• Berner, A. H., , C. S. Bretherton, , and R. Wood, 2011: Large-eddy simulation of mesoscale dynamics and entrainment around a pocket of open cells observed in VOCALS RF06. Atmos. Chem. Phys., 11, 10 52510 540.

    • Search Google Scholar
    • Export Citation
  • Blossey, P. N., , and D. R. Durran, 2008: Selective monotonicity preservation in scalar advection. J. Comput. Phys., 227, 51605183.

  • Bretherton, C. S., and Coauthors, 1999: An intercomparison of radiatively driven entrainment and turbulence in a smoke cloud, as simulated by different numerical models. Quart. J. Roy. Meteor. Soc., 125, 391423.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and Coauthors, 2004: The EPIC 2001 stratocumulus study. Bull. Amer. Meteor. Soc., 85, 967977.

  • de Szoeke, S. P., , S. Yuter, , D. Mechem, , C. W. Fairall, , C. Burleyson, , and P. Zuidema, 2012: Observations of stratocumulus clouds and their effect on the eastern Pacific surface heat budget along 20°S. J. Climate, in press.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., , R. Boers, , B. Stevens, , and W. R. Cotton, 1997: A modeling study of the effect of drizzle on cloud optical depth and susceptibility. J. Geophys. Res., 102 (D12), 13 52713 534.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., , R. L. Walko, , B. Stevens, , and W. R. Cotton, 1998: Simulations of marine stratocumulus using a new microphysical parameterization scheme. Atmos. Res., 47–48, 505528.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., , I. Koren, , H. Wang, , H. Xue, , and W. Brewer, 2010: Precipitation-generated oscillations in open cellular cloud fields. Nature, 466, 849852.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., , and K.-N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 21392156.

    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., , and R. Muñoz, 2004: The diurnal cycle in circulation and cloudiness over the subtropical southeast Pacific: A modeling study. J. Climate, 17, 16991710.

    • Search Google Scholar
    • Export Citation
  • Gerber, H., , and G. Frick, 2012: Drizzle rates and large sea-salt nuclei in small cumulus. J. Geophys. Res., 117, D01205, doi:10.1029/2011JD016249.

    • Search Google Scholar
    • Export Citation
  • Golaz, J.-C., , S. Wang, , J. D. Doyle, , and J. M. Schmidt, 2005: COAMPS-LES: Model evaluation and analysis of second- and third-momentum vertical velocity budgets. Bound.-Layer Meteor., 116, 487517.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., , and D. A. Short, 1980: On the use of earth radiation budget statistics for studies of clouds and climate. J. Atmos. Sci., 37, 12331250.

    • Search Google Scholar
    • Export Citation
  • Hodur, R. M., 1997: The Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 14141430.

    • Search Google Scholar
    • Export Citation
  • Jiang, Q., 2012: On offshore diurnal waves. J. Atmos. Sci., 69, 15621581.

  • Kazil, J., , H. Wang, , G. Feingold, , A. D. Clarke, , J. R. Snider, , and A. R. Bandy, 2011: Modeling chemical and aerosol processes in the transition from closed to open cells during VOCALS-Rex. Atmos. Chem. Phys., 11, 74917514.

    • Search Google Scholar
    • Export Citation
  • Kiehl, J. T., , and P. R. Gent, 2004: The Community Climate System Model, version 2. J. Climate, 17, 36663682.

  • Kogan, Y. L., 2006: Large-eddy simulation of air parcels in stratocumulus clouds: Time scales and spatial variability. J. Atmos. Sci., 63, 15431559.

    • Search Google Scholar
    • Export Citation
  • Ma, C.-C., , C. R. Mechoso, , A. W. Robertson, , and A. Arakawa, 1996: Peruvian stratus clouds and the tropical Pacific circulation: A coupled ocean – atmosphere GCM study. J. Climate, 9, 16351645.

    • Search Google Scholar
    • Export Citation
  • Mechem, D. B., , S. E. Yuter, , and S. P. de Szoeke, 2012: Thermodynamic and aerosol controls in southeast Pacific stratocumulus. J. Atmos. Sci., 69, 12501266.

    • Search Google Scholar
    • Export Citation
  • Muñoz, R. C., 2008: Diurnal cycle of surface winds over the subtropical southeast Pacific. J. Geophys. Res., 113, D13107, doi:10.1029/2008JD009957.

    • Search Google Scholar
    • Export Citation
  • Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527530.

  • Rahn, D. A., , and R. Garreaud, 2010: Marine boundary layer over the subtropical southeast Pacific during VOCALS-REx – Part 1: Mean structure and diurnal cycle. Atmos. Chem. Phys., 10, 44914506.

    • Search Google Scholar
    • Export Citation
  • Savic-Jovcic, V., , and B. Stevens, 2008: The structure and mesoscale organization of precipitating stratocumulus. J. Atmos. Sci., 65, 15871605.

    • Search Google Scholar
    • Export Citation
  • Sorbjan, Z., 2005: Large-eddy simulations of the atmospheric boundary layer. Advanced Topics, P. Zannetti, Ed., Vol. 2, Air Quality Modeling—Theories, Methodologies, Computational Techniques, and Available Databases and Software, EnviroComp Institute, 11–82.

  • Stevens, B., , W. R. Cotton, , G. Feingold, , and C. Moeng, 1998: Large-eddy simulations of strongly precipitating, shallow, stratocumulus-topped boundary layers. J. Atmos. Sci., 55, 36163638.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2003: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II. Bull. Amer. Meteor. Soc., 84, 579593.

  • Stevens, B., and Coauthors, 2005: Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Mon. Wea. Rev., 133, 14431462.

    • Search Google Scholar
    • Export Citation
  • Wang, H., , and G. Feingold, 2009: Modeling mesoscale cellular structures and drizzle in marine stratocumulus. Part I: Impact of drizzle on the formation and evolution of open cells. J. Atmos. Sci., 66, 32373255.

    • Search Google Scholar
    • Export Citation
  • Wang, H., , G. Feingold, , R. Wood, , and J. Kazil, 2010: Modelling microphysical and meteorological controls on precipitation and cloud cellular structure in southeast Pacific stratocumulus. Atmos. Chem. Phys., 10, 63476362.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , J.-C. Golaz, , and Q. Wang, 2008: Effect of intense wind shear across the inversion on stratocumulus clouds. Geophys. Res. Lett., 35, 15814, doi:10.1029/2008GL033865.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , X. Zheng, , and Q. Jiang, 2012: Strongly sheared stratocumulus convection: An observationally based large-eddy simulation study. Atmos. Chem. Phys., 12, 52235235, doi:10.5194/acp-12-5223-2012,2012.

    • Search Google Scholar
    • Export Citation
  • Wittenberg, A. T., , A. Rosati, , N.-C. Lau, , and J. J. Ploshay, 2006: GFDL’s CM2 global coupled climate models. Part III: Tropical Pacific climate and ENSO. J. Climate, 19, 698722.

    • Search Google Scholar
    • Export Citation
  • Wood, R., , M. Kohler, , R. Bennartz, , and C. O’Dell, 2009: The diurnal cycle of surface divergence over the global oceans. Quart. J. Roy. Meteor. Soc., 135, 14841493.

    • Search Google Scholar
    • Export Citation
  • Wood, R., , C. S. Bretherton, , D. Leon, , A. D. Clarke, , P. Zuidema, , G. Allen, , and H. Coe, 2011a: An aircraft case study of the spatial transition from closed to open mesoscale cellular convection over the southeast Pacific. Atmos. Chem. Phys., 11, 23412370.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and Coauthors, 2011b: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-Rex): Goals, platforms, and field operations. Atmos. Chem. Phys., 11, 627654.

    • Search Google Scholar
    • Export Citation
  • Xue, H., , G. Feingold, , and B. Stevens, 2008: Aerosol effects on clouds, precipitation, and the organization of shallow cumulus convection. J. Atmos. Sci., 65, 392406.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Horizontal wind components (m s−1), (b) potential temperature (K), and (c) water vapor mixing ratio (g kg−1) profiles derived from VOCALS RF-6 measurements.

  • View in gallery

    The normalized vertical motion W and the corresponding vertical displacement of the STBL top η for the specified mesoscale waves. The four pairs of curves correspond to semidiurnal waves with ascent–descent (SXAD) and descent–ascent (SXDA) phases, a half semidiurnal wave (SXAF), and a quarter-diurnal wave (QXAD). The letter “X” in the legend denotes wave amplitude (i.e., weak, moderate, or strong). The SXAD pair corresponds to a diurnal wave (i.e., DXAD) when the time axis runs from 2 to 26 h.

