\partial u _i}/{\partial x _j}+{\partial u _j}/{\partial x _i})/2$ is the strain rate. The PE conversion term $C$ can be decomposed into the local PE increment $\rho {\partial (\overline {hz})}/{\partial t ^*}$ and the vertical flux of PE $\rho {\partial (\overline {h\omega z})}/{\partial z ^*}$ (the right-hand side of (4.10d)), but we only evaluate their total effect. In the following analysis, the momentum and KE budget equation terms are evaluated as temporal averages over $600 \leq t/2{\rm \pi} \leq 1200$.

To separate the influence of wave motions and turbulence, the flux terms in (4.8) and (4.10) are decomposed based on horizontal harmonics. An arbitrary variable $\varphi (x^*, y^*, z^*)$ is expanded in the Fourier series:

(4.11)\begin{equation} \varphi(x^*, y^*, z^*) = \sum_{l={-}N_x/2-1}^{N_x/2} \sum_{m={-}N_y/2-1}^{N_y/2} \hat{\varphi}_{lm}(z^*) \exp \textrm{i}\left(\frac{2{\rm \pi} l x^*}{L_x}+\frac{2{\rm \pi} my^*}{L_y}\right). \end{equation}

Here, $\textrm {i}$ denotes the imaginary unit, $N_x$ and $N_y$ are the number of grid points in $x$ and $y$ direction, respectively. Since $\varphi$ is a real function, the Fourier coefficients satisfy the relation $\hat {\varphi }_{-l\:-m}=\hat {\varphi }^{\star }_{lm}$, where the superscript $\star$ denotes complex conjugate.

Then, a flux composed of a product of two variables $A, B$ can be written as follows:

(4.12)\begin{equation} \overline{AB} = \sum_{l={-}N_x/2+1}^{N_x/2} \sum_{m={-}N_y/2+1}^{N_y/2} \hat{A}_{lm}\hat{B}_{lm}^{{\star}} = \hat{A}_{00}\hat{B}_{00}^{{\star}} + 2\mathrm{Re}[\hat{A}_{10}\hat{B}_{10}^\star] + \cdots. \end{equation}

Here, $\mathrm {Re}$ denotes the real part. Since the leading-order wave motion arises in the wavenumber $(l,m)=(1,0)$, the rectified wave effect is expected to arise mainly in the $\hat {A}_{10}\hat {B}_{10}^\star$ term. We first assessed the contribution of major harmonic components to total flux in the wave-only (AW-noturb) case and then compared the result with AW-ctrl.

First, the first harmonic coefficients of $u$, $w$ and $\tilde {p}$ in the AW-noturb case were investigated to obtain an overview of the wave-induced variation. For an instantaneous field $\varphi$, its first harmonic $\hat {\varphi }_{10}$ is separated into the in-phase ($\hat {\varphi }_{r}$) and quadrature ($\hat {\varphi }_{i}$) parts relative to the surface elevation $\eta$ as follows:

(4.13)\begin{equation} \hat{\varphi}_{r} = \mathrm{Re}\left[\hat{\varphi}_{10} / (\hat{\eta}_{10}/|\hat{\eta}_{10}|)\right], \hat{\varphi}_{i} = \mathrm{Im}\left[\hat{\varphi}_{10} / (\hat{\eta}_{10}/|\hat{\eta}_{10}|)\right]. \end{equation}

The vertical profile of in-phase and quadrature part of $\hat {u}_{10}$, $\hat {w}_{10}$ and $\hat {\tilde {p}}_{10}$ in the AW-noturb case are shown in figure 10. There, the in-phase part of pressure $\hat {\tilde {p}}_{r}$ is not shown because it does not contribute to the momentum and KE flux. These coefficients are evaluated as the composites during $600{\rm \pi} \leq t<602{\rm \pi}$. Following Cao & Shen (2021), the coefficients are categorised into major and minor components. Here, $\hat {u}_{r}$, $\hat {w}_{i}$ and $\hat {\tilde {p}}_{r}$ are the major components that are present even in the inviscid interfacial waves, and the minor components arise due to the viscosity and the interaction with Eulerian current.

