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  • View in gallery

    Schematic illustration of the CAWOMS. Here u* is friction velocity and T, q, and p are atmospheric low-level temperature, specific humidity, and pressure, respectively; QS,sp and QL,sp are sea spray sensible and latent heat fluxes, respectively.

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    The initial vertical potential temperature (solid line with markers) and salinity (dashed line with *) profiles for the 12-h spinup run for (a) all half-sigma levels of the ocean model and (b) the upper 500 m only. Here + corresponds to the profile with MLD of 40 m, while ○ and □ correspond to the profiles with MLDs of 80 and 120 m, respectively.

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    Time series of the simulated (a) min SLP, (b) max 10-m wind, (c) max SWH, and (d) min SST for each experiment.

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    The simulated 45-h SLP (contours every 4 hPa) and 10-m wind vector and speed (shading at the interval of 5 m s−1) for experiments (a) CTRL, (b) CPLAW, (c) CPLAO, and (d) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

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    Vertical cross section passing through the TC center and the location of maximum surface wind of potential temperature (contours every 5°C) and horizontal wind speed (shading at an interval of 5 m s−1) valid at 45 h of the control run.

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    As in Fig. 4, but for SWH (contours every 2 m) and mean wave direction (vectors).

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    The simulated 45-h (left) SST (contours every 1°C), (middle) MLD (contours every 20 m), and (right) HHC (contours every 2 × 108 J m−2) for experiments (top to bottom) CTRL, CPLAW, CPLAO, and CPLAWO. Shadings in (c) and (f) indicate SSTs in those areas are <26°C. The dashed line with + signs in each panel is the simulated 3-hourly storm track for the corresponding experiment.

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    Evolutions of vertical potential temperature (°C) structure at point A in Fig. 7 for experiments (a) CTRL, (b) CPLAW, (c) CPLAO, and (d) CPLAWO.

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    The simulated 45-h SSC vector (filled arrow) and speed (shading interval of 0.1 m s−1) for experiments (a) CTRL, (b) CPLAW, (c) CPLAO, and (d) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

  • View in gallery

    The simulated (top) 45-h friction velocity (m s−1), (middle) sea surface roughness (m), and (bottom) wave age for experiments (left) CTRL and (right) CPLAW, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

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    The simulated 45-h total (left) sensible heat and (right) latent heat fluxes (W m−2) for experiments (top to bottom) CTRL, CPLAW, CPLAO, and CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

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    The simulated 45-h (top to bottom) direct sensible heat flux, direct latent heat flux, sea spray sensible heat flux, and sea spray latent heat flux for experiments (left) CPLAW and (right) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment. Unit is W m−2.

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    The simulated 45-h dissipative heating (W m−2) for experiments (a) CPLAW and (b) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

  • View in gallery

    Time series of the simulated (top) min SLP and (bottom) max 10-m wind for experiments with (left and right) different initial MLDs. CPLAO80 and CPLAWO80 are the same as CPLAO and CPLAWO except the initial MLD is 80 m instead of 40 m, while CPLAO120 and CPLAWO120 have the initial MLD of 120 m.

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A Coupled Atmosphere–Wave–Ocean Modeling System: Simulation of the Intensity of an Idealized Tropical Cyclone

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  • 1 Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina
  • | 2 Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
  • | 3 Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina, and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
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Abstract

A coupled atmosphere–wave–ocean modeling system (CAWOMS) based on the integration of atmosphere–wave, atmosphere–ocean, and wave–current interaction processes is developed. The component models consist of the Weather Research and Forecasting (WRF) model, the Simulating Waves Nearshore (SWAN) model, and the Princeton Ocean Model (POM). The coupling between the model components is implemented by using the Model Coupling Toolkit. The CAWOMS takes into account various wave-related effects, including wave state and sea-spray-affected sea surface roughness, sea spray heat fluxes, and dissipative heating in atmosphere–wave coupling. It also considers oceanic effects such as the feedback of sea surface temperature (SST) cooling and the impact of sea surface current on wind stress in atmosphere–ocean coupling. In addition, wave–current interactions, including radiation stress and wave-induced bottom stress, are also taken into account. The CAWOMS is applied to the simulation of an idealized tropical cyclone (TC) to investigate the effects of atmosphere–wave–ocean coupling on TC intensity. Results show that atmosphere–wave coupling strengthens the TC system, while the thermodynamic coupling between the atmosphere and ocean weakens the TC as a result of the negative feedback of TC-induced SST cooling. The overall effects of atmosphere–wave–ocean coupling on TC intensity are determined by the balance between wave-related positive feedback and the negative feedback attributable to TC-induced SST cooling.

Corresponding author address: Lian Xie, NCSU/MEAS, P.O. Box 8208, Raleigh, NC 27695-8208. Email: xie@ncsu.edu

Abstract

A coupled atmosphere–wave–ocean modeling system (CAWOMS) based on the integration of atmosphere–wave, atmosphere–ocean, and wave–current interaction processes is developed. The component models consist of the Weather Research and Forecasting (WRF) model, the Simulating Waves Nearshore (SWAN) model, and the Princeton Ocean Model (POM). The coupling between the model components is implemented by using the Model Coupling Toolkit. The CAWOMS takes into account various wave-related effects, including wave state and sea-spray-affected sea surface roughness, sea spray heat fluxes, and dissipative heating in atmosphere–wave coupling. It also considers oceanic effects such as the feedback of sea surface temperature (SST) cooling and the impact of sea surface current on wind stress in atmosphere–ocean coupling. In addition, wave–current interactions, including radiation stress and wave-induced bottom stress, are also taken into account. The CAWOMS is applied to the simulation of an idealized tropical cyclone (TC) to investigate the effects of atmosphere–wave–ocean coupling on TC intensity. Results show that atmosphere–wave coupling strengthens the TC system, while the thermodynamic coupling between the atmosphere and ocean weakens the TC as a result of the negative feedback of TC-induced SST cooling. The overall effects of atmosphere–wave–ocean coupling on TC intensity are determined by the balance between wave-related positive feedback and the negative feedback attributable to TC-induced SST cooling.