  • View in gallery

    Time series of six domain-average variables derived from the baseline set of simulations. These variables are (a) the proportional increase of the STBL depth (i.e., Zi/Zi0 − 1), (b) cloud fraction, (c) rain rate (mm day−1), (d) vertical velocity maximum (m s−1), (e) sensible heat flux (W m−2), and (f) latent heat flux (W m−2). For NMWF-100, the cloud fraction is nearly unity in (b) and the surface rain rate is virtually zero in (c), which is not visible.

  • View in gallery

    Time series of the domain-average (a) LWP (kg m−2), (b) albedo, (c) scaled LWP variances [defined as ], and (d) skewness of LWP [i.e., ] for four simulations from the control set.

  • View in gallery

    Time–height plots of (a)–(d) cloud water mixing ratio (color, g kg−1) and liquid potential temperature (white contours, interval = 5 K), (e)–(h) RWF (color, mm day−1) and (contour, interval = 0.1 m2 s−2), and (i)–(l) (color, m3 s−3) and buoyancy flux (, W m−2, interval = 10 W m−3, only positive values are contoured) for simulations (first column) NMWF-100, (second column) SWAD-100, (third column) SMAD-100, and (fourth column) SMDA-100.

  • View in gallery

    Plan views of the albedo valid at 5, 8, 11, and 14 h derived from simulations (a)–(d) NMWF-100, (e)–(h) SWAD-100, (i)–(l) SMAF-100, and (m)–(p) SWDA-100.

  • View in gallery

    Horizontal domain-average profiles of (a) water vapor (g kg−1), (b) (m2 s−2), and (c) liquid potential temperature (K) valid at 2, 5, and 8 h for SMAD-100.

  • View in gallery

    As in Fig. 3, but for two pairs of simulations for Na = 200 and 50 cm−3 with semidiurnal weak- and moderate-amplitude wave forcing.

  • View in gallery

    As in Fig. 4, but for two pairs of simulations for Na = 200 and 50 cm−3 with semidiurnal weak- and moderate-amplitude wave forcing.

  • View in gallery

    As in Fig. 5, but for simulations (left to right) NMWF-50, NMWF-200, SMAD-50, and SMAD-200.

  • View in gallery

    As in Fig. 6, but for simulations (a)–(d) NMWF-50, (e)–(h) NMWF-200, (i)–(l) SMAD-50, and (m)–(p) SMAD-200.

  • View in gallery

    As in Fig. 5, but for (left to right) QWAD-100, QWAD-50, DWAD-100, and DWAD-50.

  • View in gallery

    Plan views of the albedo valid at t = 3.5, 5, 6.5, 8, 9.5, 11, 12.5, and 14 h derived from the two quarter-diurnal simulations, (a)–(h) QWAD-100 and (i)–(p) QWAD-50.

  • View in gallery

    Plan views of the albedo at t = 5, 8, 11, 14, 17, 20, 23, and 26 h for (a)–(h) DWAD-100 and (i)–(p) DWAD-50.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3 3 0
PDF Downloads 2 2 0

Impact of Gravity Waves on Marine Stratocumulus Variability

View More View Less
  • 1 Naval Research Laboratory, Monterey, California
© Get Permissions
Full access

Abstract

The impact of gravity waves on marine stratocumulus is investigated using a large-eddy simulation model initialized with sounding profiles composited from the Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-Rex) aircraft measurements and forced by convergence or divergence that mimics mesoscale diurnal, semidiurnal, and quarter-diurnal waves. These simulations suggest that wave-induced vertical motion can dramatically modify the cloud albedo and morphology through nonlinear cloud–aerosol–precipitation–circulation–turbulence feedback.

In general, wave-induced ascent tends to increase the liquid water path (LWP) and the cloud albedo. With a proper aerosol number concentration, the increase in the LWP leads to enhanced precipitation, which triggers or strengthens mesoscale circulations in the boundary layer and accelerates cloud cellularization. Precipitation also tends to create a decoupling structure by weakening the turbulence in the subcloud layer. Wave-induced descent decreases the cloud albedo by dissipating clouds and forcing a transition from overcast to scattered clouds or from closed to open cells. The overall effect of gravity waves on the cloud variability and morphology depends on the cloud property, aerosol concentration, and wave characteristics. In several simulations, a transition from closed to open cells occurs under the influence of gravity waves, implying that some of the pockets of clouds (POCs) observed over open oceans may be related to gravity wave activities.

Corresponding author address: Qingfang Jiang, Naval Research Laboratory, 7 Grace Hopper Ave., Monterey, CA 93943. E-mail: jiang@nrlmry.navy.mil

Abstract

The impact of gravity waves on marine stratocumulus is investigated using a large-eddy simulation model initialized with sounding profiles composited from the Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study Regional Experiment (VOCALS-Rex) aircraft measurements and forced by convergence or divergence that mimics mesoscale diurnal, semidiurnal, and quarter-diurnal waves. These simulations suggest that wave-induced vertical motion can dramatically modify the cloud albedo and morphology through nonlinear cloud–aerosol–precipitation–circulation–turbulence feedback.

In general, wave-induced ascent tends to increase the liquid water path (LWP) and the cloud albedo. With a proper aerosol number concentration, the increase in the LWP leads to enhanced precipitation, which triggers or strengthens mesoscale circulations in the boundary layer and accelerates cloud cellularization. Precipitation also tends to create a decoupling structure by weakening the turbulence in the subcloud layer. Wave-induced descent decreases the cloud albedo by dissipating clouds and forcing a transition from overcast to scattered clouds or from closed to open cells. The overall effect of gravity waves on the cloud variability and morphology depends on the cloud property, aerosol concentration, and wave characteristics. In several simulations, a transition from closed to open cells occurs under the influence of gravity waves, implying that some of the pockets of clouds (POCs) observed over open oceans may be related to gravity wave activities.

Corresponding author address: Qingfang Jiang, Naval Research Laboratory, 7 Grace Hopper Ave., Monterey, CA 93943. E-mail: jiang@nrlmry.navy.mil

1. Introduction

Marine stratus and stratocumulus (Sc) clouds cover a considerable portion of the world’s tropical and subtropical oceans and play a significant role in the global energy balance through modulating the earth’s albedo (e.g., Hartmann and Short 1980). Marine stratus and stratocumulus clouds are characterized by strong temporal (i.e., diurnal, seasonal, interannual, etc.) and spatial variability and remain a challenge to mesoscale, global, and climate models. This is especially true over the southeastern Pacific (SEP), which is frequently covered by the world’s largest subtropical stratocumulus deck due to its relatively cool sea surface temperature. Over the SEP, coupled atmosphere–ocean models often produce significant errors in sea surface wind and temperature, along with the notorious difficulty in modeling its stratocumulus coverage (e.g., Ma et al. 1996; Kiehl and Gent 2004; Wittenberg et al. 2006; de Szoeke et al. 2012).

Because of its importance in weather and climate modeling, Sc has been the subject of extensive numerical and observational studies over the past two decades or so. The complexity of Sc comes from multiple physical, dynamic, and thermodynamic processes at play over a broad range of temporal and spatial scales. For example, the formation and characteristics of Sc are dependent on synoptic-scale conditions as well as small-scale turbulence and microphysical processes, and its cellular structure (approximately tens of kilometers) is clearly mesoscale (i.e., meso-γ). Benefiting from recent advances in computing power, numerics and physical parameterizations, modern large-eddy simulation (LES) models are capable of simulating Sc cells that resemble satellite imagery and aerial photographs, and play an instrumental role in advancing our understanding of Sc structure and variability (Stevens et al. 1998; Kogan 2006; Wang and Feingold 2009; among others). For example, based on LES simulations, Feingold et al. (2010) demonstrated that precipitation and the associated evaporative cooling are essential to the formation of open cells with much reduced albedo.

Over the last decade, several field observations have been conducted over tropical or subtropical oceans with a focus on Sc [e.g., Eastern Pacific Investigation of Climate Processes in the Coupled Ocean–Atmosphere System (EPIC; Bretherton et al. 2004) and Second Dynamics and Chemistry of Marine Stratocumulus field study (DYCOMS II; Stevens et al. 2003)]. More recently, the Variability of American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study (VOCALS; Wood et al. 2011b), the field observation component of which [VOCALS Regional Experiment (i.e., VOCALS-Rex)] took place over the SEP in the austral spring of 2008, represented a major international effort to improve understanding, model simulations, and predictions of the coupled ocean–atmosphere–land system over the SEP. One of the main goals of VOCALS is to advance our understanding of the marine cloud variability over subtropical oceans, including the formation and maintenance of pockets of open cells (POCs), which significantly reduce cloud fraction and albedo. One of the VOCALS-Rex research flights, research flight 6 (RF-6), drew broad interests because it documented POC embedded in the SEP stratus deck and a transition from closed cells to open cells (Wood et al. 2011a).