Figure 10. Vertical profiles of the first harmonic coefficients of $u$, $w$ and $p$ in the AW-noturb case: (a) profiles of the major parts; and (b) profiles of the minor parts. The profile of $\hat {u}_{i}$ is shown in both panels. The vertical axis is logarithmic for $|z^*/2{\rm \pi} |\geq 10^{-2}$ and linear for $|z^*/2{\rm \pi} |< 10^{-2}$.

The harmonic coefficient profiles consist of two parts, namely, the SBL and the bulk region. The SBL is the region near the interface where an oscillatory response to the boundary condition is present, and its e-folding thicknesses in the air and water sides are $\sqrt {2/ {\textit {Re}}_{a}}\approx 1.8\times 10^{-3}\times 2{\rm \pi}$ and $\sqrt {2/{\textit {Re}}_{o}}\approx 5.8\times 10^{-4}\times 2{\rm \pi}$, respectively. Figure 10 shows that the response to the boundary is visible to the extent approximately five times thicker than those values. Therefore, we loosely define the SBL of air and water as $0\leq z^*/2{\rm \pi} \leq 0.01$ and $-0.003\leq z^*/2{\rm \pi} \leq 0$, respectively, and the region outside them as the bulk region.

The water-side profile of $\hat {u}_{r,i}$ and $\hat {w}_{r,i}$ are mostly consistent with the irrotational waves: $u$ is in-phase and $w$ is 90$^\circ$ out-of-phase with $\eta$, and their minor components are nearly zero. In water-side SBL, $\hat {u}_{i}$ shows a slight deviation from zero, which is the response to satisfy the no-slip interfacial boundary condition, whereas $\hat {w}_{r}$ is nearly zero throughout the water side. The phase of $\tilde {p}$ is slightly ahead of $\eta$ (higher pressure at the forward face of the wave). In the air-side bulk region, the major components $\hat {u}_{r}$ and $\hat {w}_{i}$ agree with the irrotational waves. However, the minor components $\hat {u}_{i}$ and $\hat {w}_{r}$ show greater deviation than the water side. From figure 10, it can be seen that $\hat {u}_{r}\hat {w}_{r}+\hat {u}_{i}\hat {w}_{i}\approx 0$, so $u$ and $w$ are still in quadrature. In the air-side SBL, there is a strong SBL response due to the no-slip boundary condition and large density ratio $\rho _{o}/\rho _{a}$, resulting in a strong shear layer just above the interface. As in the water side, the phase of $\tilde {p}$ is slightly ahead of $\eta$.

The momentum flux profile of the AW-noturb case is shown in figure 11(a). In the water side, the net momentum flux $F_{p}+F_{v}+F_{a}$ is upwards with a maximum value at $z^*/2{\rm \pi} \approx -0.08$. Therefore, the horizontal momentum is transferred from the lower part to the near-surface part of the water body. This corresponds to the transformation of the horizontal momentum from the wave motion that is distributed over the vertical scale of $O(0.5)$, to the vortical current that diffuses downwards from the SBL. The upwards momentum transfer in the bulk of the water is performed via the form stress and the advection, and the viscous stress carries momentum downwards. Each term is decomposed into contributions from harmonics, and it is found that the following terms dominate the momentum flux terms:

(4.14a)$$\begin{gather} F_{p1} = 2 \mathrm{Re} [\hat{\tilde{p}}_{10}\hat{z}_{x^* 10}^\star], \end{gather}$$

(4.14b)$$\begin{gather}F_{v0} = \rho \hat{{\mathsf{T}}}_{00}^{xz},\ F_{v1} ={-}2 \rho \mathrm{Re} [\hat{{\mathsf{T}}}_{10}^{xx}\hat{z}_{x^* 10}^\star], \end{gather}$$