Corresponding author address: Lian Xie, NCSU/MEAS, P.O. Box 8208, Raleigh, NC 27695-8208. Email: xie@ncsu.edu

1. Introduction

Tropical cyclones (TCs) are intense cyclonic atmospheric vortices originated in warm tropical oceans. They are strongly coupled to ocean mixed layer and surface waves through momentum, heat, and moisture exchanges at the air–sea interface. In a TC system, the atmospheric forcing drives sea surface waves and underlying ocean currents, while the energy for a TC to maintain or strengthen its intensity comes mainly from the ocean through air–sea heat and moisture fluxes. The high winds and waves in a TC condition enhance upper-ocean turbulent mixing, cool sea surface temperature (SST), and result in a cold wake behind (Price 1981), which, in turn, provides a negative feedback on TC intensity (Schade and Emanuel 1999; Chan et al. 2001). The existence of sea surface waves and sea spray modifies the structures of both the atmospheric and marine boundary layers, and thus influences air–sea momentum and heat fluxes. Therefore, in order to better understand the processes that contribute to TC intensity, it is necessary to employ a coupled atmosphere–wave–ocean modeling system, which considers various air–sea interaction processes valid from low to high wind conditions (Chen et al. 2007).

In traditional uncoupled atmospheric modeling studies, SST is usually specified by observations or results from an ocean model. In uncoupled ocean models, oceanic responses are driven by prescribed atmospheric forcing derived from observations or from a stand-alone atmospheric model. No feedback is considered in uncoupled atmospheric or oceanic models. Oceanic effects on TC intensity include both positive and negative contributions. Warmer SST associated with large ocean heat content causes TC intensification (Hong et al. 2000; Shay et al. 2000; Bright et al. 2002), whereas negative SST anomalies associated with cold-core eddies or TC-induced cold wake weakens TC systems (Walker et al. 2005). Thus, two-way coupled atmosphere–ocean models are needed to take into account oceanic feedback effects on TC intensity. By using a simple coupled atmosphere–ocean model, Schade and Emanuel (1999) found that the feedback of SST cooling could significantly reduce TC intensity. Their results are consistent with early coupled atmosphere–ocean modeling studies (e.g., Bender et al. 1993). Chan et al. (2001) also pointed out that TC intensity is sensitive to SST, and ocean vertical temperature structure plays a significant role in modulating TC intensity. More recently, three-dimensional (3D) coupled atmosphere–ocean research and operational models have been developed to consider atmosphere–ocean interaction in TC systems (Bao et al. 2000; Bender and Ginis 2000; Bender et al. 2007). Although some hurricane–ocean coupled models adopted one-dimensional (1D) ocean models (Emanuel et al. 2004; Lin et al. 2005; Bender et al. 2007; Davis et al. 2008), recent studies suggest 3D ocean models are more appropriate since 1D ocean models tend to underestimate the TC-induced sea surface cooling (Yablonsky and Ginis 2009).

TCs not only induce sea surface cooling, but the strong winds associated with a TC also generate large sea surface waves, which affect sea surface roughness and air–sea momentum, heat, and moisture fluxes. Although the widely used classical Charnock relation (Charnock 1955) implicitly expresses the surface wave effect on air–sea momentum flux, wave state (such as wave age) has also been shown to have important impacts on wind stress (Toba et al. 1990; Donelan 1990; Johnson et al. 1998; Drennan et al. 2003). More recently, field and laboratory observations (Alamaro et al. 2002; Powell et al. 2003; Donelan et al. 2004; French et al. 2007) showed that the drag coefficient approaches a limiting value and even decreases with increasing wind speed under high wind conditions. This is likely due to the existence of sea spray (Powell et al. 2003), as well as flow separation induced by wave breaking, which causes the airflow to not “see” the troughs of the waves and skips from breaking crest to breaking crest (Donelan et al. 2004). Sea spray produced by surface wave breaking and wind tearing wave crests have significant impacts on both the air–sea momentum flux (e.g., Makin 2005) and the air–sea heat and moisture fluxes (e.g., Andreas et al. 1995). Since sea spray is mainly generated from surface wave breaking, sea spray generation function (SSGF) and sea spray heat flux are dependent upon wave state (Chaen 1973; Piazzola et al. 2002; Zhao et al. 2006). Coupled atmosphere–wave modeling studies have been conducted to investigate the impacts of wave-state-dependent air–sea fluxes on TC systems (e.g., Bao et al. 2000; Doyle 2002), large-scale circulation (e.g., Perrie and Zhang 2001; Weisse and Schneggenburger 2002), and extratropical cyclones (e.g., Doyle 1995; Lionello et al. 1998; Powers and Stoelinga 2000). Recent studies also took into account the effects of sea spray heat fluxes on atmospheric systems (e.g., Fairall et al. 1994; Kepert et al. 1999; Wang et al. 2001; Andreas and Emanuel 2001; Perrie et al. 2004; Zhang et al. 2006; Gall et al. 2008) and found that sea spray modifies air–sea fluxes and atmospheric structures in important but complex ways. However, in previous coupled atmosphere–wave modeling studies, the effects of wave state and sea spray have not been considered simultaneously. Another important issue under high winds is the atmospheric dissipative heating. Previous studies (Bister and Emanuel 1998; Zhang and Altshuler 1999; Businger and Businger 2001) have shown that taking into account the dissipative heating increases TC intensity by 10%–20% as measured in maximum surface wind speed. As it depends on surface friction under the impact of wave state and sea spray, dissipative heating should also be included in the coupled modeling system, particularly when concerning TC systems.

As for wave–current interaction (though it does not impact TC intensity directly), the modified wave parameters and ocean temperature, current, and turbulent structures would modify air–sea fluxes and influence the TC system. Xie et al. (2001) developed a wave–current coupled system that takes into account wave-state-dependent surface wind stress, two-dimensional (2D; depth averaged) radiation stress, and wave-enhanced bottom stress. Mellor (2003, 2008) derived the equations for depth-dependent wave–current interaction, pointing out that a 3D radiation stress that varies with water depth should be included in wave–current coupled models. Xie et al. (2008) and Liu and Xie (2009) extended their wave–current coupled modeling system to incorporate depth-dependent radiation stress and surface and bottom shear stresses, as well as wetting and drying. Their results show that both wave parameters and storm surge are changed when wave–current interaction is included. Fan et al. (2009) constructed a coupled wind–wave–ocean model by using a TC wind model to provide wind fields to a coupled wave–current model, and found that wind–wave–current interaction has significant impact on air–sea momentum flux and ocean response in TCs.

