Journal of the Meteorological Society of Japan, Vol. 91, No. 6, pp. 817̶833, 2013
DOI:10.2151/jmsj.2013-607

817

Aerosol Effects on Cumulus Congestus Population over the Tropical Pacific:
A Cloud-Resolving Modeling Study
Xiaowen LIGESTAR Program, Morgan State University, Maryland, USA
Code 612, NASA Goddard Space Flight Center, Maryland, USA

Wei-Kuo TAO
Code 612, NASA Goddard Space Flight Center, Maryland, USA

Hirohiko MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan

Guojun GU
ESSIC, University of Maryland, College Park, Maryland, USAandXiping ZENGGESTAR Program, Morgan State University, Maryland, USA
Code 612, NASA Goddard Space Flight Center, Maryland, USA(Manuscript received 18 September 2012, in final form 6 September 2013)

Abstract
This study examines the significance of aerosol serving as cloud condensation nuclei (CCN) in modulating strengthsof tropical maritime convection. Through a Tropical Ocean Global Atmosphere Couple Ocean̶Atmosphere Response
Experiment (TOGA COARE) case study using a cloud-resolving model (the Goddard Cumulus Ensemble Model witha horizontal mesh interval of 750 m) and a detailed spectral bin microphysical scheme, it is found that low aerosol
concentration acts to reduce convection strengths. Over the tropical western Pacific where low-level water vapor isabundant and a ubiquitous weak stable level exists near 0°
C, the low background maritime aerosol concentration isconducive for forming cumulus congestus. Sensitivity tests show that the main mechanism of convection damping in aclean maritime environment is through reduced condensational growth, although the freezing of supercooled water,
cloud top evaporation, and rain evaporation also contribute to the simulated effects. Considering the importance ofcongestus in tropical dynamics and the Madden̶Julian oscillation (MJO) lifecycle, we further propose a hypothesisthat aerosol̶cloud̶precipitation interactions in an ultraclean marine environment may serve as a damping mechanism
for tropical convection.
Keywords

aerosol; CCN; cumulus congestus

Corresponding author: Xiaowen Li, Code 612, NASA
Goddard Space Flight Center, Greenbelt, Maryland 20771,
USA
E-mail: Xiaowen.Li@nasa.gov©2013, Meteorological Society of Japan

1.

Introduction

Aerosols in the atmosphere that serve as cloud
condensation nuclei (CCN) are crucial for cloud and818

Journal of the Meteorological Society of Japanrain formation. Aerosols as small as tens of nanometers
could serve as CCN, depending on their hygroscopicity and environmental humidity. Human activities
generate significant amounts of these aerosols, both
directly (e.g., dust, soot, and primary organic carbon)
or through gas-to-aerosol conversions (e.g., sulfate,nitrate, and secondary organic carbon). In the past
IPCC reports (e.g., IPCC 2007), the single largest
uncertainty in human-induced climate change has been
associated with how these anthropogenic aerosols
affect the Earthʼs climate through aerosol̶cloud̶
precipitation interactions. Considerable research isdevoted to this problem, from both observations andmodel simulations. While this study builds upon thesestudies, it focuses on how aerosols serving as CCN
modulate convection intensity by changing the
cumulus congestus fraction over the ultraclean tropicaloceans.
The indirect effects of aerosols on convective clouds
are highly uncertain. In addition, the in situ
observations of these violent clouds are rare. Remotesensing can be difficult to interpret because of thetransient nature of the convection, the difficulties insimultaneously observing aerosol and clouds while
separating them unambiguously, and the complications produced by ice phase particles. Cloud-resolving
models have simulated both enhancement andsuppression in storm intensity due to increasing
aerosols (e.g., Tao et al. 2012; Tao et al. 2007; Khain etal. 2008). These simulations included both longlasting mesoscale convective systems (the PRESTORM and TOGA COARE cases in Tao et al. 2007)
and single convective events (the CRYSTAL-FACE
case in Tao et al. 2007; all three cases in Khain et al.
2008). Even fewer studies focused on the tropicalmaritime convection. Several previous studies showedthat high CCN concentration may enhance convection
intensity and surface rainfall over the tropical oceans.
For example, Yuan et al. (2011) linked the enhancement of lightning activity and the convection strength
over the western Pacific to increased aerosol loading
due to volcanic emissions. Koren et al. (2010) studied
deep convective clouds over the tropical Atlantic andfound that increased aerosol loading invigorated
convection. Moreover, cloud-resolving model simulations of the maritime convection have shown increased
storm strengths when CCN concentrations were raised
above their background values. Wang (2005) studied
the deep convection during the Central Equatorial
Pacific Experiment (CEPEX 1993) using a 3D cloudresolving model with a two-moment microphysicalscheme. They found that convection intensity in-Vol. 91, No. 6