Recently, two possible POC formation mechanisms were tested using LES simulations, namely, inhomogeneity in aerosol number concentration (Berner et al. 2011; Kazil et al. 2011) and variations in the stratocumulus-topped boundary layer (STBL) characteristics, such as depth, moisture, and temperature (Wang et al. 2010; Mechem et al. 2012). Using an LES model, Berner et al. (2011) examined the Sc evolution in an elongated domain with different cloud droplet number concentrations in three separate areas that are otherwise horizontally homogenous (i.e., in terms of both initial condition and large-scale forcing). They demonstrated that open-cell-like patterns with reduced albedo form in the clean air area, while the rest domain with a larger droplet number concentration is still overcast. Wang et al. (2010) suggested that the formation of POC was more sensitive to the moisture and temperature perturbations in the STBL than to the aerosol number concentration variations over the range of parameters they examined. This raises an interesting question about the impact of mesoscale waves on Sc variability, as vertical motion associated with gravity waves may introduce larger-amplitude perturbations in moisture, temperature, and STBL depth than those tested in Wang et al. (2010). In fact, diurnal waves that propagate offshore of northern Chile and southern Peru (e.g., Garreaud and Muñoz 2004; Muñoz 2008; Wood et al. 2009) were hypothesized to have a significant impact on the observed diurnal variation of Sc over the SEP (VOCALS-Rex SOP). According to satellite observations and numerical simulations in previous studies, the diurnal waves offshore of the northern Chile coast are characterized by an ascent band nearly parallel to the coastline with a width of about 400 km and a vertical extension of about 5 km (Garreaud and Muñoz 2004). Observations obtained from the VOCALS-Rex provided further evidence for the existence of these waves (Rahn and Garreaud 2010), and their dynamics and offshore decay distance were examined analytically by Jiang (2012).

In a broad sense, gravity waves are virtually ubiquitous in the troposphere and can be generated by a variety of sources (e.g., topography, hurricanes, frontal activities, and convection). An important and yet open question is how much influence gravity waves have on Sc variability. The objective of this study is to provide some insights into the response of a STBL to mesoscale wave forcing through systematic LES simulations over a range of wave parameters and aerosol number concentrations. The remainder of this paper is organized as follows. section 2 includes a brief description of the LES model, model configuration, characteristics of the sounding profiles, and the mesoscale waves enforced. The numerical results are presented in section 3. Section 4 includes concluding remarks.

2. Numerical configuration

a. LES model and numerical setup

The simulations are performed using a large-eddy simulation model based on the atmospheric component of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS1; Hodur 1997; referred to as COAMPS-LES). The COAMPS-LES model was described and evaluated in detail by Golaz et al. (2005). This same model was employed by Wang et al. (2008, 2012) for the study of vertical wind shear effects on STBL-top entrainment and turbulence structure. For this study, a selective monotonicity-reserving advection scheme described in Blossey and Durran (2008) is used for the advection of the potential temperature and moisture variables. The two-moment bulk microphysical scheme illustrated in Feingold et al. (1998) and Fu-Liou four-stream radiation code (Fu and Liou 1992) are used for cloud physics and longwave radiation calculation. For simplicity, the aerosol number concentration Na is specified and stays constant for each simulation, while the cloud droplet number concentration Nc can still be changed by microphysical processes, such as activation, evaporation, collection, coalescence, and sedimentation. In addition, to focus on the impact of dynamic wave forcing, the diurnal variation of the shortwave (i.e., solar) radiation is not taken into account.

The computational domain contains 351 × 351 grid points in the horizontal with a grid spacing of Δx = 200 m and periodic conditions applied along lateral boundaries. The grid spacing is chosen based on a set of sensitivity simulations over a smaller domain and in line with other similar studies [e.g., Δx = 300 m in Wang et al. (2010); Δx = 125 m in Berner et al. (2011)]. The relatively coarse horizontal resolution allows us to perform multiple simulations of cell development in a reasonably large domain (i.e., 70 km × 70 km). There are 100 vertical model levels with a uniform grid spacing of 30 m, which is comparable to Wang et al. (2010) and Mechem et al. (2012). It is noteworthy that some previous studies suggested that a finer vertical spacing (i.e., 5 m or less) should be used across an inversion to better resolve smaller-scale eddies and adequately represent the entrainment process (Bretherton et al. 1999; Stevens et al. 2005). We choose to use the relatively coarse vertical spacing because the cloud-top entrainment is not the focus of this study, and the resulted error in the entrainment rate is much smaller than the wave vertical velocity. The model top is located at 3 km, where the potential temperature and water vapor gradients are fixed throughout each simulation.

b. Sounding profiles and mesoscale wave forcing

The model is initialized homogeneously in the horizontal using the wind, potential temperature, and moisture profiles shown in Fig. 1. These profiles are constructed from observations obtained during VOCALS-Rex RF-6 on 28 October 2008 (Wood et al. 2011a), which documented pockets of open cells embedded in a stratocumulus deck over the southeastern Pacific. The mean winds are northwesterly above the STBL and are assumed to be in geostrophic balance. The STBL is approximately 1.3 km deep capped by a sharp inversion of 12.5 K, above which the water vapor mixing ratio decreases sharply. A detailed description of the cloud characteristics and large-scale conditions documented by VOCALS-Rex RF-6 can be found in Wood et al. (2011a). It is noteworthy that these profiles have been used in a few other LES studies (Wang et al. 2010; Berner et al. 2011) as well.

Fig. 1.
Fig. 1.

(a) Horizontal wind components (m s−1), (b) potential temperature (K), and (c) water vapor mixing ratio (g kg−1) profiles derived from VOCALS RF-6 measurements.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

In our simulations, two separate subsidence (i.e., divergence) terms are included, representing the synoptic-scale and mesoscale wave forcing, respectively. Associated with the subtropical Pacific high pressure system, the VOCALS-Rex study area was constantly under the influence of large-scale subsidence over the observational period (Wood et al. 2011b). Previous studies found that the large-scale subsidence exhibits strong diurnal variations and is difficult to derive from in situ measurements (Bretherton et al. 2004; Garreaud and Muñoz 2004; Wood et al. 2009). In LES simulations of this same case, a constant divergence of 1.33 × 10−6 s−1 was used throughout the model depth in Berner et al. (2011) and 1.67 × 10−6 s−1 was used across the depth of the boundary layer in Wang et al. (2010). These values are based on an estimation from the satellite-derived sea level winds (Rahn and Garreaud 2010) and the National Centers for Environmental Prediction reanalysis (Wood et al. 2011b). In this study, we use a constant divergence of 2 × 10−6 s−1 up to the bottom of the inversion Zi. This value is slightly larger than those in Wang et al. (2010) and Berner et al. (2011), and results in a better balance with the growth of the boundary layer depth due to the STBL-top entrainment in our simulations.

As for the mesoscale gravity wave forcing, we consider a monotonic plane wave in a general form of , where denotes the amplitude of the divergence induced by a mesoscale wave, is the wave frequency, and T is the period; k and m are the horizontal and vertical wavenumbers, respectively. According to previous studies (e.g., Garreaud and Muñoz 2004), the typical horizontal length scale of a diurnal wave over the southeastern Pacific is on the order of 400 km, which is much longer than a typical cloud cell size (L ~ 30 km, i.e., meso-γ) and is well within the meso-α range (Orlanski 1975; therefore, it is still referred to as mesoscale wave forcing as opposed to synoptic conditions). The vertical extension of an upsidence wave (i.e., a half of the vertical wavelength) is about 5 km. For simplicity, we assume the horizontal and vertical wavelengths of the mesoscale waves of interest are much longer than the cloud cell size (i.e., , where L is the characteristic cloud cell size) and depth (i.e., , where Zi ~ 1 km), and accordingly, the horizontal advection associated with a wave-generated gradient is ignored. This allows us to simplify the mesoscale wave as
e1
Accordingly, the vertical velocity amplitude associated with the mesoscale wave (i.e.,; note that W denotes the wave-induced vertical velocity and that w is used for vertical velocity associated with model-resolved turbulence and mesoscale circulations) increases linearly in the STBL to the bottom of the inversion, and is equal to at the inversion base and aloft. It is noteworthy that the mesoscale subsidence is included in the LES model in the same way as the synoptic-scale subsidence, and this is justified by the scale analysis above (Sorbjan 2005). The mesoscale wave forcing actually starts at t = 2 h, and the first 2 h of each integration characterized by abrupt boundary layer adjustment and fast development of turbulence is considered the model spinup period and discarded.