Although extensive studies on atmosphere–ocean, atmosphere–wave, and wave–ocean interactions have been carried out, a fully coupled air–sea modeling system integrating all of the above air–sea interaction processes, as well as the effects of sea spray, has yet to be developed. In this study, we aim to establish a coupled atmosphere–wave–ocean modeling system (CAWOMS) capable of simulating coupled atmospheric, wave, and oceanic processes by integrating our wave–current interaction model (Xie et al. 2001, 2008; Liu and Xie 2009), atmosphere–wave interaction model (Liu et al. 2008; Xie et al. 2009), and the air–sea–wave coupling framework presented by Xie et al. (2009). Parameterizations of air–sea fluxes applicable to a range of low to high wind conditions, sea spray heat fluxes, dissipative heating, SST feedbacks, and wave–current interaction are considered in the CAWOMS. To investigate the effects of atmosphere–wave–ocean coupling on TCs, several experiments are designed to simulate the intensity change of a TC system in an idealized setting by using the CAWOMS. The rest of this paper is organized as follows. Section 2 gives a detailed description of the CAWOMS, including various model components and the atmosphere–wave–ocean interaction processes. Section 3 describes the idealized TC, model settings, and experiment design. The results and corresponding analyses are presented in section 4. Finally, summary and conclusions are given in section 5.

2. Description of the CAWOMS

a. Model components and coupling tool

The CAWOMS consists of an atmospheric model, the Weather Research and Forecasting (WRF) model (Skamarock et al. 2007); an ocean circulation model, the Princeton Ocean Model (POM; Mellor and Blumberg 1985); and a sea surface wave model, the Simulating Waves Nearshore (SWAN) model (Booij et al. 1999). The model components are coupled to each other through the Model Coupling Toolkit (MCT; Larson et al. 2005; Jacob et al. 2005), which was used as the basis for the Community Climate System Model coupler (Craig et al. 2005) as well as the coupler for the Regional Ocean Modeling System (Warner et al. 2008).

A schematic illustration depicting the CAWOMS is given in Fig. 1. The couplings among atmosphere, wave, and ocean model components are taken into account through air–sea interaction processes at the air–sea interface. Air–sea momentum flux is estimated by including the effects of sea state, sea spray, and sea surface current (SSC). Wave-state-dependent sea spray heat fluxes and the SST simulated by the ocean model are considered in the estimation of total air–sea sensible and latent heat fluxes. WRF drives POM and SWAN through atmospheric forcing and provides environment variables such as temperature, specific humidity, surface pressure, and friction velocity to estimate sea spray heat fluxes. SWAN provides wave parameters for determining wave state, which influences sea surface roughness parameters as well as sea spray heat fluxes. POM provides SST to the coupled system for the estimations of air–sea heat fluxes and sea spray heat fluxes. Additionally, wave–current interaction is considered by adding wave-induced 2D and/or 3D radiation stress and wave-induced bottom stress to POM and considering the Doppler effect of current on waves in SWAN. Moreover, the dissipative heating in the surface layer is also taken into account in the CAWOMS. Details of the model components, the parameterization of air–sea interaction processes, and the coupling procedure can be found in Xie et al. (2009) and are briefly described below for convenience.

The atmospheric WRF model with the Advanced Research WRF (ARW) core used in the CAWOMS is a fully compressible, nonhydrostatic numerical weather prediction model suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. WRF-ARW utilizes Arakawa-C grid, and it uses a terrain-following hydrostatic pressure coordinate system in the vertical. WRF incorporates various physical processes including microphysics, cumulus parameterization, planetary boundary layer, surface layer, land surface, and longwave and shortwave radiations, with several options available for each process. More details about the WRF model are referred to Skamarock et al. (2007) and Wang et al. (2007).

The SWAN wave model used in the CAWOMS is a third-generation wave model. It solves the wave action balance equation in both spatial and spectral spaces. SWAN considers various source and sink terms including wind input, quadruplet wave–wave interaction, and dissipation terms. In shallow water, the effects of triad wave interaction and depth-induced wave breaking are also included in SWAN. Wave characteristics such as significant wave height (SWH) can be obtained from the wave action (spectrum) in the governing equation.

The POM ocean model used in the CAWOMS is a three-dimensional, primitive equation model using the Arakawa-C grid staggering and a sigma coordinate in the vertical. The turbulence closure scheme employed in POM is the Mellor–Yamada scheme (Mellor and Yamada 1982). In addition, wave-enhanced upper-ocean turbulent mixing due to wave breaking (Craig and Banner 1994) was incorporated into the Mellor–Yamada turbulence closure scheme by Mellor and Blumberg (2004), in which the effect of wave breaking on upper-ocean turbulent mixing is considered by specifying a wave-enhanced upper boundary condition depending on friction velocity. POM has been widely used in applications from estuaries to coastal ocean circulation as well as operational ocean forecasting.

b. Atmosphere–wave coupling

The atmosphere and ocean surface waves are coupled by considering the effects of wave state and sea spray on air–sea momentum and heat fluxes as well as the atmospheric low-level dissipative heating. A wave-age- and sea-spray-dependent parameterization of sea surface roughness, applicable to both low and high wind conditions, is utilized to estimate sea surface wind stress. Wave-state-affected sea spray heat fluxes, as well as atmospheric low-level dissipative heating, which are important especially under high winds, are taken into account in the atmosphere–wave coupling processes.

Air–sea momentum flux is usually estimated through the Charnock relation (Charnock 1955) gz0/u*2 = α, where g is gravity, z0 is the sea surface aerodynamic roughness, u* is the friction velocity, and α is the Charnock constant. A choice of 0.0185 (Wu 1980) for the Charnock constant was widely used in atmospheric, oceanic, and surface wave models. The classical Charnock relation does not explicitly consider the wave state effects on sea surface roughness, though it has been commonly recognized that wave state has an important impact on air–sea momentum flux (Toba et al. 1990; Donelan 1990; Johnson et al. 1998; Drennan et al. 2003). Furthermore, recent studies (Alamaro et al. 2002; Powell et al. 2003; Donelan et al. 2004; Makin 2005) show that the classical Charnock relation is not applicable to high wind conditions. Considering both wave state and sea spray effects on sea surface wind stress, Liu et al. (2008) obtained a parameterization of sea surface aerodynamic roughness applicable to both low-to-moderate and high winds by combining the Scientific Committee on Oceanic Research (SCOR) relation (Jones and Toba 2001) with the resistance law of Makin (2005):
i1520-0493-139-1-132-e1
where β* = cp/u* is the wave age in which cp is the phase speed at the peak of the wave spectrum, and ω = min(1, acr/κu*) is the correction parameter indicating the influence of sea spray on the logarithmic wind profile in which κ is the Karman constant and acr = 0.64 m s−1 is the critical value of terminal fall velocity of the droplets (Makin 2005). Equation (1) is thus used to parameterize air–sea momentum flux in the CAWOMS with both wave state and sea spray effects being included. The roughness due to molecular viscosity zs = 0.11ν/u*, where ν is the kinematic molecular viscosity of air, is also added to the sea surface roughness (Smith 1988).

