• Ackerman, A. S., O. B. Toon, D. E. Stevens, and J. A. Coakley Jr., 2003: Enhancement of cloud cover and suppression of nocturnal drizzle in stratocumulus polluted by haze. Geophys. Res. Lett., 30 , 1381. doi:10.1029/2002GL016634.

    • Search Google Scholar
    • Export Citation
  • Ackerman, A. S., M. P. Kirkpatrick, D. E. Stevens, and O. B. Toon, 2004: The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature, 432 , 10141017.

    • Search Google Scholar
    • Export Citation
  • Albrecht, B., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245 , 12271230.

  • Coakley Jr., J. A., and C. D. Walsh, 2002: Limits to the aerosol indirect radiative effect derived from observations of ship tracks. J. Atmos. Sci., 59 , 668680.

    • Search Google Scholar
    • Export Citation
  • Coakley Jr., J. A., and Coauthors, 2000: The appearance and disappearance of ship tracks on large spatial scales. J. Atmos. Sci., 57 , 27652778.

    • Search Google Scholar
    • Export Citation
  • Comstock, K. K., S. E. Yuter, R. Wood, and C. S. Bretherton, 2007: The three-dimensional structure and kinematics of drizzling stratocumulus. Mon. Wea. Rev., 135 , 37673784.

    • Search Google Scholar
    • Export Citation
  • Conover, J. H., 1966: Anomalous cloud lines. J. Atmos. Sci., 23 , 778785.

  • Durkee, P. A., K. J. Noone, and R. T. Bluth, 2000: The Monterey Area Ship Track experiment. J. Atmos. Sci., 57 , 25232541.

  • Feingold, G., B. Stevens, W. R. Cotton, and A. S. Frisch, 1996: The relationship between drop in-cloud residence time and drizzle production in numerically simulated stratocumulus clouds. J. Atmos. Sci., 53 , 11081122.

    • 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
  • Ferek, R. J., and Coauthors, 2000: Drizzle suppression in ship tracks. J. Atmos. Sci., 57 , 27072728.

  • Garay, M. J., R. Davies, C. Averill, and J. A. Westphal, 2004: Actinoform clouds: Overlooked examples of cloud self-organization at the mesoscale. Bull. Amer. Meteor. Soc., 85 , 15851594.

    • Search Google Scholar
    • Export Citation
  • Gassó, S., 2008: Satellite observations of the impact of weak volcanic activity on marine clouds. J. Geophys. Res., 113 , D14S19. doi:10.1029/2007JD009106.

    • Search Google Scholar
    • Export Citation
  • Hill, A., G. Feingold, and H. Jiang, 2009: The influence of entrainment and mixing assumption on aerosol–cloud interactions in marine stratocumulus. J. Atmos. Sci., 66 , 14501464.

    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., and Coauthors, 2000: Emissions from ships with respect to their effects on clouds. J. Atmos. Sci., 57 , 25702590.

  • Jiang, H., G. Feingold, and W. R. Cotton, 2002: Simulations of aerosol-cloud-dynamical feedbacks resulting from entrainment of aerosol into the marine boundary layer during the Atlantic Stratocumulus Transition Experiment. J. Geophys. Res., 107 , 4813. doi:10.1029/2001JD001502.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., Y. L. Kogan, D. K. Lilly, D. W. Johnson, G. E. Innis, P. A. Durkee, and K. E. Nielsen, 2000: Modeling of ship effluent transport and its sensitivity to boundary layer structure. J. Atmos. Sci., 57 , 27792791.

    • Search Google Scholar
    • Export Citation
  • Lu, M. L., and J. H. Seinfeld, 2005: Study of the aerosol indirect effect by large-eddy simulation of marine stratocumulus. J. Atmos. Sci., 62 , 39093932.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 , (D14). 1666316682.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2000: The role of background cloud microphysics in the radiative formation of ship tracks. J. Atmos. Sci., 57 , 26072624.

    • Search Google Scholar
    • Export Citation
  • Radke, L. F., J. A. Coakley Jr., and M. D. King, 1989: Direct and remote sensing observations of the effects of ships on clouds. Science, 246 , 11461149.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., Y. J. Kaufman, and I. Koren, 2006: Switching cloud cover and dynamical regimes from open to closed Benard cells in response to the suppression of precipitation by aerosols. Atmos. Chem. Phys., 6 , 25032511.

    • 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
  • Segrin, M. S., J. A. Coakley, and W. R. Tahnk, 2007: MODIS observations of ship tracks in summertime stratus off the west coast of the United States. J. Atmos. Sci., 64 , 43304345.

    • Search Google Scholar
    • Export Citation
  • Sharon, T. M., B. A. Albrecht, H. H. Jonsson, P. Minnis, M. M. Khaiyer, T. M. van Reken, J. Seinfeld, and R. Flagan, 2006: Aerosol and cloud microphysical characteristics of rifts and gradients in maritime stratocumulus clouds. J. Atmos. Sci., 63 , 983997.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.

  • Stevens, B., W. R. Cotton, G. Feingold, and C-H. 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., G. Vali, K. Comstock, R. Wood, M. C. vanZanten, P. H. Austin, C. S. Bretherton, and D. H. Lenschow, 2005: Pockets of open cells and drizzle in marine stratocumulus. Bull. Amer. Meteor. Soc., 86 , 5157.

    • Search Google Scholar
    • Export Citation
  • vanZanten, M. C., and B. Stevens, 2005: Observations of the structure of heavily precipitating marine stratocumulus. J. Atmos. Sci., 62 , 43274342.

    • 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 , 32373256.

    • Search Google Scholar
    • Export Citation
  • Wang, H., W. C. Skamarock, and G. Feingold, 2009: Evaluation of scalar advection schemes in the Advanced Research WRF model using large-eddy simulations of aerosol–cloud interactions. Mon. Wea. Rev., 137 , 25472558.

    • Search Google Scholar
    • Export Citation
  • Wang, S., Q. Wang, and G. Feingold, 2003: Turbulence, condensation, and liquid water transport in numerically simulated nonprecipitating stratocumulus clouds. J. Atmos. Sci., 60 , 262278.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and D. L. Hartmann, 2006: Spatial variability of liquid water path in marine low cloud: The importance of mesoscale cellular convection. J. Climate, 19 , 17481764.

    • Search Google Scholar
    • Export Citation
  • Wood, R., K. K. Comstock, C. S. Bretherton, C. Cornish, J. Tomlinson, D. R. Collins, and C. Fairall, 2008: Open cellular structure in marine stratocumulus sheets. J. Geophys. Res., 113 , D12207. doi:10.1029/2007JD009371.

    • 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

    Snapshots of (left) cloud albedo field and (right) column-average CCN number concentration Nc at (a)–(c) t = 3, 6, and 9 h from experiment CONT with contours of cloud-base rain rate Rzb of 1, 10, and 20 mm day−1 superimposed.

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    The xz cross sections (at y = 20 km) from experiment CONT at (a)–(c) t = 3, 6, and 9 h. (left) Gray shaded areas denote clouds (0.01 g kg−1) and contours outline drizzle (0.01 mm day−1); arrows qualitatively represent wind perturbations (uw) with red for westerly (u > 0) and blue for easterly (u < 0). (right) Shaded colors indicate total particle number concentration (Nc + Nd), and contours water vapor mixing ratio perturbations (qυ; positive by solid lines and negative by dotted lines); perturbations are relative to the horizontal slab average at each level. Note that only one-third of the domain in the x direction is shown for clarity with the 160 < x <180-km portion of the domain attached (left) to depict the entire circulation on the cross sections.

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    Time evolution of x-wind perturbation (u; top color bar) at (top to next-to-bottom) three height levels (100, 450, and 750 m) and (bottom) cloud-base rain rate (Rzb; bottom color bar) for experiments (left) CONT and (right) CONT-C. All quantities are averaged over the y axis. Dotted lines mark the boundary between the circulation-affected region and open (closed) cells to its right (left). For clarity, arrows are drawn in the boundary region to indicate wind direction.

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    Snapshots of (left) cloud albedo field and (right) column-average ship-emitted CCN number concentration Nsc at (a)–(c) t = 3, 6, and 9 h from experiment SHIP-C with contours of cloud-base rain rate Rzb of 1, 10, and 20 mm day−1 superimposed. The arrow in the upper right plot (on the x axis) points to the x coordinate of the plume head at t = 3 h.