(4.14c)$$\begin{gather}F_{a1} = 2 \rho \mathrm{Re} [\hat{u}_{10}\hat{\omega}_{10}^\star]. \end{gather}$$

These terms are plotted in figure 11(a) as markers. The pressure form stress is caused by the relative phase shift of $\tilde {p}$ and $z$. Note that $z$ is in phase with $\eta$ by the model coordinate definition, so $\mathrm {Re} [\hat {\tilde {p}}_{10}\hat {z}_{x^* 10}^\star ]\propto \hat {\tilde {p}}_{i}\hat {\eta }_{r}$, and this correlation is consistent with that observed in figure 10. The viscous stress is composed of two components, out of which the downwards momentum transfer is supported by the phase-independent stress term $\hat {{\mathsf{T}}}^{xz}_{00}=\overline {{\mathsf{T}}^{xz}}$. This is an understandable result considering that the development of the vortical current is well-reproduced in the one-dimensional wave-averaged equation with the momentum imposed as the uniform virtual wave stress at the surface.

Figure 11. Vertical profiles of the momentum flux components defined in (4.8): (a,b) profiles of the AW-noturb and AW-ctrl cases, respectively; (a ii,b ii) the same quantities as in (a i,b i), but the range of the horizontal axis is magnified. The vertical axis is logarithmic for $|z^*/2{\rm \pi} |\geq 10^{-2}$ and linear for $|z^*/2{\rm \pi} |< 10^{-2}$.

In the air side, the momentum is transferred upwards from the near-interface layer to the bulk of the air. The upwards transfer of momentum roughly agrees with the viscous stress, where the phase-independent part $\hat {{\mathsf{T}}}^{xz}_{00}=\overline {{\mathsf{T}}^{xz}}$ plays a central role as in the water side. The pressure and advective momentum fluxes almost cancel each other.

The discussed results are compared with the momentum transfer profile in the presence of turbulence (AW-ctrl), as depicted in figure 11(b). In contrast to the AW-noturb case, the momentum is transferred downwards in the water side. This is because, over $600\leq t/2{\rm \pi} \leq 1200$ of the AW-ctrl case, the Eulerian current profile (figure 7e) developed deeper than the vertical extent of the wave momentum ($z^*\gtrsim -0.5$) due to the presence of turbulence. This is illustrated in the significant difference in $F_{a}$ from AW-noturb. Although the contribution from the first harmonic ($F_{a1}$) is almost unchanged, the turbulent flow structure that arises at other horizontal wavenumbers carries momentum downwards to change the direction of total advective momentum flux in the bulk of the water side. Due to the reduced vertical shear of the horizontal velocity, the downwards momentum transfer by viscosity ($F_{v}$) is also reduced. The pressure-induced momentum transfer is almost unchanged. Although somewhat mild, the change from AW-noturb is also visible in the air side especially away from the interface ($z^*/2{\rm \pi} \leq 0.1$). There, the upwards momentum transfer is intensified compared with the AW-noturb case, and this difference can be attributed to the advective flux. As in the water side, the contribution from the first harmonic is unchanged, and the turbulence influence at other components enhances the upwards momentum transfer.

In both the AW-ctrl and AW-noturb cases, the magnitude of net momentum transfer between air and water is much smaller than that within the water layer. Therefore, most of the horizontal momentum lost from the waves is received as the water-side vortical current that diffuses downwards from the SBL. Since the cross-$z^*$ velocity $\omega$ is zero at the interface by definition, the cross-interface momentum flux is done by the pressure and viscous stresses. At both the air and water sides, the pressure–slope correlation carries momentum upwards, whereas the viscous stress nearly cancels that. Although small, the net momentum flux across the interface was upwards in this case.