creased with CCN number concentration when theatmosphere was relatively clean. Tao et al. (2007)
simulated deep convection during the Tropical Ocean
Global Atmosphere Couple Ocean̶Atmosphere Response Experiment (TOGA COARE 1992̶1993) over
the western Pacific warm pool and also concluded thatan increase in CCN concentration produced deeper
convection and more surface rain. Khain et al. (2008)
simulated both the Global Atmospheric Research
Program (GARP) Atlantic Tropical Experiment(GATE 1974) and the Large-scale Biosphere̶Atmosphere Experiment (LBA 2002) cases and determinedthat an increase in CCN number concentration fromthe clean background resulted in enhanced surface
precipitation. They concluded that in an environment
with high humidity, increased CCN concentration
produced more condensational gain than evaporativeloss in cloud contents. The net effect was an increase insurface precipitation. van den Heever et al. (2011)
conducted radiative̶convective equilibrium simulations using a cloud-resolving model with a twomoment microphysical scheme and found that
increased aerosol loading strengthened updraft velocities and ice productions, as well as frequencies of the
middle clouds. Storer and van den Heever (2013) went
on to identify the increase of latent heating as the main
mechanism for the enhanced updrafts at lower levels,
whereas the increase of hydrometeor loading resulted
in weaker updrafts at upper levels. Lebo et al. (2012)
demonstrated that increasing aerosol concentration
invigorates an idealized supercell storm using both a
bin and two-moment bulk microphysical schemes.
Their sensitivity tests showed that the increase in latentheating is the key for convection invigoration by
aerosols.
Evidence from both observations and modelingstudies has shown convection invigoration due toincreased aerosol loading, especially in the maritime
environment. However, with the exception of van den
Heever et al. (2011), previous studies did not specifically address how aerosols (serving as CCN) affected thefrequencies of cumulus congestus. Nevertheless,congestus are an important component of tropicalconvection (e.g., Johnson et al. 1999; Folkins et al.
2008). Recent studies have emphasized their crucialrole in moistening mid-troposphere, setting up the
optimal condition for deep convective systems,
especially in the context of the Madden̶Julian
oscillation (MJO) lifecycle (Kemball-Cook and Weare
2001; Lin et al. 2004; Kiladis et al. 2005; Tian et al.
2006; Masunaga 2009; Lau and Wu 2010). Environmental factors and physical mechanisms conducive toDecember 2013

the cumulus congestus population over the tropicaloceans have been studied by Johnson et al. (1996),
Mapes and Zuidema (1996), Folkins et al. (2008), andFolkins et al. (2009). These factors include therelatively small convective available potential energy(CAPE) and the weak stable layer around the melting
layer, which is sometimes accompanied by a dry air
layer. In this study, we will add an additional factor tothese environmental factors. We will demonstrate,
through cloud-resolving model simulations, that thelack of aerosols serving as CCN over the clean tropicaloceans could promote the formation of congestus.
The paper is organized as follows, Section 2
describes the Goddard cumulus ensemble (GCE)model with an explicit bin microphysical scheme, as
well as the environmental and aerosol conditions usedin the simulations, Section 3 analyzes sensitivity tests
with an emphasis on cloud top heights and cumulus
congestus statistics, Section 4 attempts to identify
mechanisms of the simulated sensitivities throughdiagnostic analyses and additional model runs, Section
5 calculates the environment feedbacks produced bydifferent convection types. Finally, Section 6 provides
a summary followed by discussions.
2.

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X. LI et al.