  • View in gallery

    As in Fig. 4, but from experiment SHIP-P; note that almost no precipitation with Rzb > 1 mm day−1 is observed at cloud base.

  • View in gallery

    (top two rows) Time evolution of y-wind perturbation (υ) at two height levels (100 and 750 m, respectively), (third row) LWP, and (bottom row) cloud-base rain rate (Rzb), with cloud-average ship-emitted CCN number concentrations (labeled contours) superimposed. All quantities are averaged over the x axis.

  • View in gallery

    (a)–(d) The yz cross-sections (at x = 20 km) of ship-emitted CCN concentration Nsc at t = (left) 3 and (right) 9 h for four experiments denoted at the upper left corner of each row. The yz wind perturbation (υw) vectors are superimposed to qualitatively indicate organized turbulent flow or circulation.

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    Conditional composites of x-wind perturbation u (dotted contours for −0.1, −0.5 and −1.0 m s−1 and solid contours for 0.1, 0.5, and 1.0 m s−1), rain rate Rr (shaded colors), and LWP (thick red lines; vertical axes on the right): (a)–(d) CONT, RRTM, FIXR, and NOR experiments.

  • View in gallery

    Conceptual diagrams illustrating the mesoscale circulation near the (a) open–closed-cell boundary and (b) ship track in precipitating open cells and its effect on redistributing CCN and moisture. In (a), the degree of CCN transport toward the open cells depends on the concentration of unactivated aerosol on the closed-cell side.

  • View in gallery

    Cloud-average ship-emitted CCN number concentration Nsc and changes in (top to bottom) drop number concentration Nd, LWP, cloud albedo αc, and cloud-base rain rate Rzb as a function of time for (a) clean and (b) polluted cases. The change is relative to each quantity in the corresponding control experiment. Solid lines are for averages in the ship-track zone (Nsc > 20 mg−1), dotted lines are for averages in the non-ship-track zone, and dashed lines are for the whole domain.

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    Cloud fraction in the ship track (solid lines) and the entire domain of the corresponding control experiment (dashed lines) as a function of time for the (a) clean (Nc = 60–150 mg−1) and (b) polluted (Nc = 210–300 mg−1) cases. The solid and dashed lines are overlaid in (b). The dotted line in both panels is from the sensitivity test in which background Nc changes abruptly from an initial value of 60 to 300 mg−1 at t = 6 h.

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Modeling Mesoscale Cellular Structures and Drizzle in Marine Stratocumulus. Part II: The Microphysics and Dynamics of the Boundary Region between Open and Closed Cells

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  • 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado, and NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 2 NOAA/Earth System Research Laboratory, Boulder, Colorado
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Abstract

This is the second of two companion papers on modeling of mesoscale cellular structures and drizzle in marine stratocumulus. In the first, aerosol–cloud–precipitation interactions and dynamical feedbacks were investigated to study the formation and evolution of open and closed cellular structures separately. In this paper, coexisting open and closed cells and how they influence one another are examined in a model domain of 180 × 60 × 1.5 km3. Simulations show that gradients in aerosol at the open–closed-cell boundary cause gradients in precipitation that generate a mesoscale circulation. The circulation promotes precipitation in the polluted closed cells but suppresses it in open cells by transporting water vapor to the closed-cell regime and carrying drier air and aerosol back to the open cells. The strength of this circulation depends on the contrast in precipitation under clean and polluted conditions at the boundary. Ship plumes emitted into clean, precipitating regions, simulated as a special case of a clean–polluted boundary, develop a similar circulation. Drizzle in the ship track is first suppressed by the increase in aerosol particles but later recovers and becomes even stronger because the local circulation enhances liquid water path owing to the convergence of water vapor from the region adjacent to the track. This circulation modifies the transport and mixing of ship plumes and enhances their dispersal. Finally, results show that whereas ship emissions do increase cloud albedo in regions of open cells, even the addition of very large aerosol concentrations cannot transform an open cellular structure to a closed one, for the case considered.

Corresponding author address: Hailong Wang, 325 Broadway, R/CSD2, Boulder, CO 80305. Email: hailong.wang@noaa.gov

Abstract

This is the second of two companion papers on modeling of mesoscale cellular structures and drizzle in marine stratocumulus. In the first, aerosol–cloud–precipitation interactions and dynamical feedbacks were investigated to study the formation and evolution of open and closed cellular structures separately. In this paper, coexisting open and closed cells and how they influence one another are examined in a model domain of 180 × 60 × 1.5 km3. Simulations show that gradients in aerosol at the open–closed-cell boundary cause gradients in precipitation that generate a mesoscale circulation. The circulation promotes precipitation in the polluted closed cells but suppresses it in open cells by transporting water vapor to the closed-cell regime and carrying drier air and aerosol back to the open cells. The strength of this circulation depends on the contrast in precipitation under clean and polluted conditions at the boundary. Ship plumes emitted into clean, precipitating regions, simulated as a special case of a clean–polluted boundary, develop a similar circulation. Drizzle in the ship track is first suppressed by the increase in aerosol particles but later recovers and becomes even stronger because the local circulation enhances liquid water path owing to the convergence of water vapor from the region adjacent to the track. This circulation modifies the transport and mixing of ship plumes and enhances their dispersal. Finally, results show that whereas ship emissions do increase cloud albedo in regions of open cells, even the addition of very large aerosol concentrations cannot transform an open cellular structure to a closed one, for the case considered.

Corresponding author address: Hailong Wang, 325 Broadway, R/CSD2, Boulder, CO 80305. Email: hailong.wang@noaa.gov

1. Introduction

In the first paper of a two-part series (Wang and Feingold 2009, hereafter Part I) aerosol–cloud–precipitation interactions and dynamical feedbacks were investigated to study the formation and evolution of open and closed cellular structures in marine stratocumulus clouds. Earlier modeling work had shown that the initiation of precipitation, followed by the dynamic response to the evaporation of rain drops, promotes and sustains the formation of open cellular structure in cloud fields (Savic-Jovcic and Stevens 2008; Xue et al. 2008). Part I confirmed these results by showing that for the same large-scale environment and initial thermodynamic conditions stratocumulus clouds influenced by polluted air prefer a closed cellular structure, whereas open cellular convection develops in pristine clouds as a result of the formation of precipitation. Part I also showed that without any perturbation from changing ambient conditions and additional aerosol sources, a collection of open cells in a 60 × 60 km2 domain can persist for over 10 h but that the open-cell walls tend to collapse when cloud condensation nuclei (CCN) and liquid water are significantly depleted.

Closed and open cellular structures exhibit distinct differences in shortwave energy reflected to space and are therefore of great interest from the climate change perspective. The fact that aerosol perturbations have the potential to change the organizational structure of cloud fields, as mediated by dynamical feedbacks initiated by precipitation, makes them the subject of intense interest. In this study, we extend our previous work (Part I) by simulating closed and open cellular structures in the same model domain to allow for microphysical and dynamical interactions between the two distinct regions.

As a particular case of a polluted–clean air mass interface, the response of cellular structures to a substantial perturbation of CCN from underlying ship emissions is also examined. Serving as striking examples of aerosol effects on marine stratocumulus clouds, ship tracks as seen in satellite imagery have been studied for decades since they were first reported by Conover (1966) as “anomalous cloud lines.” Perturbations to the ambient environment by sailing ships include CCN enhancement (ship emissions and sea salt particles generated in the ship’s wake), the addition of heat and water vapor, and ship-wake turbulence. These perturbations may or may not produce ship tracks depending on the preexisting boundary layer thermodynamics and cloud microphysics (e.g., Coakley et al. 2000). Transport of CCN from ship effluent to clouds may be facilitated in a well-mixed boundary layer compared to a decoupled one (Liu et al. 2000). All other conditions being equal, a low background CCN number concentration Nc favors the formation of ship tracks (e.g., Conover 1966). Therefore, ship tracks appear to form in certain locations and on certain days but not in others (e.g., Durkee et al. 2000).