3. Results

To understand the response of Sc and STBL to mesoscale wave forcing over a relatively broad parameter space, 18 simulations have been carried out for a variety of mesoscale wave parameters and aerosol number concentrations (Table 1). These simulations are grouped into three sets, namely, the control set (C), the aerosol concentration sensitivity set (A), and the wave frequency sensitivity set (F), and the results are summarized in sections 3a, 3b, and 3c, respectively.

Table 1.

List of all simulations and the corresponding acronyms and parameters. The simulations are named as follows: the first letter represents wave frequency (i.e., Q, S, and D for quarter-diurnal, semidiurnal, and diurnal, respectively), the second corresponds to wave amplitude (i.e., W, M, and S for weak, moderate, and strong, respectively), the last two denote wave phase (i.e., AD, DA, and AF for ascent–descent, descent–ascent, and ascent–flat), and the number corresponds to the aerosol number. The reference simulations free of mesoscale wave forcing are referred to as NMWF. The simulations are grouped into three sets; refer to section 3 for details.

Table 1.

a. Control set of simulations

We start with comparisons of six 14-h simulations (referred to as the control set for the convenience of description) with Na = 100 cm−3, including a reference simulation free of mesoscale wave forcing (NMWF-100) and five simulations forced by semidiurnal waves. Among them, three simulations are forced by semidiurnal waves with the same phase, φ0 = 0, but with differentwave amplitudes, namely, SWAD-100, SMAD-100, and SSAD-100, corresponding to a maximum divergence Dm0 of 10−5 (weak), 1.5 × 10−5 (moderate), and 2 × 10−5 s−1 (strong), respectively (see Table 1 for a list of parameters and acronyms). The resulted vertical velocity W maxima at the STBL top and aloft are approximately 13, 20, and 26 mm s−1. The maximum W for the weak wave forcing simulations is comparable to those derived from mesoscale numerical simulations over the SEP by Garreaud and Muñoz (2004). It is noteworthy that, in one of their LES simulations, Wang et al. (2010) included a 15-km-wide wavelike upward motion and found that, while the liquid water path (LWP) shows a sizable increase, the impact of the upward motion on cloud structures is relatively insignificant. The mesoscale wave forcing used in this study aims to mimic the upsidence waves offshore of the Chilean and Peruvian coastlines and, accordingly, the wavelength and wave period are much longer (i.e., hundreds of kilometers, quarter-diurnal to diurnal).

For φ0 = 0, the STBL first experiences a 6-h (i.e., 2–8 h) ascent with a W maximum at 5 h, followed by a 6-h descent (i.e., 8–14 h) with the strongest descent at 11 h (Fig. 2). Correspondingly, the STBL depth increases between 2 and 8 h and decreases between 8 and 14 h. The wave-induced STBL depth change (or the vertical displacement of the inversion induced by the mesoscale wave) η reaches its maximum at t = 8 h, given by , which is approximately 180, 268, and 358 m for the weak, moderate, and strong semidiurnal wave forcing, respectively, and returns to zero at the end of a whole wave cycle. The other two simulations are SMDA-100 and SMAF-100, which are identical to SMAD-100 except that φ = π in SMDA-100 (i.e., 6-h descent followed by 6-h ascent, Fig. 2), and the wave forcing is only applied to the 2–8-h period in SMAF-100 (i.e., 6-h half-sine ascent followed by 6 h free of wave forcing). We focus on the results from four of the six simulations, namely, NMWF-100, SWAD-100, SMAD-100, and SMDA-100 (Figs. 37).

Fig. 2.
Fig. 2.

The normalized vertical motion W and the corresponding vertical displacement of the STBL top η for the specified mesoscale waves. The four pairs of curves correspond to semidiurnal waves with ascent–descent (SXAD) and descent–ascent (SXDA) phases, a half semidiurnal wave (SXAF), and a quarter-diurnal wave (QXAD). The letter “X” in the legend denotes wave amplitude (i.e., weak, moderate, or strong). The SXAD pair corresponds to a diurnal wave (i.e., DXAD) when the time axis runs from 2 to 26 h.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 3.
Fig. 3.

Time series of six domain-average variables derived from the baseline set of simulations. These variables are (a) the proportional increase of the STBL depth (i.e., Zi/Zi0 − 1), (b) cloud fraction, (c) rain rate (mm day−1), (d) vertical velocity maximum (m s−1), (e) sensible heat flux (W m−2), and (f) latent heat flux (W m−2). For NMWF-100, the cloud fraction is nearly unity in (b) and the surface rain rate is virtually zero in (c), which is not visible.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 4.
Fig. 4.

Time series of the domain-average (a) LWP (kg m−2), (b) albedo, (c) scaled LWP variances [defined as ], and (d) skewness of LWP [i.e., ] for four simulations from the control set.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 5.
Fig. 5.

Time–height plots of (a)–(d) cloud water mixing ratio (color, g kg−1) and liquid potential temperature (white contours, interval = 5 K), (e)–(h) RWF (color, mm day−1) and (contour, interval = 0.1 m2 s−2), and (i)–(l) (color, m3 s−3) and buoyancy flux (, W m−2, interval = 10 W m−3, only positive values are contoured) for simulations (first column) NMWF-100, (second column) SWAD-100, (third column) SMAD-100, and (fourth column) SMDA-100.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 6.
Fig. 6.

Plan views of the albedo valid at 5, 8, 11, and 14 h derived from simulations (a)–(d) NMWF-100, (e)–(h) SWAD-100, (i)–(l) SMAF-100, and (m)–(p) SWDA-100.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 7.
Fig. 7.

Horizontal domain-average profiles of (a) water vapor (g kg−1), (b) (m2 s−2), and (c) liquid potential temperature (K) valid at 2, 5, and 8 h for SMAD-100.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

In the absence of mesoscale wave forcing (NMWF-100), the STBL depth grows slowly with the integration time (Fig. 3a), implying that the growth of the boundary layer depth due to the STBL-top entrainment is still slightly faster than the specified large-scale subsidence. In accordance with the STBL-top entrainment and growth of the STBL depth, the domain-average LWP and pseudoalbedo (Figs. 4a,b) decrease slowly. The STBL turbulence weakens in accordance with weaker radiative cooling near the cloud top (Fig. 5e). The pseudoalbedo (referred to as albedo for short) is determined by integrating both the cloud and rain spectra for calculating the optical cross-section area in the entire column, following Feingold et al. (1997). Toward the end of the simulation (i.e., t > 10 h), light drizzle precipitation (~1 mm day−1) occurs in the cloud layer, and yet little precipitation reaches the surface (Figs. 3c, 5e). The buoyancy flux is characterized by a positive maximum in the cloud layer, associated with radiative cooling, positive values right above the surface on account of the surface heating, and nearly zero or negative between (Fig. 5i). While the cloud fraction remains nearly unity throughout the simulation, the w maximum (Fig. 3d) as well as the LWP variance and skewness (Figs. 4c,d) increases slowly with time, implying development of mesoscale circulations. Accordingly, cellularization occurs (Figs. 6b,c) and the average cell size tends to grow with time as reported in previous studies (e.g., Wang and Feingold 2009). By the end of the simulation, the cloud morphology is closed cellular (Fig. 6d).

The two simulations forced by semidiurnal waves corresponding to φ0= 0 (i.e., SWAD-100 and SMAD-100) share many similar features that are profoundly different from the reference simulation (NMWF-100). Compared to the reference simulation, the STBL grows faster between 2 and 8 h and slower (or decreases) from 8 to 14 h (i.e., the descending phase, Fig. 2a), associated with the wave-induced stretching or squashing of the STBL. Accordingly, both the LWP and albedo oscillate approximately in phase with the specified wave forcing (Figs. 4a,b). It is noteworthy that the ascent-induced LWP increase includes contributions from the increase of cloud water mixing ratio and the thickening of the cloud layer; the former is approximately proportional to W, and the latter is more in phase with the vertical displacement of the cloud layer. Therefore, if ignoring precipitation and cloud-top entrainment, then the LWP should reach the maximum between 5 and 8 h.