The profile of KE source terms and the KE tendency in the AW-noturb case is shown in figure 12(a). The profile of the dissipation differs significantly between the air and water sides. In the air side, strong dissipation occurs in the SBL, whereas the dissipation in the water side occurs over the bulk of the water. The vertically integrated dissipation in the air and water was $9.362\times 10^{-5}$ and $1.009\times 10^{-4}$, respectively. The dissipation term is decomposed into harmonics, and the contribution from the first harmonic, $D_1$ defined as follows, is found to account for most of the total dissipation:

(4.15)\begin{equation} D_1 = (2\rho/{\textit{Re}})\overline{\left(|\hat{S}^{xx}_{10}|^2+2|\hat{S}^{xz}_{10}|^2+|\hat{S}^{zz}_{10}|^2\right)}. \end{equation}

The vertical profile of the PE conversion is less straightforward to interpret, but their vertically integrated value in the air and water is $2.22\times 10^{-7}$ and $-9.80\times 10^{-5}$, respectively. The PE-to-KE conversion in the water is concentrated in the SBL, as the integral of $C$ over $-0.005\leq z^*/2{\rm \pi} \leq 0$ accounts for 98.4 % of the total. The tendency of KE ${\textrm {d} (\overline {hE})}/{\textrm {d} t^*}$ mostly consists of water-side contribution due to the large density ratio, and its vertical structure corresponds to the wave orbital motion. Vertically integrated KE tendency in the air and water is $-9.81\times 10^{-8}$ and $-9.72\times 10^{-5}$, respectively. The total energy loss (vertically integrated $-C-{\partial (hE)}/{\partial t ^*}$) in the air and water is $-1.24\times 10^{-7}$ and $1.95\times 10^{-4}$, respectively. Therefore, most of the energy lost during the wave attenuation originates from the water side, but approximately half of it is transferred to and dissipated in the air-side SBL. Considering the relative contribution of energy dissipation in the air and water (1.3), the fraction above would increase with higher Reynolds number, i.e. longer waves.

Figure 12. Vertical profiles of the dissipation and PE conversion terms defined in (4.10): (a,b) profiles of the AW-noturb and AW-ctrl cases, respectively; (a ii,b ii) the same quantities as in (a i,b i), but the range of the horizontal axis is magnified. Note that the positive values correspond to the KE sink and vice versa. The vertical axis is logarithmic for $|z^*/2{\rm \pi} |\geq 10^{-2}$ and linear for $|z^*/2{\rm \pi} |< 10^{-2}$.

The KE flux terms in the AW-noturb case are shown in figure 13(a). In the air side, the pressure flux term carries KE downwards to the air-side SBL, where strong dissipation occurs. Due to the large density ratio, the viscous and advective flux terms contribute little to the total KE flux. In the bulk of the water, KE is transferred upwards by the pressure term and downwards by the viscosity term, resulting in a net downwards flux. In the near-surface water, however, the downwards viscous energy flux sharply approaches zero, resulting in a net upwards energy flux. This supplies energy to the air-side SBL, where the energy is strongly dissipated. Although most of the energy flux is explained with pressure and viscous terms, advective energy flux is also present in both domains, carrying energy towards the interface. All of these flux terms are mostly explained with the first-harmonic contributions defined as follows:

(4.16a)$$\begin{gather} W_{p1}=2\mathrm{Re}\left[\hat{\tilde{p}}_{10}\hat{w}_{10}^\star\right], \end{gather}$$

(4.16b)$$\begin{gather}W_{v1}={-}2\rho \mathrm{Re}\left[\hat{u}_{10}{\hat{{\mathsf{T}}}_{10}^{xz\star}}\right], \end{gather}$$

(4.16c)$$\begin{gather}W_{a1}=2\mathrm{Re} \left[\hat{\omega}_{10}\hat{E}_{10}^\star\right]. \end{gather}$$

Figure 13. Vertical profiles of the energy flux components defined in (4.10): (a,b) profiles of the AW-noturb and AW-ctrl cases, respectively, (a ii,b ii) the same quantities as in (a i,b i), but the range of the horizontal axis is magnified. The vertical axis is logarithmic for $|z^*/2{\rm \pi} |\geq 10^{-2}$ and linear for $|z^*/2{\rm \pi} |< 10^{-2}$.