There is disagreement in the literature over whether liquid water path (LWP) increases or decreases in ship tracks. An increase in LWP is often invoked to support the “second aerosol indirect effect” (Albrecht 1989) whereby more CCN cause smaller drops, less precipitation, and an increase in cloud liquid water. Conclusions from satellite remote sensing retrievals, in situ measurements, and numerical simulations often disagree with one another. Satellite retrievals indicate reduced liquid water in polluted ship tracks (e.g., Platnick et al. 2000; Coakley and Walsh 2002; Segrin et al. 2007) whereas in situ measurements show either an increase in liquid water content during some individual aircraft transects (e.g., Radke et al. 1989; Ferek et al. 2000) or no statistically significant change on average (e.g., Ackerman et al. 2003). From the observational perspective, changes in LWP could result from a combination of aerosol effects, meteorological effects, and dynamical feedbacks, which are unlikely to be identified separately. Large-eddy simulation (LES) has proven useful to isolate these effects and feedbacks. Ackerman et al. (2003) modeled broken precipitating stratocumulus clouds polluted by ship emissions and found that both cloud fraction and domain-average LWP increase with Nc, but LWP averaged just over overcast pixels (each pixel comprising 256 model columns) decreases. Therefore, they attributed the disagreement with satellite studies to an artificial enhancement of LWP in the surroundings by omitting more partially cloudy pixels than in ship tracks.

LES studies of stratocumulus exposed to different background aerosol concentrations have revealed complex microphysical and dynamical responses that do not fit into simple constructs such as that posed by the second indirect effect (e.g., Feingold et al. 1996; Stevens et al. 1998; Jiang et al. 2002; Wang et al. 2003; Lu and Seinfeld 2005; Xue et al. 2008; Hill et al. 2009). The complexity of this issue is compounded when one allows variation in meteorological conditions. For example, Ackerman et al. (2004) proposed that drying from entrainment of overlying air may be sufficient to offset moistening from suppression of precipitating and, therefore, lower LWP of stratiform clouds, depending on the humidity of the overlying air.

This study extends the scope of these earlier studies by addressing the dynamical implications of idealized clean–polluted air mass boundaries as manifested either in mesoscale gradients in aerosol or as aerosol intrusions emulating idealized ship emissions into a cleaner background. We pay close attention not only to the more obvious microphysical responses (e.g., albedo and precipitation responses) but also to the dynamical feedbacks that they incur. Specific questions that will be addressed are: To what extent does an increase in CCN in open-cell regions alter cloud and precipitation microphysics? Does this substantially change the dynamics of the local circulation, and therefore, the evolution of open cells? What are the albedo, LWP, and precipitation responses to ship plumes in open and closed cells? Can a significant aerosol perturbation to open cells result in a transition to closed cellular structure?

2. Numerical model and experiments

As in Part I, we have performed simulations of marine stratocumulus (Sc) using a high-resolution version of the Advanced Research Weather Research and Forecasting model (ARW; Skamarock et al. 2008) with a high-order monotonic advection scheme (Wang et al. 2009) and a double-moment bulk microphysical scheme (Feingold et al. 1998). The microphysical scheme assumes a lognormal size distribution for CCN, cloud droplets, and rain drops with a prescribed geometric standard deviation of 1.5, 1.2, and 1.2, respectively. The CCN spectrum has a median radius of 0.1 μm. The cutoff radius between cloud and drizzle drops is 25 μm. The mean radius of cloud droplets and rain drops is computed based on predicted mass mixing ratios and number concentrations. Simulations are initialized with thermodynamic conditions and large-scale forcings obtained from the Second Dynamics and Chemistry of Marine Stratocumulus field study (DYCOMS II; Stevens et al. 2003) over the northeast Pacific Ocean as in Part I. Other model settings are also the same as in Part I unless specified otherwise.

Simulations are performed in a 180 × 60 × 1.5 km3 domain for 12 h with a horizontal (vertical) grid spacing of 300 (30) m. The relatively coarse horizontal grid spacing is a balance between the need to simulate the major dynamical features of the marine boundary layer and a desire to simulate mesoscale features in a large domain. Part I demonstrated that this grid size is a reasonable compromise. Radiative cooling is simulated as a simple function of LWP to facilitate comparison with previous studies (e.g., Savic-Jovcic and Stevens 2008; Part I), and shortwave radiation is neglected to keep a nocturnal focus. Nonetheless, the sensitivity of results to different radiation schemes and shortwave radiation is discussed in section 4.

Five numerical experiments are summarized in Table 1. Three control experiments (CONT, CONT-C, and CONT-P) are designed to examine the interaction between clouds that form in the marine boundary layer with different background Nc. As shown in Part I, open cells tend to form in clean environments and closed cells form in relatively polluted ones. The experiments SHIP-C and SHIP-P, which have ship-emitted particles as active CCN, are conducted to examine the dispersion of particles in open- and closed-cell marine boundary layers and how a local abrupt increase in Nc changes the microphysical and dynamical processes and cellular structures of marine stratocumulus. In the control experiments CONT-C and CONT-P, ship-emitted particles are treated as passive tracers to follow the dispersion of ship plumes in an environment without dynamical perturbations induced by aerosol effects and associated feedbacks.

The initial background Nc increases linearly in the x direction of the model domain to produce a gradual change in rain rate and a smooth transition in cloud cellular structure. Nonetheless, the use of cyclic boundary conditions generates a strong contrast in Nc between the two x boundaries at x = 0 and 180 km, where interactions in microphysical and dynamical processes are allowed. This setup allows us to explore the effects of both sharp and weak aerosol gradients.

No replenishing source of CCN is used except for the moving point source of underlying ship emissions, when specified. The ship emission rate of CCN is loosely based on estimates by Radke et al. (1989) of about 2 × 1016 CCN per second for an observed 80 cm−3 increase of drop number concentration (Nd) in ship tracks. This value is approximately an order of magnitude larger than emissions measured during the Monterey Area Ship Track (MAST) experiment (Hobbs et al. 2000); however, those did not produce strong enough perturbations to Nd in our simulations. For expediency, and without implying accurate representation of any particular ship effluent, the CCN emission rate is set to 3.6 × 1015 s−1 and three ships1 are aligned in a row along the y axis, 1.5 km apart from each other. These plumes, and the ship tracks they produce, merge together and will be referred to henceforth as the “ship plume” or “ship track” as opposed to three individual plumes and tracks. Assuming that particles are emitted from the ship stack and uniformly distributed in a 300 × 300 × 30 m3 grid box, the perturbation to Nc in that grid box is about 4000 cm−3 in 3 s (i.e., one model time step). With a moving speed of 10 m s−1, ships traverse through each grid box in 30 s and pass through the domain from the start point (x = 0 and y = 30 km) to the end point (x = 180 and y = 30 km) in 5 h. They pass through the domain once and do not repeat their journey.

In addition to particles, heat and water vapor are also part of ship exhausts. Based on observations made during the MAST experiment, Hobbs et al. (2000) estimated that the heat flux range was 2–22 × 106 W and the moisture flux ranged from 0.5 to 1.5 kg s−1, but the heat and moisture fluxes from ships rarely produced detectable perturbations in temperature and water vapor. In a single time step, such heat and moisture fluxes produce a maximum perturbation of 0.02 K and 1.5 × 10−3 g kg−1 in temperature and water vapor mixing ratio, respectively. Our sensitivity tests show that even the maximum temperature and water vapor perturbations have an insignificant effect on the cloud properties, so heat and moisture perturbations associated with ships are henceforth not discussed.