The cloud fraction is near unity for the first 8 h and decreases after the wave-induced descent starts (Fig. 3b). Precipitation in SWAD-100 and SMAD-100 is much more intense than in NMWF-100 and is able to reach the surface (Fig. 3c). The domain-average rainwater flux (RWF) is characterized by tilting linear patterns (Figs. 5f,g), associated with the falling of rain drops. The mean rainwater terminal velocity estimated from the slope (i.e., Δzt) of these linear patterns is about 0.5–1 m s−1, which is comparable to typical drizzle falling speeds (e.g., Gerber and Frick 2012). As suggested by previous studies (e.g., Feingold et al. 2010), precipitation has a dramatic impact on the surface sensible and latent fluxes, turbulence, mesoscale circulations, and cloud morphology. Corresponding to the precipitation maxima, the average cloud depth and LWP show abrupt decreases in both simulations (Figs. 4a, 5b,c). The subcloud layer becomes more stratified on account of rainwater evaporation, and consequently, the turbulence (i.e., , Figs. 5f,g, 7b) profiles exhibit a weak minimum below the cloud base, indicative of the decoupled vertical structure. The surface sensible (latent) heat flux increases (decreases) sharply between 5 and 8 h, in contrast to the slow monotonic decrease with time in the reference simulation (Figs. 3e,f), likely due to the evaporative cooling of low-level air associated with drizzling. The subcloud layer is moistened during the precipitation episodes (Fig. 7a), and simultaneously, the buoyance flux exhibits a positive maximum below 300 m (Figs. 5j,k), more evidence of the low-level evaporation and surface cold pooling. It has been suggested that the cold pooling associated with precipitation and low-level evaporation plays an instrumental role in driving mesoscale circulations in the STBL and forcing a transition from closed to open cellular patterns (e.g., Savic-Jovcic and Stevens 2008; Xue et al. 2008; Feingold et al. 2010). In accordance with the precipitation maximum, the w maximum, the LWP variance, and skewness increase rapidly (Figs. 4c,d), implying the strengthening of mesoscale circulations. The evolution of the third moment of vertical velocity (i.e., , Figs. 5j,k) further confirms the importance of precipitation in the Sc cellularization and formation of open cells. As shown in the reference simulation, is negative below the cloud top and weakly positive approximately in the lowest 300 m, implying more intense and narrower downdrafts amid widespread ascent, associated with radiative cooling aloft, which is typical for closed cells. Similar profiles are present in the wave-forcing runs until precipitation occurs. A sudden change of from negative to significantly large positive values all the way to the cloud base occurs in SWAD-100 and SMAD-100 in accordance to precipitation (Figs. 5j,k), implying that precipitation and low-level cooling generate narrow but intense updrafts, one of the key characteristics of open cells. This is consistent with the albedo patterns in Fig. 6, which shows that, with wave forcing, the cloud clusters are more reflective during the ascending wave phase than in the corresponding reference simulation, indicating stronger updrafts. During the descending phase, the LWP decreases sharply with time, and so do the domain-average albedo and cloud fraction, apparently associated with the descent-induced adiabatic warming. From 11 to 14 h, the albedo patterns for SWAD-100 exhibit some characteristics of typical open cells (Figs. 6g,h) and become more open for stronger waves (i.e., SMAD-100, not shown). Precipitation in SMAD-100 is virtually shut down after 10 h by the stronger wave-induced descent (Fig. 5g).

Despite many common features shared by the two simulations, the differences in precipitation intensity, mesoscale circulations, and cloud patterns between them are significant, highlighting the sensitivity of the STBL and clouds to the wave amplitude. When forced by a moderate-amplitude wave (i.e., SMAD-100), the LWP shows a maximum around 4 h, 2 h earlier and about 20% larger than in the corresponding simulation with weak wave forcing (i.e., SWAD-100). The early arrival of the LWP maximum in SMAD-100 implies that precipitation overpowers the condensation induced by wave lifting; compared to SWAD-100, the maximum surface rain rate produced by SMAD-100 is approximately 5 times more (Fig. 3c). As shown in the time–height sections of the RWF (Figs. 5f,g), the SWAD-100 simulation produces an RWF maximum between 5 and 8 h and weak precipitation throughout the rest of the simulation. In contrast, there are two distinctive RWF maxima in the SMAD-100 simulation, centered at 5 and 7.5 h (Fig. 5g), which are substantially larger than from SWAD-100. The surface average rain rate exhibits two maxima in SMAD-100, located at 5.5 and 8 h, respectively (Fig. 3c), about half an hour behind their corresponding RWF maxima aloft. It is also noteworthy that while the two maxima are comparable, the first surface rainrate maximum in SMAD-100 is only half as large as the second maximum, suggesting that a large portion of the rainwater is evaporated during the first precipitation episode before it reaches the surface. The low-level air is closer to saturation during the second precipitation episode due to the moistening and cooling associated with evaporation during the first episode. Accordingly, the positive buoyancy flux maximum corresponding to the first episode is more pronounced. Even with weak wave forcing (i.e., SWAD-100), high albedo clusters appear during the ascent phase (Fig. 6f), corresponding to updrafts driven by evaporative cooling below. In general, during the descent wave phase, the cloud fraction becomes smaller when forced by a stronger wave and accordingly, the Sc cells become more open. For SMAD-100, the cloud fraction decreases to about 0.15 between 11 and 14 h (Fig. 3b) on account of stronger descent, suggesting Sc is largely dissipated except for some cloud filaments associated with narrow but strong updrafts. As expected, the precipitation becomes even more intense between 2 and 8 h in SSAD-100 (not shown) than in SMAD-100, in accordance with the stronger ascent and the cellular patterns develop earlier and are more open during the wave-induced descent.

In the SMAF-100 simulation, only a half-sine wave is enforced during the 2–8 h to mimic an asymmetric wave (i.e., stronger ascent and weaker descent). The SMAD-100 and SMAF-100 simulations are identical for the first 8 h. From 8 to 14 h, the STBL top in SMAF-100 stays aloft (Fig. 2b) after the wave forcing terminates as opposed to the rest of the simulations, in which the STBL approximately returns to its original depth after a whole wave cycle. In absence of the wave-forced descent, the precipitation continues after the wave forcing terminates (not shown) and the w maximum and are significantly larger than those from SMAD-100 for the 8–14 h period, indicating stronger mesoscale circulations. While the cloud fraction is still near 100%, the albedo and LWP show a moderate decrease in SMAF-100, probably associated with Sc cellularization, as shown in the albedo patterns (Figs. 6j–l). Accordingly, the clouds are less open than those in the simulations with the wave-induced descent.

At last, when forced by a wave starting with its descent phase (i.e., SMDA-100), there is little precipitation in the first 11 h (Fig. 5h) and the clouds are largely dissipated during the descent phase. The boundary layer turbulence is much weaker than the reference simulation, in accordance with reduced cloud-top cooling. It is also noteworthy that, compared to NMWF-100 and SWAD-100, the mean BL top of SMDA-100 is about 15% lower, indicative of less STBL-top entrainment (Fig. 3a). Overall, the evolution of the average albedo and LWP is approximately in phase with the oscillation of the wave-induced STBL depth. During the descent period, the w maximum and decrease (Fig. 3d, 5l), indicating that the wave-induced descent tends to weaken mesoscale circulations. However, the scaled LWP variances and skewness increase (Figs. 4c,d) in accordance with the formation of scattered clouds, presumably due to adiabatic warming associated with gravity wave-induced descent. Precipitation occurs over the last 3 h of the integration, associated with the recovery of the LWP and albedo. Qualitatively, the cloud coverage and morphology of SMDA-100 and the reference run NMWF-100 at 11 and 14 h (Figs. 6c,d,o,p) are rather similar, characterized by closed cellular patterns, except that the cloud cells in the reference simulation are better organized and larger in size. It appears that the wave forcing interrupts the development and growth of mesoscale cells in SMDA-100. In addition, the weakened turbulence and STBL-top entrainment associated with cloud reduction in SMDA-100 may also contribute to the difference. It is worth mentioning that the wave-induced STBL depth change is always positive during an ascent–descent cycle, and therefore it tends to increase the condensation rate in clouds through adiabatic cooling. On the contrary, the STBL depth experiences a negative change over a descent–ascent wave cycle, and therefore it tends to reduce condensation and weaken or inhibit precipitation. In summary, the influence of a passing wave with a descent–ascent phase on the STBL appears to be less significant than a wave with a comparable amplitude but with an ascent–descent phase.

b. Aerosol number concentration

We have demonstrated that, with a moderate aerosol number concentration (i.e., Na = 100 cm−3), gravity wave forcing can significantly modify the albedo and morphology of marine stratocumuli through triggering or enhancing precipitation and dissipating clouds. As reported in previous studies, precipitation of marine stratocumuli is sensitive to the aerosol number concentration. To examine the sensitivity of the stratocumulus evolution under mesoscale wave forcing to the aerosol number concentration, we have performed several pairs of simulations with aerosol number concentrations varying from 30 to 200 cm−3 with or without wave forcing (see set A in Table 1), and the results from the Na = 50 and 200 cm−3 pairs are summarized in Figs. 811, representing clean and dirty air examples, respectively.

Fig. 8.
Fig. 8.

As in Fig. 3, but for two pairs of simulations for Na = 200 and 50 cm−3 with semidiurnal weak- and moderate-amplitude wave forcing.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 9.
Fig. 9.

As in Fig. 4, but for two pairs of simulations for Na = 200 and 50 cm−3 with semidiurnal weak- and moderate-amplitude wave forcing.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 10.
Fig. 10.