The KE budget equation terms for the AW-ctrl case are shown in figures 12(b) and 13(b). Unlike horizontal momentum, the KE budget profile of the AW-ctrl case is very similar to the AW-noturb case and is mostly explained with the first harmonic contributions. Therefore, the major energetics of the attenuating interfacial waves of $\lambda \approx 1$ m is dominated by the laminar wave dynamics, even in the presence of wave-induced turbulence. Nevertheless, some differences can be seen in the advective flux in the water side. Although the first harmonic $W_{a1}$ still carries KE upwards, the contributions from other components carry KE downwards, resulting in $W_{a1}<0$ in the bulk of the water.

4.4. Comparison of TKE

In order to illustrate the role of the air–water coupling in turbulence production and to investigate its reproducibility with the wave-averaged framework, two additional simulations were conducted. The new simulations, named AW-CL and W-CL, are replications of the AW-ctrl and W-ctrl cases using the CL equation and virtual wave stress as the boundary condition. We focus on the turbulence in the water side, so they were conducted in the rigid-lid water-side domain $-H_{o}\leq z\leq 0$, $\eta =0$. The CL vortex force term with the prescribed Stokes drift $(u^{ {\textit {St}}}(z,t),0,0)$ was added to the governing equation:

(4.17a)$$\begin{gather} \dfrac{\partial u}{\partial{t}}+u\dfrac{\partial u}{\partial{x}}+ v\dfrac{\partial u}{\partial{y}}+w\dfrac{\partial u}{\partial{z}}={-}\dfrac{\partial p}{\partial{x}} +\frac{1}{{\textit{Re}}_{o}}\left(\dfrac{\partial^2u}{\partial{x}^2}+\dfrac{\partial^2u}{\partial{y}^2}+\dfrac{\partial^2u}{\partial{z}^2}\right), \end{gather}$$

(4.17b)$$\begin{gather}\dfrac{\partial v}{\partial{t}}+u\dfrac{\partial v}{\partial{x}}+ v\dfrac{\partial v}{\partial{y}}+w\dfrac{\partial v}{\partial{z}}={-}\dfrac{\partial p}{\partial{y}} +\frac{1}{{\textit{Re}}_{o}}\left(\dfrac{\partial^2 v}{\partial{x}^2}+\dfrac{\partial^2 v}{\partial{y}^2}+\dfrac{\partial^2 v}{\partial{z}^2}\right)-u^{{\textit{St}}}\left(\dfrac{\partial v}{\partial{x}}-\dfrac{\partial u}{\partial{y}}\right), \end{gather}$$

(4.17c)$$\begin{gather}\dfrac{\partial w}{\partial{t}}+u\dfrac{\partial w}{\partial{x}}+ v\dfrac{\partial w}{\partial{y}}+w\dfrac{\partial w}{\partial{z}}={-}\dfrac{\partial p}{\partial{z}} +\frac{1}{{\textit{Re}}_{o}}\left(\dfrac{\partial^2w}{\partial{x}^2}+\dfrac{\partial^2w}{\partial{y}^2}+\dfrac{\partial^2w}{\partial{z}^2}\right)+u^{{\textit{St}}}\left(\dfrac{\partial u}{\partial{z}}-\dfrac{\partial w}{\partial{x}}\right). \end{gather}$$

Here, $p$ is the generalised pressure that includes both the non-hydrostatic pressure and the Bernoulli head effect and was calculated to retain incompressibility. The horizontal and bottom boundary conditions are the same as the wave-resolving simulations. The dynamical boundary condition at $z=0$ is the prescribed virtual wave stress ((1.2) and (1.4)):