3. Results

a. Interactions at the open–closed-cell boundary

Figure 1 shows snapshots of cloud albedo, cloud-base rain rate (Rzb), and column-average Nc calculated from the model output at different times for experiment CONT. With increasing x distance one sees a transition from open to closed cellular structure accompanied by a decrease in precipitation as the background Nc increases from the clean toward the more polluted regime. The open and closed cellular structures in clouds look very similar to those seen in the corresponding separate domains of Part I, Savic-Jovcic and Stevens (2008), and satellite imagery (e.g., Garay et al. 2004; Stevens et al. 2005; Wood and Hartmann 2006; Wood et al. 2008). The transition from open cells to closed cells is gradual and smooth. Nonetheless, precipitation and the associated breakup of clouds is observed to move into the previously closed-cell region as background CCN are diluted through mixing, transport, and drop coalescence processes, indicating some degree of interaction between open and closed cells in the middle of the domain. The most distinguishable interactions between open cells and closed cells are, however, at the x = 0,180 km boundary where the initial contrast in Nc (60 versus 300 mg−1) is a maximum. In the top two panels, with the apparent migration of CCN from the polluted regime to the clean regime, clean clouds cease precipitating (e.g., at t = 6 h, x < 15 km) and polluted clouds begin to precipitate (e.g., at t = 6 h, x > 170 km). The suppression of precipitation in clean clouds that become contaminated by pollution is consistent with conventional wisdom. However, clouds near the boundary become brighter on the polluted side (e.g., at t = 6 h, x > 170 km) but less reflective or even invisible on the clean side (e.g., at t = 6 h, x < 15 km). This seemingly contradicts the expected albedo response of the clean (polluted) clouds to the increase (decrease) in aerosol that one would expect from simple aerosol transport and mixing across the clean–polluted boundary. Moreover, in Fig. 1c near the boundary (x < 10 km), Rzb does not correlate with Nc in the same manner as it does in the middle of the domain; precipitation is present in the relatively polluted air at x < 10 km. With very weak mean winds (0.1 m s−1) in the boundary layer, what causes such a rapid migration of CCN? Interactions other than CCN transport by the mean wind are needed to explain this puzzle and the aforementioned apparent contradictions to conventional wisdom.

Vertical cross sections of the wind perturbation vector, water vapor mixing ratio perturbation qυ, and total particle number concentration are shown in Fig. 2 to illustrate dynamical, microphysical, and thermodynamic interactions in the boundary region (160 < x < 180 km; 0 < x < 30 km). A circulation pattern, as depicted by colored arrows in the left panel, fairly distinct from the rest of the domain, is seen in this location. The circulation—which consists of an outflow from open cells to closed cells in the lower boundary layer and a return inflow into clouds, extending from cloud base to cloud top—is driven by precipitation and dynamical feedback in the open-cell region. As discussed in Part I, the strong near-surface outflow is associated with below-cloud drizzle in the downdrafts. The outflow, which is enriched in water vapor (Fig. 2, right panel), penetrates into the closed cells until a counteracting flow is met, at which point the moist air ascends and solid stratocumulus is reinforced. Part of the moisture is associated with the pool of trapped water vapor in the subcloud layer that is decoupled from the cloud layer. Drier air and CCN are transported into the open cells in the upper return flow (Fig. 2, right panel). Perturbation to particle number concentration from the neighboring polluted environment is apparent. Responding to the inflow–outflow circulation pattern associated with the precipitating open-cell walls, the return flow from the closed cells penetrates more actively at the base (horizontal inflow) than at the top (horizontal outflow) of the precipitating clouds. As drier air and CCN are pumped into open-cell walls, precipitation in the walls gradually diminishes and clouds eventually dissipate. This is the mechanism via which the low albedo vertical stripe in Fig. 1 is formed; clouds are cleared out in the boundary region adjacent to open cells but are reinforced at the polluted edge of the boundary. Two factors eventually promote precipitation near the boundary as illustrated by Fig. 2c: (i) clouds become thicker because of the dynamical support and moisture convergence and (ii) CCN on the polluted side are diluted by the lower ambient concentrations. In turn, newly formed precipitation perturbs the already established mesoscale circulation (Fig. 2c, left panel; 0 < x < 15 km).

To give a broader picture of the relationship between precipitation and the mesoscale circulation, the time evolution of the y-direction average u perturbation winds and cloud-base rain rate covering the full extent of the x axis is shown in Fig. 3 for CONT (left panel). In the boundary region (bordered by dotted lines at both left and right), the mesoscale circulation is characterized by near-surface (100 m) negative and in-cloud (450 and 750 m) positive wind perturbations. As discussed before, this mesoscale circulation is driven by the near-surface outflow and supported by the inflow–outflow circulation pattern associated with precipitating open-cell walls. Clearly, the circulation is well correlated with precipitation along the boundary. With the eastward retreat of precipitating open cells and westward penetration of near-surface flow into closed cells, the horizontal scale of the circulation (distance between the two dotted lines in the plots) increases from less than 20 km at t = 3 h to about 70 km at t = 11.5 h at 100- and 450-m levels. At 750 m (near cloud top), the return branch of the circulation meets a counteracting outflow from precipitating open-cell walls, where air converges and descends. This supports the formation of the cloud-free boundary region and the pool of moist air trapped near the surface. After about t = 6 h, a smaller-scale local circulation embedded in the larger one appears on the polluted side of the boundary region (x = 170 km) when drizzle with cloud-base rate comparable to that in the open cells develops. It then propagates eastward and reappears at the left of the domain at approximately 7 h (upper-left corner of left panel plots). It superimposes an outflow in the lower boundary layer relative to its location and an inflow–outflow in-cloud circulation, which resembles the one associated with open-cell walls.

Interestingly, Rzb tends to be higher near the boundary. This is consistent with the observations reported by Comstock et al. (2007) (i.e., that the highest rain rates preferentially occur in the open-cell region near the boundary or transition between open- and closed-cell regions). Although the initial low background Nc contributes to the high rain rate near the boundary in open cells, enhanced LWP by the mesoscale circulation more than compensates for the higher Nc in the closed-cell region and promotes similar precipitation rates to those in the open cells.

In the middle of the domain, the overall trend of precipitation propagating eastward into a progressively more polluted regime is clear. A similar although much weaker circulation is observed near the weak precipitation front, with near-surface outflow moving eastward from the precipitating toward the nonprecipitating region and with weak return flow at 750 m.

Experiment CONT-C (Fig. 3, right panel) provides an interesting comparison with CONT. Here the gradient of Nc at the x = 0,180 km boundary (60 versus 150 mg−1) is much weaker than in CONT. As a consequence, the circulation initiated by the precipitation gradient at the left boundary is less marked. It enhances precipitation at the closed-cell side of the boundary region (i.e., 170 < x < 180 km). This reinforces the conclusion that the contrast in precipitation is responsible for the circulation observed near the boundary and that the greater the precipitation contrast, the stronger the circulation.

In the middle of the CONT-C domain (30 < x < 150 km), precipitation maxima are lined up along the time axis and tilted both eastward and westward. This is a result of the propagation of open-cell walls in all directions and the production of new cells during propagation, as discussed in Part I.

b. Impact of ship emissions on cellular structures

Figure 4 shows changes in cloud albedo, precipitation, and open cellular structure resulting from CCN perturbations from ships (experiment SHIP-C where ship-emitted particles act as CCN). Note that the shading of the ship plume in the right panel is derived from the ship-emitted particle concentration Nsc, recorded separately as a passive scalar. Column-average Nsc with 10 mg−1 as a lower threshold is plotted. A ship track emerging from the ship and spreading laterally with increasing distance from the source is clearly visible in the cloud albedo field. The response of cloud-base precipitation to the ship plume is location dependent. First, focusing on the ship track, although the highest CCN perturbation is close to the plume head, precipitation further behind the plume head is suppressed even more because time is needed for the emitted CCN to enter clouds and perturb the microphysical processes. Interestingly, new and sometimes stronger precipitation develops tens of kilometers behind the plume head where clouds are still under the influence of high Nsc. Second, clouds close to the lateral boundaries of the ship plume become less reflective and precipitation here is highly reduced. It will be shown that this cannot be simply explained by aerosol suppression of precipitation.

Figure 5 shows the impact of the same underlying ship emissions on closed cells in the relatively polluted regime, where ship plumes are much more spatially confined than in open cells. The ship track is barely seen in the cloud albedo field because the more polluted clouds are less susceptible to the CCN perturbation and the small change in cloud albedo is masked by the highly reflective neighbors. More importantly, the polluted clouds are nonprecipitating, so no interaction among the CCN perturbation, precipitation, and consequent feedbacks are involved; this is elaborated upon in the following.