As in Fig. 5, but for simulations (left to right) NMWF-50, NMWF-200, SMAD-50, and SMAD-200.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 11.
Fig. 11.

As in Fig. 6, but for simulations (a)–(d) NMWF-50, (e)–(h) NMWF-200, (i)–(l) SMAD-50, and (m)–(p) SMAD-200.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

It is evident that, even under an identical wave forcing, the characteristics of Sc vary significantly with Na. When the air is clean (e.g., Na = 50 cm−3), precipitation occurs even in the absence of wave forcing (Figs. 8c, 10e) and the intensity oscillates with a period of approximately 2 h, which is comparable to those documented in Feingold et al. (2010). As a result of precipitation, the average LWP and albedo are significantly smaller than the corresponding simulations with more polluted air (e.g., NMWF-200 in Figs. 9a,b, 10a,b, and NMWF-100 in Figs. 4, 5). The surface sensible (latent) heat flux is substantially larger (smaller) than in NMWF-200 (Figs. 8e,f), indicative of stronger low-level evaporative cooling in accordance with precipitation. Correspondingly, the w maximum (Fig. 8d), LWP variance and skewness, and (Figs. 9c,d, 10i) are larger and increase steadily with time, implying vigorous mesoscale circulations and cellularization, likely driven by evaporative cooling. As shown in the albedo plan views (Figs. 11a–d), cells develop in NMWF-50 accordingly. In the late hours of the integration, although the cloud fraction is still close to unity, the cellular patterns in Figs. 11c,d resemble typical open cells. When forced by a moderate-amplitude semidiurnal wave (i.e., SMAD-50), the precipitation rate at the surface is nearly quadrupled and characterized by a maximum at 7 h (Fig. 8c). The surface sensible (latent) heat flux is consistently larger (smaller), indicative of stronger evaporative cooling than in NMWF-50. Consequently, mesoscale circulations become much more vigorous as evidenced in the w maximum (Fig. 8d) and (Fig. 10k). The large positive values suggest enhanced mesoscale circulations characterized by narrow and strong updrafts. This is consistent with the evolution of albedo patterns; highly-reflective cloud filaments are evident at 8 h (Fig. 11j), approximately the time when reaches maxima. Both the albedo and LWP decrease rapidly after the wave-induced descent starts (Figs. 9a,b), and the average precipitation weakens substantially. Consequently, the w maximum and decrease, implying weakened updrafts in SMAD-50 between 8 and 14 h. The albedo shows clear areas enclosed by bright polygon-shaped cloud filaments, resembling typical open cells observed by satellites (Figs. 11k,l). It is interesting to compare NMWF-50 and SMAD-100, which produce comparable precipitation rate maxima at the surface, and accordingly, after 5 h, the average surface sensible (latent) heat fluxes are close to each other in the two simulations (Figs. 3e,f, 8e,f). Driven by evaporative cooling, both simulations are characterized by vigorous mesoscale circulations, narrow concentrated updrafts, and cellular clouds. The difference becomes significant after the descent starts in SMAD-100. While mesoscale circulations weaken some, likely due to weaker precipitation, the descent dissipates clouds, increases the scaled variances of LWP, and accelerates the transition from closed cells to open cells. Similarly, cellular patterns in SMAD-30 are better developed and more open than in NMWF-30 between 8 and 14 h on account of more intense precipitation during the ascent wave phase and cloud dissipation during the descent wave phase (not shown).

When the air is polluted (Na = 200 cm−3), there is little precipitation in both the no-wave-forcing reference simulation (NMWF-200, Fig. 10f) and the simulation forced by a moderate-amplitude semidiurnal wave (SMAD-200, Fig. 10h). In the absence of precipitation, the surface sensible and latent heat fluxes show slow monotonic decrease with time. For SMAD-200, the average LWP and albedo are above their initial values (Fig. 9b) and the third-moment of w′ (i.e., ) is predominantly negative in the first 8 h (Fig. 10l), implying widespread upward motion and more concentrated downdrafts, consistent with the characteristics of closed cells (Fig. 11n). During the descent phase, becomes close to zero, and in contrast, the skewness of LWP increases, associated with the descent-induced cloud dissipation. It is instructive to compare the albedo patterns in NMWF-200 and SMAD-200 at 11 and 14 h (Figs. 11g,h,o,p). The former shows typical closed-cell patterns, and the latter is characterized by scattered and less organized clouds with much reduced albedo. The dramatic differences in the cloud coverage and albedo between NMWF-200 and SMAD-200 near the end of the simulations may appear counterintuitive, considering that the STBL depths of the two are comparable and there is no significant precipitation during the integration. Further inspection identifies two processes in SMAD-200 that contribute to the differences, namely, the enhanced evaporation at the cloud top due to stronger entrainment associated with wave lifting and weakened upward water vapor transport on account of the decoupling structure in accordance with the descent (Figs. 10d,h).

c. Quarter diurnal and diurnal wave forcing

To some degree, the wave forcing introduces a new external time scale to the already complicated Sc–precipitation–aerosol–turbulence feedback system that involves multiple internal time scales. Naturally, we expect the response of Sc to a monotonic wave varies with the wave period. In this section, we examine two additional pairs of simulations forced by quarter diurnal and diurnal waves, respectively (Figs. 1214).

Fig. 12.
Fig. 12.

As in Fig. 5, but for (left to right) QWAD-100, QWAD-50, DWAD-100, and DWAD-50.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 13.
Fig. 13.

Plan views of the albedo valid at t = 3.5, 5, 6.5, 8, 9.5, 11, 12.5, and 14 h derived from the two quarter-diurnal simulations, (a)–(h) QWAD-100 and (i)–(p) QWAD-50.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

Fig. 14.
Fig. 14.

Plan views of the albedo at t = 5, 8, 11, 14, 17, 20, 23, and 26 h for (a)–(h) DWAD-100 and (i)–(p) DWAD-50.

Citation: Journal of the Atmospheric Sciences 69, 12; 10.1175/JAS-D-12-0135.1

The aerosol number concentrations for the pair of simulations with quarter-diurnal wave forcing are 100 and 50 cm−3, referred to as QWAD-100 and QWAD-50, respectively. The wave period is T = 6 h, and the divergence amplitude in the STBL is Dm0 = 2 × 10−5 s−1. It is noteworthy that we refer to the wave as weak, as it produces the same maximum vertical displacement of the STBL top (i.e., ηm) as the weak semidiurnal waves with Dm0 = 10−5 s−1. However, the vertical velocity maximum Wm of the weak quarter-diurnal wave is twice as large as that of the weak semidiurnal wave. The integration time for the two quarter-diurnal simulations is 14 h, including a 2-h spinup period, 3-h ascent phase, 3-h descent phase, and 6 h free of wave forcing (Fig. 2). To accommodate a whole diurnal cycle, the integration time for the two diurnal simulations, DWAD-100 and DWAD-50, is 26 h, including 2-h spinup, 12-h ascent, and 12-h descent. The maximum divergence is Dm0 = 5 × 10−6 s−1 for the diurnal simulations, which gives the same maximum vertical displacement of the STBL top as in the SWAD and QWAD simulations.

The domain-average cloud water and RWF for the simulations with quarter-diurnal wave forcing (Figs. 12a,b,e,f) are evidently different than the corresponding simulations forced by semidiurnal waves (Figs. 5b,f). During the 3-h ascent phase, QWAD-100 produces a precipitation (i.e., RWF) maximum comparable to those by SMAD-100, probably because the two simulations have the same maximum wave vertical velocity. Unlike SMAD-100, there is only one RWF maximum in QWAD-100, likely due to the shorter ascent time (i.e., 3 h, comparable to the precipitation oscillation period, ~2 h). In accordance with the precipitation during the ascent phase, the domain-average becomes positive in the upper STBL (Fig. 12i). However, while the intense precipitation between 3 and 5 h decreases the average albedo some, there are no well-developed cellular patterns before the descent phase starts (Fig. 13b). The descent between 5 and 8 h virtually shuts down the precipitation and substantially reduces the LWP. Correspondingly, the upper STBL becomes profoundly less turbulent due to the reduction in the cloud-top radiative cooling (Fig. 12e). At 6.5 h, the albedo patterns are characterized by scattered high-reflection cloud patches, likely associated with the strong updrafts driven by precipitation and cold pooling, among clear areas (Figs. 13c,d), suggesting that the fast descent dissipates clouds before the cellular network develops. The clouds are better organized at hour 8. It is interesting that, after the wave-induced descent is over, the cloud water slowly recovers (Fig. 12a), and the average albedo (not shown) and the RWF become progressively larger. The relaxation time Tr (defined as , where α stands for LWP or albedo and denotes the maximum difference of α and its equilibrium value, which decreases to in seconds), for the domain-average LWP and albedo is approximately 6 h. Accordingly, increases with time for the last 6 h (Fig. 12i), implying stronger mesoscale circulations and possible cellularization. While the cloud fraction keeps increasing, cellular patterns develop and exhibit general characteristics of open cells (Figs. 13e–h). The results from QWAD-50 are qualitatively similar to QWAD-100 with the following exceptions. First, with a smaller aerosol number concentration, the precipitation occurs earlier and is noticeably more intense. Correspondingly, is larger, indicative of stronger mesoscale circulations. Second, the cellularization develops faster in QWAD-50, probably due to the earlier and more intense precipitation during the 3-h ascent phase. The albedo patterns at 5 h in general resemble closed cells and the subsequent wave-induced descent forces a fast transition from closed to open cells (Figs. 13j–k).