Estimations of air–sea heat and moisture fluxes used in the CAWOMS are from the Coupled Ocean–Atmosphere Response Experiment (COARE) algorithm V3.1 (Fairall et al. 2003), in which the sea surface scalar roughness parameters, zT and zq, are related to the Reynolds number of sea surface aerodynamic roughness. The effects of wave state and sea spray on direct air–sea sensible and latent heat fluxes are thus included implicitly through their impacts on sea surface aerodynamic roughness.

Under high winds, wave breaking and wind tearing wave crests disrupt the air–sea interface and generate sea spray, which, in turn, influences the air–sea heat and water vapor fluxes. In terms of the generation mechanism, there are mainly two kinds of spray droplets. One is bubble-derived droplets, including film droplets and jet droplets, which are produced by the breaking of the air bubbles when arising to the sea surface within whitecaps. The radii of bubble-derived film and jet droplets are typically less than 5 and 20 μm, respectively. The other kind of droplet is spume droplet generated by wind tearing breaking wave crests, with its minimum radius generally about 20 μm. To estimate the sea spray heat flux, one needs to determine the SSGF (dF/dr0), which quantifies how many spray droplets of initial radius r0 are produced per square meter of the surface per second per micrometer increment in droplet radius. SSGF is usually considered as a function of wind speed and droplet radius (e.g., Monahan 1986; Andreas 1992, 1998; Wu 1992; Smith et al. 1993; Fairall et al. 1994). Yet it is evident that SSGF also depends on the surface wave development (Chaen 1973; Piazzola et al. 2002; Zhao et al. 2006).

As for the SSGF for spume droplets, based on field and laboratory observational data, Zhao et al. (2006) proposed a wave-state-dependent SSGF applicable to droplet radius between 30 and 500 μm. Accordingly, this SSGF depends on the wind-sea Reynolds number, RB = u*2/νωp, where u*, ν, and ωp are friction velocity, molecular viscosity, and wave peak frequency, respectively. As the wind-sea Reynolds number can also be expressed as RB = ()−1u*3β*, thereby considered as a parameter combining the wind and wave state effects. For bubble-derived droplets, introducing the whitecap coverage function of Zhao and Toba (2001) into Monahan (1986)’s SSGF, we obtain a windsea Reynolds number (RB)–dependent SSGF:
i1520-0493-139-1-132-e2
Combining the SSGF in Eq. (2), which is applicable to bubble-derived droplets with the SSGF for spume droplets (Zhao et al. 2006), a wave-state-affected SSGF applicable to both bubble-derived droplet and spume droplet is thus obtained.
Further concerning sea spray droplet microphysics (Andreas 1989, 1990), using Andreas (1992)’s method to estimate the “nominal” sea spray sensible and latent heat fluxes, and considering the following feedback effects,
i1520-0493-139-1-132-e3
where α, β, and γ are nonnegative feedback coefficients, one can then estimate the net sea spray contribution to the total sensible and latent heat fluxes:
i1520-0493-139-1-132-e4
Hereafter, QS,sp and QL,sp are called sea spray sensible heat flux and sea spray latent heat flux, respectively. The α and γ are determined following Bao et al. (2000), while β is taken as 1 (Andreas 1992). Equation (4), which includes the effect of wave state on sea spray heat flux, is employed to estimate the net contribution of sea spray to air–sea sensible and latent heat fluxes in the coupled modeling system.
Another important issue under high wind condition is dissipative heating. The frictional dissipation of atmospheric kinetic energy ultimately occurs at molecular scales, which, in turn, is converted into thermal energy. Following Zhang and Altshuler (1999), the dissipative heating in the lowest level of the atmospheric model is expressed as
i1520-0493-139-1-132-e5
where Cp is the air specific heat at constant pressure, z1 is the height of model surface layer, and Va is the wind speed at the model lowest semi-sigma level. Dissipative heating is approximately proportional to the cubic power of surface wind speed. Thus, under high winds, especially TC conditions, dissipative heating increases rapidly with wind speed, which, in turn, will strengthen the TC system. In the present coupled system, since only the dissipative heating in the atmospheric surface layer is considered (Bister and Emanuel 1998; Zhang and Altshuler 1999), an equivalent upward heat flux HE = ρCpVau*2 at the atmosphere surface layer is considered in the CAWOMS.

c. Atmosphere–ocean coupling

The atmosphere–ocean coupling procedure includes 1) the atmospheric model component driving the ocean model component through atmospheric forcings (including sea surface wind stress, shortwave and longwave radiation, sensible and latent heat fluxes, and atmospheric sea level pressure); 2) the ocean circulation model component providing SST to the atmospheric model component to estimate air–sea heat fluxes; 3) the ocean circulation model component providing SSC to determine the relative wind speed for estimation of sea surface wind stress. Variables are exchanged between the atmospheric and ocean circulation model components at the interval of the coupled time step.

d. Wave–ocean coupling

Wave–ocean interaction includes both oceanic effects on sea surface waves and wave-related effects on ocean circulation. The influences of ocean currents on sea surface waves mainly include 1) the Doppler shift effect of background current on sea surface waves; 2) changing water depth by considering variation of water level; 3) consideration of SSC in estimation of sea surface wind stress, which would change the wind forcing on sea surface waves. Sea surface waves also have various effects on ocean circulation: 1) changing sea surface wind stress, which drives the ocean by taking into account the effects of wave state and sea spray; 2) wave-enhanced upper-ocean mixing attributable to wave breaking (Craig and Banner 1994); 3) modifying upper-ocean currents through radiation stress (Longuet-Higgins and Stewart 1962); 4) wave-enhanced bottom stress attributable to wave orbital velocity (especially under shallow water conditions); 5) wave-induced Coriolis–Stokes forcing changing upper-ocean current structure (Polton et al. 2005); 6) Langmuir circulation generated through the instability arising from the interaction of the wave-induced Stokes drift and the shear of ocean current, which contributes to upper-ocean mixing (Polton and Belcher 2007; Grant and Belcher 2009; Kukulka et al. 2009).