Stevens et al. (2005) noted the consistency between the tendency for open cells to be associated with precipitation and the observation that open-cell regions are more favorable for the formation of ship tracks. Our simulation results support this notion. As shown in Fig. 4, ship-emitted CCN influence the formation and evolution of precipitation and open cells. However, the underlying mechanism is not as straightforward as the conventional wisdom associated with the second indirect effect might suggest. Once ship-emitted CCN are mixed into the overlying clouds and start to suppress in-cloud drizzle, the original open cellular structures are perturbed. Subsequently, near-surface outflows driven by precipitation in the unaffected open cells on both lateral sides of the ship track meet and converge in the ship-track zone, forming a local circulation that has a return flow in the cloud layer similar to the one near the open- and closed-cell boundary (Figs. 1 –3). Figure 6 (left panel) shows the time evolution of the mesoscale circulation characterized by near-surface (100 m) inbound and in-cloud (750 m) outbound y-wind component υ relative to ship-track center. Cloud-average Nsc contours are superimposed on the plots of SHIP-C to locate areas in the y direction affected by ship-emitted CCN. The maximum concentrations are along the central ship trajectory at y = 30 km. With the spread of the ship plume and the retreat of precipitating clouds on the lateral sides, the horizontal scale of the circulation increases from less than 10 km at t = 2 h to about 50 km at the end of the simulation. It is noted that the dispersion of the ship plume is not symmetrical about the y axis because of a weak mean y wind of −0.1 m s−1.

Corresponding results from the control case CONT-C are plotted in the right panel of Fig. 6 for comparison. In this case there is no distinct circulation pattern associated with these passive particles, indicating that the circulation observed in SHIP-C is caused by ship-emitted active CCN. Also plotted in Fig. 6 are Rzb and LWP, the distributions of which also exhibit substantial changes due to ship emissions. Note that because the model output is averaged over the x axis in this display, earlier periods of time represent less influence by the ship track because it takes 5 h for the ship to traverse the 180 km. After model spinup, but before the formation of the circulation (2 < t < 3 h), LWP increases in the ship plume because of rain suppression by CCN enhancement. This is not easily seen in Fig. 6 because of the averaging over the x direction but is clearly seen in Fig. 4. After t = 3 h, LWP and rain rate are both enhanced along the ship track but reduced near the lateral boundaries because of the local circulation. As witnessed near the open–closed-cell boundary (Fig. 3), the circulation pumps moist air from neighboring open cells into the ship plume and dilutes the CCN in the ship plume. As a result, LWP further increases and new precipitation develops along the convergence zone in the ship track, whereas precipitation near the lateral boundaries is reduced owing to a loss of water vapor and a lack of dynamical support. After t = 8 h, some smaller-scale circulations exhibiting a variety of patterns can be seen embedded in the large one. For example, at t = 9 h and y = 25 km a local maximum in precipitation develops inside the ship plume. However with time, the gradient in Nsc becomes weaker, precipitation generates open cells within the plume, and the organized circulation described above weakens and loses its influence on moisture convergence and cloud formation.

c. Transport and dispersion of ship emissions

Given the near-zero mean winds, ship emissions are transported and mixed by organized large eddies in the turbulent flow. In a well-mixed stratocumulus-topped boundary layer, the ship plume reaches the capping inversion within about 15 min (e.g., an air parcel rises 0.9 km in 15 min in an updraft of 1 m s−1), so that vertical transport is well constrained by boundary layer depth and updraft velocity. Horizontally, local circulations associated with open and closed cells have an important impact on the transport and dispersion of ship emissions. In addition ship-emitted particles themselves can induce mesoscale circulations in open-cell regions, as discussed earlier, and therefore feed back on the dispersion process. To evaluate the extent to which this is true, ship-emitted CCN are tracked as passive tracers (separately and distinct from active CCN) so that we can use their distribution to characterize the dispersion of ship plumes with active CCN (experiments SHIP-C and SHIP-P). As shown in Fig. 6, on average, passive ship plumes (outlined by contours) are diluted more slowly in CONT-C than in SHIP-C where ship-emitted CCN affect cloud microphysics and change the local circulation. The mean Nsc (averaged over all grids in which Nsc > 1 mg−1 after 2-h spinup time) in SHIP-C is 12% smaller than in CONT-C. The ship plume is much more confined in the nonprecipitating case SHIP-P even though the same influx of CCN is applied (cf. Figs. 4 and 5). In Fig. 7, instantaneous vertical distributions of the ship plume (Nsc) are compared for the four experiments. Once the aforementioned local circulation has formed in SHIP-C (e.g., at t = 3 h), it lifts ship emissions into the cloud layer and then spreads them in the horizontal outflows. Maximum concentrations are seen in the upper boundary layer instead of near the surface. In the other three experiments, ship plumes are well mixed vertically, but the horizontal mixing is much less effective than in SHIP-C. Although there are also local circulations associated with precipitating open-cell walls, the horizontal scale is very small. At a later stage (t = 9 h), the horizontal mixing slows down in SHIP-C (see Fig. 6) because the circulation has been disturbed by embedded precipitation. Nonetheless, compared to others, the horizontal dispersion in the y direction is almost doubled. With the more rapid dilution of ship plumes and cloud scavenging of CCN, ship-track features are less persistent in the SHIP-C case.

4. Discussion

a. Impact of radiation on the observed circulations

The parameterized longwave radiation used in the simulations, which is dependent on the vertical distribution of liquid water content, tends to underestimate radiative cooling of optically thin clouds and exaggerate the contrast in radiation between clear and cloudy regions. Does this radiative effect influence the circulation observed in the boundary region between open and closed cells? To answer this question, three sensitivity tests were conducted. They have the same settings as the control experiment (CONT) except that experiment RRTM uses a more accurate multiband longwave radiation scheme (Mlawer et al. 1997), FIXR uses a fixed cooling–heating profile (calculated using the initial cloud water profile) in all model columns, and in experiment NOR no radiation is considered. Comparison of results from experiments RRTM, FIXR, NOR, and CONT suggests that the parameterized longwave radiation performs adequately for the present study and that the formation of the observed circulations is not driven by the contrast in radiation between clear and cloudy regions. Figure 8 shows an xz cross section of x-wind perturbation u, rain rate Rr, and LWP in a boundary-centric view. The plots are produced using a conditional-composite-averaging procedure loosely following Savic-Jovcic and Stevens (2008). The leftmost grid of precipitating cells (defined as Rr > 2 mm day−1 at z = 100 m) near the open–closed-cell boundary is aligned at x = 0, and other x grids on each y slice are shifted accordingly. The illustrated quantities are averaged over all selected y slices at four independent times (t = 6, 6.5, 7, and 7.5 h).

Overall, the strongest precipitating cells along the open–closed-cell boundary have the largest LWP. Both the local circulation associated with precipitating cells and the complete circulation that connects open and closed cells are consistent with those discussed in section 3. There are two pieces of evidence showing that the circulation is not driven by the contrast in radiation between clear and cloudy regions or between thin and dense clouds but rather by the contrast in precipitation. In experiment RRTM, the contrast in radiation is likely weaker while the circulations tend to be stronger than in CONT. However, this is consistent with the stronger precipitation in open cells in RRTM. The other piece of evidence is that without any radiation contrast in FIXR and with no radiation at all in NOR, the circulations still persist. Moreover, with weaker precipitation in NOR the circulation is significantly less intense. On the other hand, the results show that longwave radiative cooling is critical to the maintenance of clouds and therefore to the persistent precipitation that drives the circulations. In this sense, the circulations are indirectly closely related to radiation.

Results of the no-radiation experiment (NOR) indicate how the cancelling of longwave cooling by solar radiation can impact open and closed cells and their interaction. Another sensitivity experiment, including solar radiation, in which the sun rises at about t = 6 h was conducted to compare with the nocturnal simulation RRTM. Precipitation is highly reduced and almost completely shut off by local noon because clouds are dissipated by solar radiation (figures not shown). With the drastic decrease in rain rate the already established circulations in the open–closed-cell boundary are weakened and then disappear. More comprehensive studies are needed to describe the evolution of open and closed cells and their interaction over a diurnal cycle.

b. Conceptual diagrams

The conceptual diagram in Fig. 9a summarizes the discussion above and gives a schematic view of how the local mesoscale circulation near the open–closed-cell boundary is driven by precipitation and how it redistributes moisture and CCN. Outflow associated with the precipitating open cells transports moisture toward originally nonprecipitating closed cells. This results in local enhancement in LWP and subsequent initiation of precipitation in the closed cells. The upper branches of the circulation transport drier air and possibly CCN, depending on the concentration of unactivated CCN in and below clouds in the relatively polluted regime, to neighboring open cells. This tends to dissipate clouds and diminish precipitation in the nearest open-cell wall. The retreat of the open cells is faster than the propagation of the closed cell so that the boundary region broadens. Note that the precipitation generated on the closed-cell side of the boundary develops at a later stage.