It is interesting to compare the cloud patterns in the quarter diurnal wave runs and those in the corresponding no-wave-forcing runs over the last 6 h. For the period of 8–14 h, the cloud morphology is closed cellular in NMWF-100 and open cellular in QWAD-100 (Figs. 6b–d, 13d,f,h) The difference is likely due to the much enhanced precipitation between 2 and 5 h in the QWAD-100 simulation, which significantly reduces liquid water and cloud fraction. In addition, low-level cold pooling and subcloud-layer decoupling occur in QWAD-100, associated with the more intense precipitation. The differences between QWAD-100 and NMWF-100 in terms of the average LWP, albedo, and cloud coverage decrease with time between 8 and 14 h. In general, after the wave forcing is over, the STBL tends to relax slowly toward the corresponding no-wave-forcing simulation and the characteristic time scale for such relaxation is on the order of 6 h. Both NMWF-50 and QWAD-50 are characterized by typical open-cellular networks for t > 6.5 h, associated with precipitation substantially more intense than in simulations with Na = 100 cm−3. There are some noticeable differences in the cloud morphology during the period of 8–14 h between the two simulations. First, the cells are more open in QWAD-50 due to the ascent-enhanced precipitation. Similar to the pair with Na = 100 cm−3, the reduced albedo and cloud coverage in the simulation with wave forcing slowly increase with time and the relaxation time is comparable to the Na = 100 cm−3 pair. Second, while the horizontal dimensions of the Sc cells grow with time in both simulations, the average cell sizes in QWAD-50 are smaller than those in NMWF-50 at the same hours, likely associated with more intense precipitation, which interrupts the cell growth. The third pair of such simulations for Na = 200 cm−3 (not shown) indicate that, for polluted air, the impact of a passing mesoscale wave on a STBL and cloud morphology is more transient because of the lack of precipitation. In polluted air with overpopulated aerosol particles, the albedo shows a small increase during the ascent phase of the enforced wave and a decrease during the descent phase.

Finally, we show two simulations forced by a diurnal wave while keeping in mind that the shortwave radiation is not considered in this study. The DWAD-100 produces a similar evolution of RWF with a maximum at 8 h when W reaches its maximum and reduced WRF throughout the rest of the simulation. Similarly, shows an abrupt change from negative to positive in the upper portion of the STBL (Figs. 12k,l), and accordingly, the albedo exhibits more cellular structure (Fig. 14) than in the reference run free of wave forcing. The cloud coverage is substantially reduced during the descent phase (Figs. 14e–h), on account of the persistent precipitation during ascent and cloud dissipation associated with wave-induced descent. As expected, more intense precipitation occurs in DWAD-50 than in DWAD-100, and the precipitation is much enhanced in the first 12-h ascent phase and reduced in the descent phase. Unlike the episodic precipitation in NMWF-50, the precipitation in DWAD-50 is steadier during the ascent, which drives strong mesoscale circulations in the STBL as indicated by the larger positive maxima in (Fig. 12l). The cloud morphology of DWAD-50 (Figs. 14i–l) between 2 and 14 h resembles that of NMWF-50 (Figs. 11a–d), characterized by highly reflective filaments, typical for open cells, and the cells become more open during the descent phase, associated with cloud dissipation.

4. Summary and concluding remarks

The objective of this study is to shed some light on the impact of gravity waves on marine stratocumuli and stratocumulus-topped boundary layer. We have carried out 18 simulations using an LES model initialized with a composite sounding from VOCALS-Rex measurements and forced by gravity waves with different amplitudes, frequencies, and phases. The results indicate that the cloud morphology and albedo often exhibit dramatic changes in response to the wave-induced vertical motion. A transition from closed cells to open cells occurs in several simulations associated with gravity wave forcing, implying that gravity waves may play a role in the formation of POCs over the open ocean. In general, wave-induced ascent tends to increase the liquid water content and therefore enhance precipitation under proper conditions. On the contrary, during the descending phase of a wave, the cloud albedo decreases significantly as the cloud dissipates, associated with subsidence and adiabatic warming. Depending on the aerosol concentration, the descent may break up overcast clouds or lead to a transition from closed cells to open cells.

Our simulations confirm the crucial role of precipitation and the associated evaporative cooling in driving mesoscale circulations in an STBL, as suggested by previous studies. Wave-induced ascent tends to increase the cloud water mixing ratio as well as the cloud depth and therefore facilitate the growth of rain droplets. Consequently, precipitation can be significantly enhanced during the ascent phase of a wave. In accordance with the enhanced precipitation, the surface sensible heat flux increases, the latent heat flux decreases, and low-level relative humidity increases, indicative of evaporative cooling below the cloud level. The evaporative cooling or cold pooling is accompanied with strengthened mesoscale circulations, as evidenced in the increased w maximum and scaled LWP variances and LWP skewness. Precipitation also leads to a sudden increase of in the upper portion of the STBL from negatives to positives, implying a transition from a state characterized by widespread ascent and narrow downdrafts to narrow but strong updrafts amid weaker and more widespread descent. The former is typical for closed cells, and the latter is characteristic of open cells. Correspondingly, the transition from closed to open cells is evident in the albedo patterns of several simulations.

The timing and intensity of precipitation, and therefore the cloud morphology, vary with the wave characteristics. In general, when forced by a larger amplitude wave, the precipitation is more intense and tends to occur earlier, which leads to stronger mesoscale circulations and fast development of cellular patterns. If the wave period is long enough, a large-amplitude wave may cause episodic precipitation with a characteristic life span on the order of 2 h. The impact of a mesoscale wave on an STBL varies with the wave phase and period as well. An ascent–descent wave cycle has stronger influence on an STBL through enhancing precipitation, and its modification on the cloud morphology and albedo is irreversible. For the parameters examined in this study, it takes about 6 h to replenish the Sc through vertical turbulence transport of water vapor after the wave forcing is over. On the contrary, the influence of a descent–ascent wave cycle on an STBL is more transient and nearly reversible due to the overall negative change in the STBL depth and accordingly little or negative contribution to the precipitation. The dependence of precipitation and cloud cellularization on the wave period is more complicated. When the wave period is longer, the enhanced precipitation time is longer, and accordingly it allows more time for Sc cellularization. In contrast, a longer wave usually corresponds to a weaker vertical velocity (i.e., W) and consequently weaker precipitation.

The impact of gravity waves on precipitation is found to be more significant when the air is moderately polluted (i.e., Na ~ 75–150 cm−3). When Na is low, precipitation could occur even in the absence of wave forcing, and the increase in cloud water content due to wave lift could enhance the precipitation and accelerate the formation of open cells. For an intermediate Na, the increased cloud water during the ascending phase may trigger or significantly enhance precipitation and therefore lead to strengthened mesoscale circulations, cloud cellularization, and sometimes the transition from closed to open cells. For polluted air (i.e., Na ~ 200 cm−3 or larger), no substantial precipitation is observed even with relatively strong wave forcing. The wave-induced ascent (descent) only enhances (dissipates) the stratocumuli temporarily. However, the cloud water content may be modified by the enhanced entrainment during the ascent phase and reduced water vapor supply during the descent phase.

There are a few noteworthy limitations of this study. First, some of the results in this study might be significantly modified by the diurnal cycle of solar radiation, which is not considered here for simplicity. Presumably, the solar radiation may interfere with gravity wave forcing constructively or destructively in terms of their impacts on stratocumulus, depending on the phase difference between the two, which will be investigated in a future study. Second, the Na is fixed throughout each simulation. In the real world, Na can be reduced by precipitation and replenished by processes such as entrainment, turbulence mixing, and advection. The temporal and spatial variations of Na shall add an additional layer of complexity to the already complicated problem and will be examined in a future study. Furthermore, the gravity wave forcing is approximated as a sinusoidal vertical oscillation in time. Accordingly, the advection effect associated with wave-induced horizontal gradients is not discussed.