The wave–current interaction processes included in the CAWOMS takes into account the above-mentioned three oceanic effects on sea surface waves as well as the first four wave-related effects on ocean circulation. The Stokes drift-related effects are not currently included in the wave–ocean coupling, as further observational and modeling investigations are needed to include those effects in ocean circulation models. The ocean model provides ocean current and water level to the wave model. The wave model provides wave parameters to calculate bottom stress and radiation stress (2D or 3D). In addition, sea surface wind stress affected by wave state, sea spray, and SSC is transferred from the atmospheric model to both the wave and ocean models. This could also influence the wave-enhanced upper-ocean turbulent mixing since it is dependent upon waterside friction velocity (Mellor and Blumberg 2004). The approach used by Davies and Lawrence (1995) is employed to calculate the wave-enhanced bottom friction coefficient. To account for the effects of radiation stresses on ocean currents, radiation stress forcing terms are added to the right-hand side of the momentum balance of the ocean model. The depth-dependent 3D radiation stress extended from the traditional depth-averaged 2D radiation stress (Xie et al. 2008) is used in this study.

It should be noted that although the above coupling processes are introduced individually, they influence each other in the whole coupled system. The atmosphere–wave coupling modifies air–sea momentum and heat fluxes and changes the atmospheric forcing, which, in turn, impacts the atmosphere–ocean coupling. The coupling between atmospheric and ocean models also changes the atmospheric circulation as well as ocean currents, which, in turn, affects sea surface waves and sea spray heat fluxes in atmosphere–wave interaction. The wave–current coupling effect can also be propagated to the whole coupled system, as it changes surface waves and upper-ocean structure.

3. Idealized TC generation and experimental design

a. Idealized TC generation

The CAWOMS is used to investigate the effects of air–sea interaction on an idealized TC system. A bogus vortex is implanted into the typical tropical atmosphere environment to generate an idealized TC. The atmospheric environment is horizontally uniform. The ambient temperature and humidity profiles are derived from the monthly averaged vertical profiles for September at the location of (20°N, 145°E) from the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (Kanamitsu et al. 2002). Uniform easterly winds of 5 m s−1 are specified at all levels. Geopotential heights are then determined by the geostrophic balance. Following Kwok and Chan (2005), a vortex with a maximum wind speed of 30 m s−1 at a radius of 70 km is implanted in the ambient atmosphere. The vortex has Chan and Williams (1987)’s horizontal wind profile with the vertical level of maximum tangential wind being set at 850 hPa. Within the TC, temperatures are determined from the thermal wind balance, and geopotential heights are obtained according to the gradient wind balance.

The monthly averaged temperature and salinity profiles for September at (20°N, 145°E) from the 1° World Ocean Atlas 2005 (WOA05; Locarnini et al. 2006; Antonov et al. 2006) are used to initialize the ocean model. Whereas, within the upper-ocean mixed layer, the temperature profile is slightly modified so that the initial SST equals 29°C, which is the SST value specified in the uncoupled atmospheric model simulation. Figure 2 gives the initial potential temperature and salinity profiles used to initialize the ocean model with a mixed layer depth (MLD) of 40 m. The initial ocean current is set to be quiescent. The initial wave spectra are determined from the initial wind fields.

b. Model settings and spinup

The model domain extends 4320 × 3600 km2 including 361 × 301 horizontal grid points with a uniform grid spacing of 12 km. For the WRF model, geophysical coordinate is adopted with the domain center being located at (20°N, 145°E). Cartesian coordinates on an f plane (with f = 1.45 × 10−4) are used in the POM and SWAN model components. The whole domain is set on a hypothetical deep ocean with a uniform water depth of 2500 m. Thus, land and shallow water effects are not considered. The model components exchange variables every 15 min, and the simulation results are output every 3 h. WRF contains 30 sigma levels in the vertical with the model top being at 50 hPa. Its time step is 60 s. The WRF single-moment 5-class (WSM5) microphysics scheme (Hong et al. 2004), Kain–Fritsch cumulus scheme (Kain and Fritsch 1990), Yonsei University planetary boundary layer (YSU PBL) scheme (Hong et al. 2006), and Dudhia shortwave (Dudhia 1989) and Rapid Radiative Transfer Model (RRTM) longwave (Mlawer et al. 1997) radiation schemes are used in this study. POM has 24 full sigma levels in the vertical direction, with 12 levels in the upper 100 m, and the time steps for the internal and external modes are 300 s and 10 s, respectively. SWAN resolves 32 frequencies logarithmically spaced from 0.0418 to 0.8023 Hz and 36 direction bands of 10° each. Its time step is 15 min. As for lateral boundary conditions, variables at boundary points are kept time-invariant for WRF, and open boundary conditions are utilized for POM and SWAN.

The model components are first integrated 12 h for model spinup, during which, each model component runs in control mode. In the spinup procedure, WRF drives SWAN and POM through atmospheric forcing, and no wave and ocean feedbacks are activated. The SST is set as 29°C for the atmospheric model. The results of the spinup are then used as the initial conditions for the rest of the experiments.

c. Experiments

To investigate the effects of atmosphere–wave–ocean coupling on a TC system, several experiments are conducted in this study. Each experiment carries out a 72-h integration based on the same initial conditions from the 12-h spinup. Experiment CTRL is the control run, in which the three model components run in the same way as in the spinup procedure. Atmospheric forcing fields from WRF are transferred to SWAN and POM every 15 min. The SST for the WRF model is kept constant (29°C). Neither effects of wave state and sea spray nor oceanic feedbacks are considered in this experiment. In experiment CPLAW, the WRF model component is coupled to the SWAN model component, in which the atmosphere–wave coupling processes described in section 2b are taken into account, while the ocean model component is run in control mode. Experiment CPLAO couples WRF to POM, only considering the atmosphere–ocean interaction described in section 2c. In the CPLAWO experiment, all three models are coupled to each other, taking into account atmosphere–wave, atmosphere–ocean, and wave–ocean interaction processes. Table 1 gives a summary of the experiments conducted in this study.

4. Results and discussion

a. Control experiment

Because of the specified uniform environmental easterly wind and the beta effect, the idealized TC moves west-northwestward. There is no noticeable difference among the simulated tracks for the experiments (figure not shown here) indicating that the effect of air–sea interaction on the track of this idealized TC is negligible. In the following, we will mainly focus on the effects of atmosphere–wave–ocean coupling on the intensity of the TC system in both atmospheric and oceanic aspects.

In the control run, after the first 24-h rapid intensification, the TC strengthens further and reaches its peak intensity at about 45 h with a minimum sea level pressure (SLP) of 959 hPa, and a maximum 10-m wind of 41 m s−1 (Fig. 3a and Table 2). Figure 4a shows the SLP field and 10-m wind vector in CTRL valid at 45 h. Asymmetric surface wind distribution with the maximum wind in the right side of the TC moving direction can be noticed. A marked warm core structure and the asymmetry of wind structure are also evident in the TC vertical cross section (Fig. 5).