Sharon et al. (2006) also presented a schematic of a possible mechanism via which a mesoscale circulation process may be involved in generating strong drizzle near the stratocumulus deck. The circulation depicted by Sharon et al. (2006) is very close to what we simulate here. The main difference is that in their schematic the local CCN near the closed-cell edge are significantly reduced. Observations emerging from the Variability of the American Monsoon System (VAMOS) Ocean–Cloud–Atmosphere–Land Study—Regional Experiment (VOCALS-Rex) field experiment also suggest that the inflow into open-cell regions is strongly depleted in CCN (R. Wood 2009, personal communication). In our simulations, Nc is not reduced to the degree required to produce strong precipitation and breakup of the solid deck. The differences may be simply a question of timing and location of observations or our idealized model simulation conditions (e.g., the initial aerosol concentrations)—that is, a difference in degree, as opposed to a conceptual difference. The datasets from VOCALS-REx will provide case studies to resolve possible discrepancies and to improve the representation of aerosol–CCN and cloud scavenging processes in our microphysical scheme.

VanZanten and Stevens (2005) and Savic-Jovcic and Stevens (2008) also conceptualized circulations in open cells and the neighboring stratiform region. Near the surface we see the same circulation branches and moister surface layer as theirs near open-cell walls but different ones near the closed-cell boundary. These differences may again be a question of particular conditions or the time at which the evolving system is sampled.

This local mesoscale circulation can either dissipate open cells or break up closed cells, depending on the initial background conditions in closed cells. The closed cells formed in a polluted region may persist for a long time, as in the schematic. Precipitation may not be strong enough to detach the deck edge and disrupt the circulation that keeps pumping moisture from the neighboring open cells before they dissipate. If processes driven by the circulation are able to lower Nd and enhance liquid water enough so that precipitation can drive a new local circulation, the precipitating part may detach from the deck. For instance, inside the domain (90 < x < 120 km) of experiment CONT (Fig. 1) the breakup of clouds is observed to move with time into the previously closed-cell region as background CCN are diluted through mixing and drop collection.

A schematic of a ship track crossing a precipitating open-cell region (Fig. 9b) shows a similar local circulation driven by precipitation in regions adjacent to the track. The difference is that in this case the driving force is from both sides of the track and therefore the stronger moisture convergence and CCN divergence facilitate a faster formation of precipitation in the track. We note that the strength of the local circulation will depend on the aerosol concentration in the ship track and the contrast with the background, so that this dynamical feedback is expected to occur to varying degrees in real cases.

c. Do clouds contaminated by ship plumes lose or gain liquid water?

Regardless of the source of the discrepancy among satellite retrievals, in situ measurements, and LES modeling of LWP in ship tracks (see the brief review in the introduction section), the dynamical interaction between clouds in a ship track and surroundings should be considered in model simulations. As shown in the previous section, the local mesoscale circulation resulting from an embedded Nc perturbation in precipitating open cells can have a profound influence on cloud properties. Figure 10 shows the time evolution of cloud-average Nsc and changes in cloud properties relative to the corresponding control experiment. Quantities in the ship-track zone (i.e., defined as column-average Nsc > 20 mg−1) and the unaffected zone are calculated separately in both clean (SHIP-C) and polluted (SHIP-P) cases. The corresponding control experiment values are then subtracted from those in the ship-track experiments. Averages over the last 10 h of simulations are summarized in Table 2. With a perturbation of between 10 and 70 mg−1 to Nd, the average LWP in the ship track penetrating open cells increases by 6.8 g m−2 or 11%. This is partly due to the suppression of drizzle by ship emissions (0.36 mm day−1 or 19% reduction in Rzb) but more to the local circulation that pumps moisture from the surrounding precipitating zone to the ship-track zone. As a result, the neighboring regions lose liquid water, leading to a decrease in mean LWP of 5%; this value is even higher if just averaged over the affected neighboring regions as shown in Fig. 6. The abrupt increase in LWP in the ship track and the simultaneous decrease in LWP and rain rate in the neighboring regions (Fig. 10), which starts at about t = 5 h when the ships leave the domain, indicate the dominant effect of the circulation on redistribution of moisture. Although Nd increases in ship tracks, enhancement in LWP can alter the sign of change in rain rate, which is more susceptible to a change in LWP than in Nd, as discussed in Part I. Therefore, as LWP increases, rain rate recovers and after a few hours even exceeds that in the control experiment by up to 0.5 mm day−1. This contradicts the line of thinking associated with the second aerosol indirect effect. Nonetheless, the overall domain-average rain rate is reduced by 6%, and LWP decreases slightly too. Cloud albedo in the ship track is enhanced by 45% as a result of increases in both Nd and LWP.

In the nonprecipitating polluted region (Fig. 10 and Table 2, right column), there are much smaller changes in LWP and cloud albedo than in the clean case although the ratio ΔNd/Nsc happens to be the same. After the 2-h spinup time, there is some evidence of a decreasing LWP in the ship track, perhaps due to the fact that smaller droplets evaporate more readily, but it quickly recovers. The average difference from the control experiment is only about 0.5%. Cloud albedo in the ship track is enhanced by about 4.3% and is much less susceptible to the change in Nd than in the clean case (ΔαcNd = 0.0006 versus 0.0026). For this reason, the ship track is barely visible in the cloud albedo field shown in Fig. 5.

Interestingly, the different response of LWP to ship plumes in open and closed cells is somewhat consistent with satellite observations of the impact of weak volcanic eruptions on marine clouds reported in a recent study by Gassó (2008). That study found that a volcanic plume reduces LWP in a solid stratocumulus deck but enhances LWP in broken, open-cell-like clouds. Satellite imagery presented in that study also shows a similar transverse variability in clouds along the plume in the broken-cloud region to that simulated for ship tracks in open cells. Moreover, the decrease in LWP near the lateral boundaries is also visible in the satellite imagery presented in the volcano plume study, suggestive, perhaps, of a similar circulation to the one modeled here.

d. Can an influx of CCN close open cells?

Satellite imagery sometimes shows open cellular structures crisscrossed by ship tracks hinting at the potential for cells to close once the atmosphere is recharged with aerosol (Rosenfeld et al. 2006). The absence of a replenishing source of aerosol via new particle formation in these simulations also begs the question of whether an influx of aerosol can close open cells. As shown in the cloud albedo field (Figs. 1, 4, and 5), a closed cellular structure is preferred when the background Nc is high enough to prevent the formation of precipitation, indicating that without considering changes in meteorology and large-scale forcing, high ambient Nc can maintain an unbroken stratocumulus deck. However, this is not the case if the initial ambient Nc is already low enough to allow for precipitation. Once the open cellular structure is established, these simulations suggest that it is not possible to close it locally simply through an influx of CCN. The interactions in the open–closed-cell boundary region and in the ship track are two examples that illustrate this point. Although the return flow may transport CCN in substantial quantities to the open cells, clouds become even thinner because of a loss of water vapor and lack of dynamical support (Figs. 1 and 2). Open cells do not close but instead dissipate. Similarly, a fairly strong Nc perturbation from ship emissions temporarily shuts off precipitation and closes open cells just along the center of ship plume but leaves the neighboring lateral regions more open (Fig. 4). The mechanism for open cells to close is clearly not just a function of CCN availability but must be supported by a circulation that can generate liquid water. After the formation of new precipitation, clouds in the ship track open up again. Figure 11a shows that the fractional coverage of clouds in the track is larger than it is in the control experiment, but it still decreases with time, suggesting that the breakup process is not halted but just temporarily delayed. The difference in cloud fraction (and albedo) due to the ship track is not insignificant (on the order of 0.2 in cloud fraction and 0.1 in albedo) and may have important ramifications, particularly in busy shipping regions covered by stratocumulus. Note, however, that the average cloud fraction is much smaller than the original closed-cell case (67% versus 98%). For the case studied here, as long as precipitation is not entirely shut off, the open cells continue to reform.