Acknowledgments

This research is supported by NRL Base Program PE 0601153N. Dr. A. Reinecke helped us with the implementation of the selective monotonic advection scheme. The authors also want to thank Drs. H. Wang and G. Feingold for the helpful discussions about the two-moment cloud microphysics. The primary sponsor of VOCALS is the U.S. National Science Foundation. The simulations were performed using the LES component of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS-LES) developed by the U.S. Naval Research Laboratory. Computational resources were supported by a grant of HPC time from the Department of Defense Major Shared Resource Centers.

REFERENCES

  • Berner, A. H., , C. S. Bretherton, , and R. Wood, 2011: Large-eddy simulation of mesoscale dynamics and entrainment around a pocket of open cells observed in VOCALS RF06. Atmos. Chem. Phys., 11, 10 52510 540.

    • Search Google Scholar
    • Export Citation
  • Blossey, P. N., , and D. R. Durran, 2008: Selective monotonicity preservation in scalar advection. J. Comput. Phys., 227, 51605183.

  • Bretherton, C. S., and Coauthors, 1999: An intercomparison of radiatively driven entrainment and turbulence in a smoke cloud, as simulated by different numerical models. Quart. J. Roy. Meteor. Soc., 125, 391423.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and Coauthors, 2004: The EPIC 2001 stratocumulus study. Bull. Amer. Meteor. Soc., 85, 967977.

  • de Szoeke, S. P., , S. Yuter, , D. Mechem, , C. W. Fairall, , C. Burleyson, , and P. Zuidema, 2012: Observations of stratocumulus clouds and their effect on the eastern Pacific surface heat budget along 20°S. J. Climate, in press.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., , R. Boers, , B. Stevens, , and W. R. Cotton, 1997: A modeling study of the effect of drizzle on cloud optical depth and susceptibility. J. Geophys. Res., 102 (D12), 13 52713 534.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., , R. L. Walko, , B. Stevens, , and W. R. Cotton, 1998: Simulations of marine stratocumulus using a new microphysical parameterization scheme. Atmos. Res., 47–48, 505528.

    • Search Google Scholar
    • Export Citation
  • Feingold, G., , I. Koren, , H. Wang, , H. Xue, , and W. Brewer, 2010: Precipitation-generated oscillations in open cellular cloud fields. Nature, 466, 849852.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., , and K.-N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 21392156.

    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., , and R. Muñoz, 2004: The diurnal cycle in circulation and cloudiness over the subtropical southeast Pacific: A modeling study. J. Climate, 17, 16991710.

    • Search Google Scholar
    • Export Citation
  • Gerber, H., , and G. Frick, 2012: Drizzle rates and large sea-salt nuclei in small cumulus. J. Geophys. Res., 117, D01205, doi:10.1029/2011JD016249.

    • Search Google Scholar
    • Export Citation
  • Golaz, J.-C., , S. Wang, , J. D. Doyle, , and J. M. Schmidt, 2005: COAMPS-LES: Model evaluation and analysis of second- and third-momentum vertical velocity budgets. Bound.-Layer Meteor., 116, 487517.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., , and D. A. Short, 1980: On the use of earth radiation budget statistics for studies of clouds and climate. J. Atmos. Sci., 37, 12331250.

    • Search Google Scholar
    • Export Citation
  • Hodur, R. M., 1997: The Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev., 125, 14141430.

    • Search Google Scholar
    • Export Citation
  • Jiang, Q., 2012: On offshore diurnal waves. J. Atmos. Sci., 69, 15621581.

  • Kazil, J., , H. Wang, , G. Feingold, , A. D. Clarke, , J. R. Snider, , and A. R. Bandy, 2011: Modeling chemical and aerosol processes in the transition from closed to open cells during VOCALS-Rex. Atmos. Chem. Phys., 11, 74917514.

    • Search Google Scholar
    • Export Citation
  • Kiehl, J. T., , and P. R. Gent, 2004: The Community Climate System Model, version 2. J. Climate, 17, 36663682.

  • Kogan, Y. L., 2006: Large-eddy simulation of air parcels in stratocumulus clouds: Time scales and spatial variability. J. Atmos. Sci., 63, 15431559.

    • Search Google Scholar
    • Export Citation
  • Ma, C.-C., , C. R. Mechoso, , A. W. Robertson, , and A. Arakawa, 1996: Peruvian stratus clouds and the tropical Pacific circulation: A coupled ocean – atmosphere GCM study. J. Climate, 9, 16351645.

    • Search Google Scholar
    • Export Citation
  • Mechem, D. B., , S. E. Yuter, , and S. P. de Szoeke, 2012: Thermodynamic and aerosol controls in southeast Pacific stratocumulus. J. Atmos. Sci., 69, 12501266.

    • Search Google Scholar
    • Export Citation
  • Muñoz, R. C., 2008: Diurnal cycle of surface winds over the subtropical southeast Pacific. J. Geophys. Res., 113, D13107, doi:10.1029/2008JD009957.

    • Search Google Scholar
    • Export Citation
  • Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527530.

  • Rahn, D. A., , and R. Garreaud, 2010: Marine boundary layer over the subtropical southeast Pacific during VOCALS-REx – Part 1: Mean structure and diurnal cycle. Atmos. Chem. Phys., 10, 44914506.

    • Search Google Scholar
    • Export Citation
  • Savic-Jovcic, V., , and B. Stevens, 2008: The structure and mesoscale organization of precipitating stratocumulus. J. Atmos. Sci., 65, 15871605.

    • Search Google Scholar
    • Export Citation
  • Sorbjan, Z., 2005: Large-eddy simulations of the atmospheric boundary layer. Advanced Topics, P. Zannetti, Ed., Vol. 2, Air Quality Modeling—Theories, Methodologies, Computational Techniques, and Available Databases and Software, EnviroComp Institute, 11–82.

  • Stevens, B., , W. R. Cotton, , G. Feingold, , and C. Moeng, 1998: Large-eddy simulations of strongly precipitating, shallow, stratocumulus-topped boundary layers. J. Atmos. Sci., 55, 36163638.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2003: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II. Bull. Amer. Meteor. Soc., 84, 579593.

  • Stevens, B., and Coauthors, 2005: Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Mon. Wea. Rev., 133, 14431462.

    • Search Google Scholar
    • Export Citation
  • Wang, H., , and G. Feingold, 2009: Modeling mesoscale cellular structures and drizzle in marine stratocumulus. Part I: Impact of drizzle on the formation and evolution of open cells. J. Atmos. Sci., 66, 32373255.

    • Search Google Scholar
    • Export Citation
  • Wang, H., , G. Feingold, , R. Wood, , and J. Kazil, 2010: Modelling microphysical and meteorological controls on precipitation and cloud cellular structure in southeast Pacific stratocumulus. Atmos. Chem. Phys., 10, 63476362.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , J.-C. Golaz, , and Q. Wang, 2008: Effect of intense wind shear across the inversion on stratocumulus clouds. Geophys. Res. Lett., 35, 15814, doi:10.1029/2008GL033865.

    • Search Google Scholar
    • Export Citation
  • Wang, S., , X. Zheng, , and Q. Jiang, 2012: Strongly sheared stratocumulus convection: An observationally based large-eddy simulation study. Atmos. Chem. Phys., 12, 52235235, doi:10.5194/acp-12-5223-2012,2012.

    • Search Google Scholar
    • Export Citation
  • Wittenberg, A. T., , A. Rosati, , N.-C. Lau, , and J. J. Ploshay, 2006: GFDL’s CM2 global coupled climate models. Part III: Tropical Pacific climate and ENSO. J. Climate, 19, 698722.

    • Search Google Scholar
    • Export Citation
  • Wood, R., , M. Kohler, , R. Bennartz, , and C. O’Dell, 2009: The diurnal cycle of surface divergence over the global oceans. Quart. J. Roy. Meteor. Soc., 135, 14841493.

    • Search Google Scholar
    • Export Citation
  • Wood, R., , C. S. Bretherton, , D. Leon, , A. D. Clarke, , P. Zuidema, , G. Allen, , and H. Coe, 2011a: An aircraft case study of the spatial transition from closed to open mesoscale cellular convection over the southeast Pacific. Atmos. Chem. Phys., 11, 23412370.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and Coauthors, 2011b: The VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-Rex): Goals, platforms, and field operations. Atmos. Chem. Phys., 11, 627654.

    • Search Google Scholar
    • Export Citation
  • Xue, H., , G. Feingold, , and B. Stevens, 2008: Aerosol effects on clouds, precipitation, and the organization of shallow cumulus convection. J. Atmos. Sci., 65, 392406.

    • Search Google Scholar
    • Export Citation
1

COAMPS is a registered trademark of the Naval Research Laboratory.

Save