The strong TC winds generate large surface waves. The maximum SWH in the control run is 17.8 m (Table 2). The 45-h SWH distribution (Fig. 6a) in CTRL shows that the largest SWHs are generally associated with the strongest winds. As for the oceanic responses, because of the strong turbulent mixing and upwelling driven by the TC forcing, a cold wake is generated behind the TC along the storm track with the coldest SST located on the right side of the track (Fig. 7a). The minimum SST reaches 25.3°C; 3.7°C cooler than the specified SST for the ocean model initialization. The simulated cold wake and SST cooling are in agreement with previous modeling (e.g., Price 1981) and observational studies (D’Asaro et al. 2007; Black and Dickey 2008). The distribution of MLD (Fig. 7b) is similar to that of SST, with the largest values within the core area of the cold wake. Figure 8 shows the evolutions of vertical temperature profile at point A in Fig. 7b. At this location, the vertical temperature structure is kept almost unchanged for the first 36 h before the effect of the TC is felt. During the period from 36 to 45 h, turbulent mixing is increased by the strong winds and waves induced by the TC, leading to a rapid increase of MLD. After the passage of the TC, near-inertial current behind the TC results in a near-inertial oscillation of MLD. Figure 9a shows the 45-h SSC in the control run, with the strongest current located in the area with the highest surface wind. Near-inertial currents behind the storm are also evident along the storm track, which is consistent with previous observational results (e.g., Shay et al. 1998; Black and Dickey 2008).

The simulated TC structure, surface waves, and oceanic responses are in agreement with previous observational and modeling studies, indicating that the control experiment can basically simulate the TC system in the atmospheric, surface wave, and oceanic aspects. In the following, we will further analyze the effects of atmosphere–wave–ocean coupling on the TC system.

b. Effects of atmosphere–wave coupling

The coupling between atmosphere and sea surface wave has both negative and positive effects on TC intensity. The wave-state-dependent sea surface roughness increases drag coefficient under low and moderate wind conditions and thus increases surface friction and weakens the TC system, while the existence of sea spray decreases drag coefficient under high winds and thus makes positive contribution to TC intensity. Sea spray heat flux and dissipative heating increase upward-surface heat flux and provide energy for the TC to maintain or strengthen its intensity. Taking into account the wave state, sea spray, and dissipative heating effects, the overall contribution of atmosphere–wave coupling strengthens the TC system, with the minimum SLP in CPLAW 8 hPa deeper than in the control run (Table 2). The maximum 10-m wind reaches 49 m s−1, corresponding to an increase of 8 m s−1 (about 20%).

Figure 4b shows the simulated 45-h SLP and 10-m wind in the CPLAW run. Compared to the results in CTRL (Fig. 4a), the minimum SLP is deeper and the maximum wind speed is larger than those in the control run, though the area with high winds (e.g., >20 m s−1) in CPLAW is smaller than that in the control run. This is because the effect of wave state increases sea surface roughness and friction velocity, while sea spray reduces drag coefficients in high wind areas. As a result, the maximum friction velocity (Fig. 10b) and sea surface roughness (Fig. 10d) in CPLAW are much larger than their counterparts (Figs. 10a,c) in CTRL, with the large sea surface roughness located in areas with small wave ages (Fig. 10f) and wind speed of 25–33 m s−1. At the low-to-moderate wind speed range, the simulation results are consistent with other atmosphere–wave coupling studies (Doyle 1995; Lionello et al. 1998) that considered the wave state effects on increasing sea surface roughness. However, those studies did not consider the sea spray effects under high winds.

The simulated 45-h total sensible and latent heat fluxes (Fig. 11) for each experiment show that strong upward heat and moisture fluxes occur in areas with strong winds and waves, which maintain and strengthen TC intensity. The left column of Fig. 12 shows the simulated 45-h direct sensible heat flux, direct latent heat flux, sea spray sensible heat flux, and sea spray latent heat flux in CPLAW. It can be seen that the sea spray sensible and latent heat fluxes are significant in areas with strong winds and waves. The sea spray sensible heat flux is negative and makes negative contribution to the total upward sensible heat flux. While the sea spray latent heat flux makes positive contribution to the total latent heat flux. These results are similar to those of Fairall et al. (1994) and Kepert et al. (1999). Compared with the results of CTRL (Figs. 11a,b), sea spray heat flux increases the direct sensible heat flux but decreases the direct latent heat flux. The evaporation of the spray droplets changes the low-level atmospheric environment by reducing air temperature and increasing air moisture, thus increasing the air–sea temperature difference and reducing the air–sea moisture difference. Another important heat source under high winds comes from dissipative heating. Figure 13a shows the 45-h equivalent sensible heat flux to dissipative heating in CPLAW. Because of the cubic power dependence on wind speed, the equivalent sensible heat flux to dissipative heating is negligible in low wind areas but significant in high wind areas. The dissipative heating is almost as large as 2 times the direct sensible heat flux in high wind areas. By increasing total upward air–sea heat flux, sea spray heat fluxes together with dissipative heating strengthen the TC system in CPLAW.

As the atmosphere–wave coupling strengthens the TC, it changes the distribution of surface wind as well as other atmospheric forcings such as air–sea heat fluxes, and the wave and oceanic responses to the TC change accordingly. In CPLAW, the maximum SWH reaches 19.1 m, which is 1.3 m (about 7%) higher than that in the control run. At 45 h, the distribution patterns of SWH in CPLAW and CTRL are quite similar to each other, except the maximum SWH is larger in CPLAW (Fig. 6b). Increased wind stress also enhances turbulent mixing in the upper ocean, leading to deeper MLD (Fig. 7e) and cooler cold wake (Fig. 7d). The minimum SST in CPLAW is 24.9°C; 4.1°C cooler than the initial environmental SST and 0.4°C cooler than that in the control run. In addition, in the CPLAW experiment, SSC is stronger (Fig. 9b) and the maximum SSC increased by 0.06 m s−1 (about 7%) relative to CTRL (Table 2).

c. Effects of atmosphere–ocean coupling

TC-induced SST cooling usually reduces air–sea heat flux, thus weakening the TC system. In CPLAO, which considers only atmosphere–ocean coupling processes without explicit coupling to waves, the simulated TC is weaker than in the control run. The minimum SLPs are generally shallower and the maximum 10-m winds are generally weaker than in CTRL during the 72-h simulation (Fig. 3). The minimum SLP increased by 7 hPa, while the peak maximum 10-m wind is only 1 m s−1 weaker (Fig. 2). Accordingly, the maximum SWH and SSC decrease by 1.8 m (10%) and 0.19 m s−1 (20%), respectively, and the minimum SST increases to 26.2°C; 0.9°C higher than that in CTRL, corresponding to a weaker cold wake (Fig. 7g). The MLD in CPLAO (Fig. 7h) is also shallower than that in CTRL (Fig. 7b) because of weaker surface wind. This is also shown in Fig. 8.