What if precipitation in the entire domain is abruptly and permanently shut off? Another sensitivity experiment has been performed in which open cells formed in a clean environment (Nc = 60 mg−1) are abruptly exposed to an influx of Nc = 300 mg−1 throughout the domain at t = 6 h. The time evolution of cloud fraction for this experiment is also shown in Fig. 11. Although precipitation ceases within 2 h and cloud fraction increases from 40% to 80%, at the same time the average LWP drops from about 70 to 45 g m−2. The increased cloud cover is in the form of thin anvil cloud adjacent to the previous open-cell walls. The thin clouds are eroded by evaporation soon after their formation and therefore a high cloud fraction cannot be sustained. Thus, even a dramatic aerosol perturbation of this kind does not transform the open-cell structure to a closed one.

5. Conclusions

In this second paper of a two-part series, open-cell and closed-cell marine stratocumulus clouds are simulated in the same model domain by applying a spatially variable initial background CCN number concentration (Nc). The interactions between open and closed cells and how they impact thermodynamics and cloud microphysics in the boundary region are examined. Simulation results show that a mesoscale circulation driven by the contrast in precipitation (drizzle versus weak or no drizzle) near the open–closed-cell boundary transports water vapor to the closed cells and returns drier air (and possibly CCN) back to open cells. The strength of this circulation is commensurate with the precipitation gradient. As a result, originally nonprecipitating closed cells start to precipitate and migrate toward the open cells. Open-cell walls in the boundary region eventually dissipate, leaving a nearly cloud-free gap behind. Precipitation near the gap tends to be the strongest because of the redistribution of water vapor. These results are in agreement with observations described by Comstock et al. (2007). The conceptual cartoon derived from these simulations (Fig. 9a) is in broad agreement with Sharon et al. (2006), Savic-Jovcic and Stevens (2008), and VOCALS-REx observations.

Ship tracks are simulated because they too represent a regularly occurring manifestation of aerosol gradients in stratocumulus clouds. When a ship plume perturbs precipitating open cells, drizzle is first suppressed in the ship track by the increase in Nc but later recovers because of enhancement in cloud liquid water by a mesoscale circulation driven by precipitation in neighboring open cells, similar to that described above. The liquid water path is enhanced on average by 11% in the ship track but does not change significantly over the entire domain. The cloud-base rain rate in the track is first significantly suppressed but later recovers in response to the increase in LWP. Consequently, open cellular structure resumes in the track zone and the ship-track features erode with time. Albedo responses for the assumed ship emissions are 45% in the track and 10% over the entire domain.

The circulation induced by ship emissions also impacts the transport and dispersion of the ship plume itself. For ship plumes penetrating drizzling regions, the convergence in the middle of the ship plume lifts particles into the cloud layer, whereas the outflow distributes them horizontally. Therefore, these ship plumes are more effectively dispersed than a passive tracer would be.

As expected, ship plumes entering nonprecipitating closed cells are barely visible in the cloud albedo field, although cloud albedo in the ship track is enhanced by up to 0.05 (10%). Very little change in mean LWP is observed in the track zone. These tracks remain more confined because they do not benefit from the same aerosol and precipitation gradient and associated mesoscale circulation as in open cells.

Although a stratocumulus deck prefers a closed cellular structure in sufficiently polluted environments, simulation results suggest that once the open cellular structure has formed it cannot be transformed to closed cellular structure by simply adding more aerosol particles. Even if precipitation in the open-cell region is entirely shut off by a substantial CCN perturbation, the clouds remain broken. As the first response to the domain-wide CCN perturbation, cloud fraction increases rapidly but LWP decreases. The increased cloud cover is in the form of anvil clouds formed by detrainment from previous open-cell walls. Without dynamical support the thin clouds evaporate soon after formation. These results suggest that the closing of open cells requires support from meteorological changes.

As a final note, the focus of this study is to improve our understanding of the formation of open cells and their interactions with closed cells and CCN perturbations through a series of simulations in which properties of background–ship-emitted CCN are simplified and idealized. Future work will include more realistic and detailed aerosol nucleation processes and a source–sink budget, as well as a diurnal cycle of radiation, to evaluate the model against observations in field campaigns such as VOCALS-REx.

Acknowledgments

This research was supported by NOAA’s Climate Goal. The authors thank Bjorn Stevens for insightful discussions and the suggestion to incorporate ship tracks. The constructive reviews by Bjorn Stevens and Robert Wood helped improve the manuscript. We also thank the NOAA ESRL High Performance Computing Systems team for computational and technical support.

REFERENCES

  • Ackerman, A. S., O. B. Toon, D. E. Stevens, and J. A. Coakley Jr., 2003: Enhancement of cloud cover and suppression of nocturnal drizzle in stratocumulus polluted by haze. Geophys. Res. Lett., 30 , 1381. doi:10.1029/2002GL016634.

    • Search Google Scholar
    • Export Citation
  • Ackerman, A. S., M. P. Kirkpatrick, D. E. Stevens, and O. B. Toon, 2004: The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature, 432 , 10141017.

    • Search Google Scholar
    • Export Citation
  • Albrecht, B., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245 , 12271230.

  • Coakley Jr., J. A., and C. D. Walsh, 2002: Limits to the aerosol indirect radiative effect derived from observations of ship tracks. J. Atmos. Sci., 59 , 668680.

    • Search Google Scholar
    • Export Citation
  • Coakley Jr., J. A., and Coauthors, 2000: The appearance and disappearance of ship tracks on large spatial scales. J. Atmos. Sci., 57 , 27652778.

    • Search Google Scholar
    • Export Citation
  • Comstock, K. K., S. E. Yuter, R. Wood, and C. S. Bretherton, 2007: The three-dimensional structure and kinematics of drizzling stratocumulus. Mon. Wea. Rev., 135 , 37673784.

    • Search Google Scholar
    • Export Citation
  • Conover, J. H., 1966: Anomalous cloud lines. J. Atmos. Sci., 23 , 778785.

  • Durkee, P. A., K. J. Noone, and R. T. Bluth, 2000: The Monterey Area Ship Track experiment. J. Atmos. Sci., 57 , 25232541.

  • Feingold, G., B. Stevens, W. R. Cotton, and A. S. Frisch, 1996: The relationship between drop in-cloud residence time and drizzle production in numerically simulated stratocumulus clouds. J. Atmos. Sci., 53 , 11081122.

    • 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
  • Ferek, R. J., and Coauthors, 2000: Drizzle suppression in ship tracks. J. Atmos. Sci., 57 , 27072728.

  • Garay, M. J., R. Davies, C. Averill, and J. A. Westphal, 2004: Actinoform clouds: Overlooked examples of cloud self-organization at the mesoscale. Bull. Amer. Meteor. Soc., 85 , 15851594.

    • Search Google Scholar
    • Export Citation
  • Gassó, S., 2008: Satellite observations of the impact of weak volcanic activity on marine clouds. J. Geophys. Res., 113 , D14S19. doi:10.1029/2007JD009106.

    • Search Google Scholar
    • Export Citation
  • Hill, A., G. Feingold, and H. Jiang, 2009: The influence of entrainment and mixing assumption on aerosol–cloud interactions in marine stratocumulus. J. Atmos. Sci., 66 , 14501464.

    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., and Coauthors, 2000: Emissions from ships with respect to their effects on clouds. J. Atmos. Sci., 57 , 25702590.

  • Jiang, H., G. Feingold, and W. R. Cotton, 2002: Simulations of aerosol-cloud-dynamical feedbacks resulting from entrainment of aerosol into the marine boundary layer during the Atlantic Stratocumulus Transition Experiment. J. Geophys. Res., 107 , 4813. doi:10.1029/2001JD001502.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., Y. L. Kogan, D. K. Lilly, D. W. Johnson, G. E. Innis, P. A. Durkee, and K. E. Nielsen, 2000: Modeling of ship effluent transport and its sensitivity to boundary layer structure. J. Atmos. Sci., 57 , 27792791.

    • Search Google Scholar
    • Export Citation
  • Lu, M. L., and J. H. Seinfeld, 2005: Study of the aerosol indirect effect by large-eddy simulation of marine stratocumulus. J. Atmos. Sci., 62 , 39093932.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 , (D14). 1666316682.