(4.18)\begin{equation} \left.\frac{1}{{\textit{Re}}_{o}}\dfrac{\partial u}{\partial{z}}\right|_{z=0} = \begin{cases} \tau^{vws}_{ao}(t) & \text{(AW-CL)}, \\ \tau^{vws}_{o}(t) & \text{(W-CL)}, \end{cases} \quad \left.\frac{1}{{\textit{Re}}_{o}}\dfrac{\partial v}{\partial{z}}\right|_{z=0} = 0. \end{equation}

Here, the temporal dependence of virtual wave stress is allowed to reflect the attenuating wave amplitude $a(t)$. The vertical structure of the Stokes drift is provided with the linear surface wave solution:

(4.19)\begin{equation} u^{{\textit{St}}}(z,t)=\frac{[a(t)]^2}{2\sinh^2H_{o}}\cosh 2(z+H_{o}). \end{equation}

The temporal decay rate of amplitude is provided with the linear theory ((1.1) and (1.3)):

(4.20)\begin{equation} a(t)= \begin{cases} a(0){\rm e}^{-\gamma_{ao}t} & \text{(AW-CL)}, \\ a(0){\rm e}^{-\gamma_{o}t} & \textrm{(W-CL)}. \end{cases} \end{equation}

The initial wave amplitude is $a(0)=0.1$. The simulation was started using the same Gaussian noise as added in the AW-ctrl and W-ctrl cases, and the simulation is conducted until $t=1200{\rm \pi}$ with $\Delta t=2{\rm \pi} /60$. Overall, the flow field obtained in the wave-averaged cases resembled the wave-filtered field of the corresponding wave-resolving cases. To quantitatively compare the temporal growth of the flow field, the variance of the turbulent velocity $u'$, $ v '$, $w'$ is examined in the following.

Figure 14 shows the temporal evolution of $\overline {u^{\prime 2}}$, $\overline { v ^{\prime 2}}$ and $\overline {w^{\prime 2}}$, averaged over $-0.4\leq z^*\leq 0$. Each case shows the accelerating growth of variances over $0\leq t\leq 300$, suggesting some instability mechanism at work. The comparison of the AW-ctrl and W-ctrl cases illustrates that the presence of a coupled air layer intensifies the wave-induced water turbulence. The velocity variance in the initial growth stage of the AW-ctrl and W-ctrl cases clearly differ: the variance grows quicker and reaches a greater value in AW-ctrl. This is consistent with the fact that a stronger Eulerian mean drift was seen in the air–water coupled case than in the water-only case (figure 7). Once the initial instability saturates at $t/2{\rm \pi} \approx 500$, the time series exhibits more chaotic behaviour. Thereafter, each variance component of AW-ctrl is clearly greater than the corresponding value in W-ctrl.

Figure 14. Temporal evolution of the water-side velocity variance, (a) $\overline {u^{\prime 2}}$, (b) $\overline { v ^{\prime 2}}$, (c) and $\overline {w^{\prime 2}}$, averaged over $-0.4\leq z^*\leq 0$ of the AW-ctrl, W-ctrl, AW-CL and W-CL cases. Solid lines show the AW-ctrl and AW-CL cases and dashed lines show the W-ctrl and W-CL cases. Blue lines show wave-resolving simulation results, and the grey lines show wave-averaged simulation results.

Comparison between the wave-averaged cases and their corresponding wave-resolving cases illustrates the reproducibility of the simulated wave-induced turbulence in the wave-averaged dynamics. In the initial growth phase ($0\leq t/2{\rm \pi} \leq 300$), the wave-averaged cases very well trace the corresponding wave-resolving cases. After the saturated phase ($t/2{\rm \pi} \approx 500$), the wave-averaged cases show deviation from the wave-resolving cases due to the chaotic behaviour of the mature flow. However, the spread of the time series of the AW-ctrl/CL pair overlaps each other and is distinct from the W-ctrl/CL pair, which is especially visible in the time series of $\overline {u^{\prime 2}}$. Therefore, we conclude that the simulated wave-induced turbulence can be successfully reproduced using the CL equation and the virtual wave stress as the imposed boundary condition.