Since both SST and the depth of the upper-ocean warm layer are important factors that affect TC intensity (Wu et al. 2007), the ocean heat content relative to the 26°C isotherm (Leipper and Volgenau 1972), also known as hurricane heat content (HHC), is a better parameter in determining oceanic effects on TC intensity. Oey et al. (2006) proposed a nondimensional parameter—the ratio of energy required to mix water in upper ocean with cooler water in subsurface layer to power dissipation by the wind—which could be a better measure to account for the effect of wind mixing on SST cooling, especially under high wind conditions. HHC is used here to demonstrate both the oceanic feedback effects on the idealized TC and the SST cooling attributable to TC-induced strong upper-ocean mixing. HHCs (right column of Fig. 7) in the cold wake area associated with deep MLD are much less than in other areas. As a result, the air–sea sensible and latent heat fluxes in CPLAO are reduced compared to the control run (Fig. 11). It can be seen that there are relatively low values of sensible and latent heat fluxes in the cold wake area. The maximum fluxes are shifted to the front area of the TC. The changes in the distributions of heat fluxes might also have impacts on the TC structure, for example, the maximum surface wind area rotates slightly counterclockwise (Fig. 4c).

The results of CPLAO show that without considering the wave dynamics, the overall effects of the atmosphere–ocean coupling weaken the TC system. This is consistent with previous atmosphere–ocean coupling studies for TCs (Schade and Emanuel 1999; Bender and Ginis 2000). However, it should be pointed out that the effects of the SST cooling depend on the upper-ocean temperature structure. Deeper upper-ocean warm layer would reduce the negative feedback of SST cooling.

d. Effects of atmosphere–wave–ocean coupling

Under the atmosphere and ocean environment specified in this study, the TC intensity in the fully coupled experiment (CPLAWO) is much weaker than in CPLAW, stronger than in CPLAO, and slightly weaker than in the control experiment (Fig. 3a). The minimum SLP in CPLAWO is 3 hPa shallower than that in the control run. Although at several instances when the maximum 10-m winds in CPLAWO are slightly higher than in those in CTRL, the CPLAWO simulated maximum 10-m winds are generally lower than those simulated by CTRL (Fig. 3). Correspondingly, the simulated SWH and SSC are also lower than in the control experiment (see Figs. 6 and 9). The maximum SWH is reduced by 1.7 m (about 10%). The minimum SST is 26.9°C, which is 0.6°C warmer than that in the control run and in CPLAO (Fig. 7d), corresponding to weaker SST cooling. The MLD in CPLAWO is shallower than in CTRL and CPLAW because of weaker winds, while a little deeper than in CPLAO because of wave effects, which increase upper-ocean mixing. The HHCs in the cold wake in CPLAWO (Fig. 7l) are less than those in CPLAO (Fig. 7i). It should be pointed out that although much less HHC occurs in CTRL and CPLAW because of strong winds and turbulent mixing, the oceanic feedback effects are not taken into account in those experiments.

The influence of atmosphere–wave–ocean coupling on air–sea momentum flux is mainly attributable to wave state and sea spray effect on sea surface roughness. The distributions of friction velocity and sea surface roughness in CPLAWO (not shown) are quite similar to those in CPLAW, except with smaller values resulting from weaker TC intensity. Sea spray heat fluxes (right column of Fig. 12) and dissipative heating (Fig. 13b) also show spatial patterns similar to those in CPLAW. The air–sea total sensible and latent heat fluxes in the fully coupled experiment (Figs. 11h,i) are modified by the oceanic negative feedback, the sea spray heat fluxes, and the dissipative heating. Thus, both the amounts and the distribution pattern of the air–sea heat fluxes in CPLAWO are different from those in the control run.

In summary, the effects of atmosphere–wave–ocean coupling on TC intensity depend on the balance between wave-related overall positive feedback and oceanic overall negative feedback. It should be noted that although under the environmental atmospheric and oceanic conditions specified in this study the full atmosphere–wave–ocean coupling weakens the idealized TC, it does not mean that the overall contribution of atmosphere–wave–ocean coupling to TC intensity is always negative. Given specified atmospheric environment and initial TC intensity and structure, the upper-ocean thermal structure determines the amount of negative feedback of SST cooling. For a specified SST favorable to TC development, shallower MLD associated with less HHC causes stronger negative feedback of SST cooling, which could exceed the wave-related positive effect and lead to a weaker TC than in the uncoupled run. However, deeper MLD associated with more HHC corresponds to weaker SST cooling, and thus results in weaker negative feedback. Under this condition, wave-related effects could outweigh the oceanic negative feedback of SST cooling, and thus strengthen the TC system. In an extreme case with a very deep warm layer in the upper ocean in which TC-induced SST cooling is negligible, the wave-related effects will be much more significant than the mixed layer feedback effects and increase the intensity of the TC system. To demonstrate the dependence of air–sea interaction on upper-ocean mixed layer structure, two additional sets of experiments, with the same initial SST (29°C) but larger initial MLDs of 80 and 120 m (see Fig. 2b for the initial vertical profiles of potential temperature in the upper ocean), are conducted. Figure 14 shows the time series of the simulated minimum SLP and maximum 10-m wind for these experiments. CPLAO80 and CPLAWO80 are the same as CPLAO and CPLAWO except the initial MLD is 80 m instead of 40 m, while CPLAO120 and CPLAWO120 have the initial MLD of 120 m. It is shown that for the same initial SST, increasing the initial ocean MLD reduces the negative feedback of SST cooling. The TC intensities in CPLAO80 and CPLAO120 are stronger than that in CPLAO (Figs. 14a,c). The weaker negative SST feedback leads to stronger TC intensities in CPLAWO80 and CPLAWO120 than those in CPLAWO and CTRL, due to the fact that the wave-related positive contribution exceeds the negative feedback from the TC-induced SST cooling.

5. Summary and conclusions

In this study, a coupled atmosphere–wave–ocean modeling system (CAWOMS), which consists of WRF, SWAN, and POM, is established based on atmosphere–wave, atmosphere–ocean, and wave–current interaction processes. Wave-related effects—including wave state and sea-spray-affected sea surface roughness, sea spray heat fluxes, and dissipative heating—are considered in atmosphere–wave coupling. Oceanic effects, such as feedback of SST cooling and impact of SSC on wind stress, are included in atmosphere–ocean coupling. Wave–current interaction, including radiation stress and wave-induced bottom stress, are taken into account in wave–ocean coupling. The CAWOMS is then employed in the simulation of an idealized TC system in order to investigate the effects of atmosphere–wave–ocean coupling on TC intensity.