    • Search Google Scholar
    • Export Citation
  • Platnick, S., and Coauthors, 2000: The role of background cloud microphysics in the radiative formation of ship tracks. J. Atmos. Sci., 57 , 26072624.

    • Search Google Scholar
    • Export Citation
  • Radke, L. F., J. A. Coakley Jr., and M. D. King, 1989: Direct and remote sensing observations of the effects of ships on clouds. Science, 246 , 11461149.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., Y. J. Kaufman, and I. Koren, 2006: Switching cloud cover and dynamical regimes from open to closed Benard cells in response to the suppression of precipitation by aerosols. Atmos. Chem. Phys., 6 , 25032511.

    • 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
  • Segrin, M. S., J. A. Coakley, and W. R. Tahnk, 2007: MODIS observations of ship tracks in summertime stratus off the west coast of the United States. J. Atmos. Sci., 64 , 43304345.

    • Search Google Scholar
    • Export Citation
  • Sharon, T. M., B. A. Albrecht, H. H. Jonsson, P. Minnis, M. M. Khaiyer, T. M. van Reken, J. Seinfeld, and R. Flagan, 2006: Aerosol and cloud microphysical characteristics of rifts and gradients in maritime stratocumulus clouds. J. Atmos. Sci., 63 , 983997.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp.

  • Stevens, B., W. R. Cotton, G. Feingold, and C-H. 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., G. Vali, K. Comstock, R. Wood, M. C. vanZanten, P. H. Austin, C. S. Bretherton, and D. H. Lenschow, 2005: Pockets of open cells and drizzle in marine stratocumulus. Bull. Amer. Meteor. Soc., 86 , 5157.

    • Search Google Scholar
    • Export Citation
  • vanZanten, M. C., and B. Stevens, 2005: Observations of the structure of heavily precipitating marine stratocumulus. J. Atmos. Sci., 62 , 43274342.

    • 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 , 32373256.

    • Search Google Scholar
    • Export Citation
  • Wang, H., W. C. Skamarock, and G. Feingold, 2009: Evaluation of scalar advection schemes in the Advanced Research WRF model using large-eddy simulations of aerosol–cloud interactions. Mon. Wea. Rev., 137 , 25472558.

    • Search Google Scholar
    • Export Citation
  • Wang, S., Q. Wang, and G. Feingold, 2003: Turbulence, condensation, and liquid water transport in numerically simulated nonprecipitating stratocumulus clouds. J. Atmos. Sci., 60 , 262278.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and D. L. Hartmann, 2006: Spatial variability of liquid water path in marine low cloud: The importance of mesoscale cellular convection. J. Climate, 19 , 17481764.

    • Search Google Scholar
    • Export Citation
  • Wood, R., K. K. Comstock, C. S. Bretherton, C. Cornish, J. Tomlinson, D. R. Collins, and C. Fairall, 2008: Open cellular structure in marine stratocumulus sheets. J. Geophys. Res., 113 , D12207. doi:10.1029/2007JD009371.

    • 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

Fig. 1.
Fig. 1.

Snapshots of (left) cloud albedo field and (right) column-average CCN number concentration Nc at (a)–(c) t = 3, 6, and 9 h from experiment CONT with contours of cloud-base rain rate Rzb of 1, 10, and 20 mm day−1 superimposed.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 2.
Fig. 2.

The xz cross sections (at y = 20 km) from experiment CONT at (a)–(c) t = 3, 6, and 9 h. (left) Gray shaded areas denote clouds (0.01 g kg−1) and contours outline drizzle (0.01 mm day−1); arrows qualitatively represent wind perturbations (uw) with red for westerly (u > 0) and blue for easterly (u < 0). (right) Shaded colors indicate total particle number concentration (Nc + Nd), and contours water vapor mixing ratio perturbations (qυ; positive by solid lines and negative by dotted lines); perturbations are relative to the horizontal slab average at each level. Note that only one-third of the domain in the x direction is shown for clarity with the 160 < x <180-km portion of the domain attached (left) to depict the entire circulation on the cross sections.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 3.
Fig. 3.

Time evolution of x-wind perturbation (u; top color bar) at (top to next-to-bottom) three height levels (100, 450, and 750 m) and (bottom) cloud-base rain rate (Rzb; bottom color bar) for experiments (left) CONT and (right) CONT-C. All quantities are averaged over the y axis. Dotted lines mark the boundary between the circulation-affected region and open (closed) cells to its right (left). For clarity, arrows are drawn in the boundary region to indicate wind direction.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 4.
Fig. 4.

Snapshots of (left) cloud albedo field and (right) column-average ship-emitted CCN number concentration Nsc at (a)–(c) t = 3, 6, and 9 h from experiment SHIP-C with contours of cloud-base rain rate Rzb of 1, 10, and 20 mm day−1 superimposed. The arrow in the upper right plot (on the x axis) points to the x coordinate of the plume head at t = 3 h.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 5.
Fig. 5.

As in Fig. 4, but from experiment SHIP-P; note that almost no precipitation with Rzb > 1 mm day−1 is observed at cloud base.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 6.
Fig. 6.

(top two rows) Time evolution of y-wind perturbation (υ) at two height levels (100 and 750 m, respectively), (third row) LWP, and (bottom row) cloud-base rain rate (Rzb), with cloud-average ship-emitted CCN number concentrations (labeled contours) superimposed. All quantities are averaged over the x axis.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 7.
Fig. 7.

(a)–(d) The yz cross-sections (at x = 20 km) of ship-emitted CCN concentration Nsc at t = (left) 3 and (right) 9 h for four experiments denoted at the upper left corner of each row. The yz wind perturbation (υw) vectors are superimposed to qualitatively indicate organized turbulent flow or circulation.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 8.
Fig. 8.

Conditional composites of x-wind perturbation u (dotted contours for −0.1, −0.5 and −1.0 m s−1 and solid contours for 0.1, 0.5, and 1.0 m s−1), rain rate Rr (shaded colors), and LWP (thick red lines; vertical axes on the right): (a)–(d) CONT, RRTM, FIXR, and NOR experiments.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 9.
Fig. 9.

Conceptual diagrams illustrating the mesoscale circulation near the (a) open–closed-cell boundary and (b) ship track in precipitating open cells and its effect on redistributing CCN and moisture. In (a), the degree of CCN transport toward the open cells depends on the concentration of unactivated aerosol on the closed-cell side.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 10.
Fig. 10.

Cloud-average ship-emitted CCN number concentration Nsc and changes in (top to bottom) drop number concentration Nd, LWP, cloud albedo αc, and cloud-base rain rate Rzb as a function of time for (a) clean and (b) polluted cases. The change is relative to each quantity in the corresponding control experiment. Solid lines are for averages in the ship-track zone (Nsc > 20 mg−1), dotted lines are for averages in the non-ship-track zone, and dashed lines are for the whole domain.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Fig. 11.
Fig. 11.

Cloud fraction in the ship track (solid lines) and the entire domain of the corresponding control experiment (dashed lines) as a function of time for the (a) clean (Nc = 60–150 mg−1) and (b) polluted (Nc = 210–300 mg−1) cases. The solid and dashed lines are overlaid in (b). The dotted line in both panels is from the sensitivity test in which background Nc changes abruptly from an initial value of 60 to 300 mg−1 at t = 6 h.

Citation: Journal of the Atmospheric Sciences 66, 11; 10.1175/2009JAS3120.1

Table 1.

Summary of numerical experiments; note that the unit of mg−1 for CCN number concentration Nc is equivalent to a unit of cm−3 when the air density is 1 kg m−3.

Table 1.
Table 2.

Cloud-average ship-emitted CCN number concentration (Nsc) and change in cloud properties in ship track (Nsc > 20 mg−1) averaged over the last 10 h of simulations. Calculations are based on results in Fig. 10.

Table 2.

1

During the dispersion of the ship plume, CCN particles may be generated as a result of gas-to-particle-conversion or by cloud processing. These processes require a certain amount of time to produce CCN. Hence, it is unrealistic to instantaneously emit a number of CCN that is an order of magnitude larger than measured into a single grid box. The use of three ship sources, spread over a larger region, attempts to account for these processes and to close the discrepancy between measured and theoretically derived CCN source strengths.

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