Model and case descriptions

The GCE model has been developed and improvedat the NASA Goddard Space Flight Center over the
past 30 years (Tao et al. 2007). The simulations in thisstudy use a similar model setup as described in Tao etal. (2007), which uses the TOGA COARE surface flux
parameterization (Wang et al. 2003). For this study,
radiation is turned off. Previous simulations withradiation produced similar sensitivities (Tao et al.
2007). All scalar variables use a positive definite
advection scheme with a non-oscillatory option(Smolarkiewicz and Grabowski 1990). The simulations in this study are conducted in two dimensions
with the open lateral boundaries. The horizontal
domain size is more than 2,000 km, with a horizontal
resolution of 750 m for the 720 points at the center ofthe domain. Grids near the lateral boundaries are
stretched, and the vertical grids are also stretched witha finer resolution (~80 m) in the boundary layer andcoarser resolution (~1000 m) at upper levels. The
February 22, 1993 TOGA COARE sounding (shown in
Fig. 1) is used as the initial condition for the entire
domain. This sounding is a composite of aircraft data
below 6 km and an average of the 1800 and 2400 UTC−1
Honiara sounding above 6 km, with 1776 J kg CAPE
and −3.2 lift index (Trier et al. 1996). This case has
been studied extensively by both observations (e.g.,

Jorgensen et al. 1997; LeMone et al. 1998) andmodeling (e.g., Trier et al. 1996; Wang et al. 1996; Taoet al. 2007). A low-level cold pool is applied for the
first 10 min in order to initiate the convection. Modelsensitivities to the initial cold pool will be discussed in
the next section. The total simulation time is 12 h,when the cloud systems have achieved their quasisteady states, with new convection regenerating
steadily in front of the old cell.
The spectral bin microphysical scheme used in the
GCE model was developed at the Hebrew Universityof Jerusalem. Particle size distributions of water drops,
six types of ice phase particles (columnar crystals,
plate crystals, dendrites, snow aggregates, graupel, andfrozen drops/hail), as well as aerosol serving as CCN,
are represented by 33 mass size bins for each species.
Activations of aerosol in each size bin are explicitly
calculated at each time step. Small aerosol particles(r < 0.03 μm) are activated into cloud droplets with theequilibrium radius calculated by the Köhler curve.
Large particles take much longer to achieve theequilibrium; therefore, they are activated to be fivetimes their dry mass (Khain et al. 2000). The condensational growth rate and environmental supersaturation
are solved simultaneously at time intervals smaller
than the model time step of 5s, representing a moreaccurate water vapor budget. Ice nucleation, deposition/sublimation of ice particles, immersion freezing,ice multiplication, melting, drop/drop, drop/ice, ice/ice
collision/coalescence, and raindrop collisional breakup are all explicitly simulated in this scheme. Ice
nuclei (IN) are specified using Meyers et al. (1992) andare independent of the aerosol concentration in order tofocus on the CCN effects. The details of the bin
microphysical scheme can be found in Khain et al.
(2000).
The main differences between the model setup in
this study and that in Tao et al. (2007) are in the
representations of the aerosol size spectra. A simple
truncated exponential distribution was used in theprevious study (Twomey and Wojciechowski 1969).
Here the aerosol size distribution is represented by thesum of three lognormal distributions (Jaenicke 1993):
3n(log D) = ∑
i=1

Ni

1
2

(2π) log σ i



exp −

2

(log D−log Di),
22log σ iwhere D is the particle diameter, Ni, Di, and σi are thespecified parameters for different aerosol types. The
three modes represent the Aitken, accumulation, andcoarse modes of atmospheric aerosols. Figure 2 shows
the size distributions of the marine background and820

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Fig. 1. TOGA COARE sounding used in the simulations. The solid line represents the temperature, and the dash-dottedline represents the dew point temperature.

remote continental aerosols, as used in our sensitivity
tests. Aerosol concentration is assumed to be aconstant with height for particles with radius smaller
than 2 μm. The large particle concentration is constantfrom the surface to 1 km, and then decreases exponentially with an e-folding factor of 2 km. The sensitivity
tests are performed with either a homogeneous or an
exponentially decreasing aerosol profile, and theresults remain similar (see Section 3.2). For in-cloudaerosol processing in the mixed phase convection,
nucleation scavenging is the largest sink term andcloud evaporation/re-suspension is by far the largest
source term (e.g., Yin et al. 2005). Nucleation
scavenging is explicitly simulated in the model. Resuspension occurs when cloud droplets completely
evaporate. The re-suspended aerosol particle isassumed to have the spectrum of the sum of theaccumulation and coarse modes, allowing for a crude
representation of the in-cloud aerosol growth. Impact
scavenging is not considered in the current simula-tions; however, it is a sink term for CCN and does notaffect conclusions in this study. In addition, aerosols
can enter the simulation domain through the lower andlateral boundaries, where the boundary conditions are
fixed with the same aerosol spectrum and concentration as the initial condition.
3.