The coupling between atmosphere and sea surface waves strengthens the TC system. The wave-state-dependent sea surface roughness, which increases drag coefficient and surface friction under low wind conditions, tends to weaken the TC system; however, sea spray under high winds reduces drag coefficient, which would decrease surface friction and favors TC intensification. Sea spray heat fluxes and dissipative heating increase air–sea heat flux, providing more thermal energy for the TC to maintain and strengthen its intensity. The overall contribution of atmosphere–wave coupling strengthens the TC system. For the case considered in this study, the minimum SLP is 8 hPa deeper than in the control run, and the maximum 10-m wind increased by 20%. The wave and oceanic responses to the TC also changed accordingly, obtaining higher SWH, stronger SSC, cooler SST, and deeper MLD.

TC-induced SST cooling reduces upward air–sea heat flux, thus providing less energy for TC intensification resulting from the negative feedback of SST cooling. When only considering the coupling between the atmosphere and the upper-ocean mixed layer without wave-related effects, the TC system is weakened as indicated by a higher central SLP and weaker surface wind. Weaker atmospheric forcing also results in weaker wave and oceanic responses. The overall oceanic negative feedback depends on the upper-ocean thermal structure. Shallower warm layer results in stronger negative feedback of SST cooling, while deeper warm layer leads to weaker negative feedback of SST cooling.

The effects of atmosphere–wave–ocean coupling on TC intensity are the results of the balance between the wave-related overall positive contribution and oceanic overall negative contribution. When the negative feedback of SST cooling is significant because of shallow upper-ocean warm layer, the negative upper-ocean mixed layer feedback exceeds the wave-related positive feedback. Thus, the overall effects of atmosphere–wave–ocean coupling would weaken the TC system. However, in the presence of a deep upper-ocean warm layer, the negative feedback of SST cooling will be less significant. The positive effect of wave and sea spray would outweigh the oceanic negative feedback, leading to a strengthening of the TC system.

Acknowledgments

We are grateful for comments from the reviewers, and for proofreading by Katie Costa. This study is a joint effort between North Carolina State University (NCSU) and Ocean University of China (OUC). The NCSU participants are supported by grants awarded by the U.S. National Oceanic and Atmospheric Administration (NOAA) through subcontract UF-EIES-0704029NCS and the U.S. Department of Energy award DE-FG02-07ER64448, whereas the OUC participants are supported by the National Natural Science Foundation of China (40830959 and 40676014).

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Fig. 1.
Fig. 1.

Schematic illustration of the CAWOMS. Here u* is friction velocity and T, q, and p are atmospheric low-level temperature, specific humidity, and pressure, respectively; QS,sp and QL,sp are sea spray sensible and latent heat fluxes, respectively.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 2.
Fig. 2.

The initial vertical potential temperature (solid line with markers) and salinity (dashed line with *) profiles for the 12-h spinup run for (a) all half-sigma levels of the ocean model and (b) the upper 500 m only. Here + corresponds to the profile with MLD of 40 m, while ○ and □ correspond to the profiles with MLDs of 80 and 120 m, respectively.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 3.
Fig. 3.

Time series of the simulated (a) min SLP, (b) max 10-m wind, (c) max SWH, and (d) min SST for each experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 4.
Fig. 4.

The simulated 45-h SLP (contours every 4 hPa) and 10-m wind vector and speed (shading at the interval of 5 m s−1) for experiments (a) CTRL, (b) CPLAW, (c) CPLAO, and (d) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 5.
Fig. 5.

Vertical cross section passing through the TC center and the location of maximum surface wind of potential temperature (contours every 5°C) and horizontal wind speed (shading at an interval of 5 m s−1) valid at 45 h of the control run.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 6.
Fig. 6.

As in Fig. 4, but for SWH (contours every 2 m) and mean wave direction (vectors).

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 7.
Fig. 7.

The simulated 45-h (left) SST (contours every 1°C), (middle) MLD (contours every 20 m), and (right) HHC (contours every 2 × 108 J m−2) for experiments (top to bottom) CTRL, CPLAW, CPLAO, and CPLAWO. Shadings in (c) and (f) indicate SSTs in those areas are <26°C. The dashed line with + signs in each panel is the simulated 3-hourly storm track for the corresponding experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 8.
Fig. 8.

Evolutions of vertical potential temperature (°C) structure at point A in Fig. 7 for experiments (a) CTRL, (b) CPLAW, (c) CPLAO, and (d) CPLAWO.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 9.
Fig. 9.

The simulated 45-h SSC vector (filled arrow) and speed (shading interval of 0.1 m s−1) for experiments (a) CTRL, (b) CPLAW, (c) CPLAO, and (d) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 10.
Fig. 10.

The simulated (top) 45-h friction velocity (m s−1), (middle) sea surface roughness (m), and (bottom) wave age for experiments (left) CTRL and (right) CPLAW, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 11.
Fig. 11.

The simulated 45-h total (left) sensible heat and (right) latent heat fluxes (W m−2) for experiments (top to bottom) CTRL, CPLAW, CPLAO, and CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 12.
Fig. 12.

The simulated 45-h (top to bottom) direct sensible heat flux, direct latent heat flux, sea spray sensible heat flux, and sea spray latent heat flux for experiments (left) CPLAW and (right) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment. Unit is W m−2.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 13.
Fig. 13.

The simulated 45-h dissipative heating (W m−2) for experiments (a) CPLAW and (b) CPLAWO, together with the simulated 3-hourly storm track (dashed line with + signs) for each experiment.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Fig. 14.
Fig. 14.

Time series of the simulated (top) min SLP and (bottom) max 10-m wind for experiments with (left and right) different initial MLDs. CPLAO80 and CPLAWO80 are the same as CPLAO and CPLAWO except the initial MLD is 80 m instead of 40 m, while CPLAO120 and CPLAWO120 have the initial MLD of 120 m.

Citation: Monthly Weather Review 139, 1; 10.1175/2010MWR3396.1

Table 1.

Summary of the experiments. In each experiment, the one-way coupling, i.e., the atmospheric model driving the wave and ocean models through atmospheric forcing, is always taken into account.

Table 1.
Table 2.

The simulated min SLP, max 10-m wind, max SWH, min SST, max SSC, and max MLD for each experiment.

Table 2.
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