5. Discussion and conclusion

To clarify the role of air–water coupling in WL turbulence production, a wave-resolving DNS of the air–water interfacial wave has been conducted and compared with other simulation set-ups such as a free surface wave-resolving DNS. At the bottom of the air, a very sharp SBL develops, where a significant amount of KE is dissipated. As a result, the wave attenuation rate is enhanced compared with the water-only case. Even in the presence of WL turbulence, the attenuation rate was well-predicted by the linear theory of Dore (1978).

The simulation results suggest that the WL turbulence is produced by the CL2 instability mechanism under the Eulerian mean drift induced by the horizontal momentum supplied from the attenuated wave. This conclusion qualitatively aligns with the preceding studies, such as Tsai et al. (2017) and Fujiwara et al. (2020), but the resulting turbulence was intensified due to the enhanced momentum transfer from the wave. The momentum and energy budget analysis revealed the detailed flow of these quantities: although a significant fraction of energy lost from the water is transferred to the air and dissipated there, the momentum of wave motion is confined within the water. The present understanding is crucial for quantitatively interpreting laboratory results and extrapolating them to the field scale, as the water surface is constantly in contact with air in reality.

Among many cases conducted in the tank experiments of Savelyev et al. (2012), there are a series of cases with $k=6.32\,{\rm m}^{-1}$ ($\lambda =0.99\,{\rm m}$), which is close to the parameter choice of the present simulations ($\lambda \approx 1\,{\rm m}$). The experimental spanwise wavenumber $l$ is evaluated as in Tsai et al. (2017), $l=\lambda /L_{eddy}$, where $\lambda$ is wavelength and $L_{eddy}$ is the eddy size provided in Savelyev et al. (2012). Here $L_{eddy}$ was measured at the initial stage of vortex formation, so $l$ is compared with the maximum value of $l_s$ (figure 9), which reflects the characteristic scale of growing instability. In the AW-ctrl and W-ctrl cases ($ak=0.1$), we obtained $l_s=15$ and $14$, respectively. In Savelyev et al. (2012), the case with $ak=0.136$ led to $l=13.1$. Although it would be safe to say that the laboratory and numerical experiments roughly agree, the interpretation of the values provided is not so straightforward because of the difference in experiment parameters and evaluation methods.

In the linear stability analysis conducted by Tsai et al. (2017), the only non-dimensional number characterising the problem was the Langmuir number

(5.1)\begin{equation} \textrm{La} = \frac{\nu_{o}^{3/2}}{a\sigma^{1/2}k^{{-}1}u_*},\end{equation}

where $u_*=(\tau ^{vws}/\rho _{o})^{1/2}$ is the water-side friction velocity associated with virtual wave stress. They used the water-side only virtual wave stress ($\tau ^{vws}=\tau ^{vws}_{o}$) and obtained $\textrm {La}=\nu _{o}/2^{1/2}a^2\sigma$. However, when interpreting the laboratory experiment result, the virtual wave stress based on the theory of Dore (1978, i.e. $\tau ^{vws}=\tau ^{vws}_{ao}$) would be more appropriate. As a result,

(5.2)\begin{equation} \textrm{La} = \frac{\nu_{o}}{2^{1/2}a^2\sigma} \left[1 + \frac{\rho_{a}}{\rho_{o}}\frac{{\textit{Re}}_{o}}{(2{\textit{Re}}_{a})^{1/2}}\right]^{{-}1/2}\end{equation}

is obtained. Assuming the material parameters at $10\,^\circ {\rm C}$ (§ 1) and wavelength $\lambda =140$ cm, the Langmuir number is reduced by 36 % compared with the water-only case, and the reduction is greater for longer waves. This modification of the Langmuir number somewhat reduces the scatter of the experimental data by Savelyev et al. (2012) shown in figure 9 of Tsai et al. (2017). When the Langmuir number is evaluated as (5.1), the correlation coefficient between $-\log _{10}(\textrm {La})$ and $l$ is 0.45, and when (5.2) is used instead, it increases to 0.51.

Consequently, a physically plausible approach to synthesise the parameterisations for LCs and WL turbulence is to use the wave-averaged large-eddy simulation based on the CL equation with the surface wind stress incremented by the virtual wave stress. Many of the present parameterisations that incorporate mixing by LCs are based on this framework without virtual wave stress and employ the friction velocity $u_*\equiv (\tau /\rho _{o})^{1/2}$ to characterise the shear of the Eulerian current (e.g. Li et al. 2019). Therefore, the effect of virtual wave stress can be easily incorporated into these parameterisations by increasing $u_*^2$ by $\tau ^{vws}_{ao}/\rho _{o}$.

For the estimation of virtual wave stress under a realistic wind-wave spectrum, more understanding of the energy dissipation mechanism is required. One important issue remaining to be solved is the turbulence transition of the air-side Stokes layer. One of the state-of-the-art wave model source function packages (Ardhuin et al. 2010) assumes that such a transition occurs when the air-side significant Reynolds number $2u_{{orb},s}H_s/\nu _{a}$, where $u_{{orb},s}$ is the significant surface orbital velocity and $H_s$ is the significant wave height, exceeds a certain limit value. Once the turbulence transition occurs, wave energy will be dissipated more efficiently, and the virtual wave stress will be further increased. In our wave-resolving DNS, such a transition could not be observed due to the smallness of the Reynolds number, so a further experimental or computational investigation of this boundary layer under a higher Reynolds number is necessary. Even without turbulence transition, the large-amplitude waves can trigger other effects, such as the generation of parasitic capillary waves and microbreaking (e.g. Tsai & Hung 2007; Deike, Popinet & Melville 2015), modulational instability interacting with currents Li & Chabchoub (2024) and so on. Such finite-amplitude effects should be explored further in the future. If the linear laminar theory of Dore (1978) remains applicable and the deep-water dispersion relation is assumed, the virtual wave stress can be estimated from a wave variance density spectrum $E(\sigma,\theta )$ following

(5.3)\begin{equation} (\tau^{vws}_x, \tau^{vws}_y) = \iint G(\sigma)E(\sigma, \theta)(\cos \theta, \sin\theta) \,{\rm d} \theta \,{\rm d} \sigma, \end{equation}

where the virtual wave stress transfer function $G(\sigma )$ is defined as follows:

(5.4)\begin{equation} G(\sigma) = 2\sigma^2\left[\frac{2}{{\textit{Re}}_{o}}\rho_{o} + \left(\frac{2}{{\textit{Re}}_{a}}\right)^{1/2}\rho_{a}\right] = \frac{4\rho_{o}\nu_{o}}{g^2}\sigma^5 + \frac{2\sqrt{2}\rho_{a} \nu_{a}^{1/2}}{g}\sigma^{7/2}. \end{equation}

For reference, with the physical parameters at 10 $^\circ$C, $G(\sigma )$ is $1.4\times 10^{0}$, $7.0\times 10^{-3}$ and $9.5\times 10^{-4}$ N m$^{-4}$ for 1-, 4- and 7-second-period waves, respectively. With a 7-second-period swell with one-side amplitude of 1 m, virtual wave stress would be $4.75\times 10^{-4}$ N m$^{-2}$. Therefore, the influence of additional momentum flux via virtual wave stress is well below a typical wind stress value unless the turbulence transition occurs. Nevertheless, its effect is worth exploring in the future because the CL2 instability efficiently produces near-surface turbulence even under slack conditions, potentially influencing phenomena such as the sea-surface skin temperature.