Results

The baseline simulations used the original sounding
shown in Fig. 1, with a background (clean) and high(dirty) aerosol scenarios. The background aerosol
scenario used the background marine aerosol distributions, shown as the solid line in Fig. 2. The highaerosol scenario simply increased the backgroundmarine aerosol value by tenfold. The purpose was topreserve the large aerosol tail that is characteristic ofmarine aerosols (Hudson 1993; Jaenicke 1993). An
aerosol scenario with a mean remote continental
spectrum was also used for additional sensitivity tests.
Convective systems are highly nonlinear. Both

December 2013

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Fig. 2. Aerosol size distributions represented by threelognormal functions. For the background maritime aerosol−3(solid), Ni = 133, 66.6, and 3.1 cm ; Di = 0.008, 0.266,
and 0.58 μm; and log σi = 0.657, 0.210, and 0.396,
respectively, for the three lognormal modes. The highaerosol spectrum in the dash-dotted line increases the
background maritime aerosol by 10 folds. For the remotecontinental aerosol (dashed), Ni = 3200, 2900, and 0.3−3cm ; Di = 0.02, 0.116, and 1.8 μm; and log σi = 0.161,
0.217, and 0.380, respectively.

modeled and observed aerosol indirect effects on
convective systems need to establish that theconclusions are statistically significant (e.g., Morrison
and Grabowski 2011). This requirement remains amajor hurdle in studying aerosol̶cloud̶precipitationinteractions. We attempt to establish the robustness of
our model simulation through both the model setup
strategy and additional sensitivity tests. First, the casesimulated in this study is a long-lived mesoscaleconvective system. A single sounding was used for thewhole domain, with the open lateral boundary
conditions and a large horizontal domain. This type ofmodel setup produced a self-regenerating convective
system that reached a quasi-steady state after 3̶5 h(Rotunno et al. 1988). The quasi-steady states achievedby the models were fairly robust with respect tochanges in model resolution (around 1 km) or methodof convection initiation (warm bubble vs. cold pool).
Second, we performed additional sensitivity tests thatvaried in initial aerosol spectra and environmental
wind shear. The results are reported in Section 3.2.
3.1 Baseline sensitivity tests
Three snapshots of simulated radar reflectivity forboth the background and high aerosol scenario areprovided in Fig. 3, representing the time evolution ofsimulated convective systems. The radar reflectivity

821

calculations assumed Rayleigh scattering. In Fig. 3, theleft column shows the marine background aerosol caseand the right column shows the high aerosol case.
Convection is generated quickly by applying a cold
pool near the ground for the first 10 min. During thefirst 90 min, cloud tops remain below 7 km, with no ice
formation for both cases. Figures 3a and 3b show theexamples of the simulated radar reflectivity at t = 70
min. The system propagates from the left to right, witha new cell forming at the leading edge and an old celldissipating behind it. Deep convection starts to form
after about 100 min. Figures 3c and 3d provide theexamples of deep convection in both cases. In general,the high aerosol case has higher cloud tops, more
frequent deep convection, and significantly more ice
production compared with those of the backgroundaerosol case. The simulations reach their quasi-steadystates after 5̶6 h. At this stage, all cells for the highaerosol case quickly rise above the 0°
C level andproduce large amount of ice, whereas significantly lesscells do so in the background case, as shown in Figs.
3c̶f. Even if they grow deep eventually, they tend tostay at the congestus stage for a much longer time. As aresult, the majority of cloud population remains as
cumulus congestus in the background aerosol simulation, as opposed to the frequent deep cumulonimbus
simulated in the high aerosol case.
The definition of cumulus congestus is not unique.
Previous studies used different criteria to definecongestus, mainly depending on the observational
instruments used (e.g., Johnson et al. 1999, Masunaga
et al. 2005, Jensen and Del Genio 2006, Luo et al.
2009). Nevertheless, there are two main physicalcharacteristics of cumulus congestus: 1) the convection is deep enough to rise above the trade inversion
and produces significant surface rain (as opposed to theshallow trade wind cumuli) and 2) there are little or no
ice phase particles in the convection (as opposed to thedeep cumulonimbus). In practice, cumulus congestus
clouds are identified by their cloud top heights, which
generally center on the melting level (~5 km in thetropics). In this study, we compare results using two
different congestus identification methods. Bothmethods use only the vertical profile information. The
first method defines congestus as model pixels with the
instantaneous surface rain rate greater than 0.1 mm−1h . In addition, the ice content cannot exceed 1 mg
−3m at any pixel along the column. In Fig. 3, thecongestus pixels identified by this method arehighlighted with hatched lines. This is an adequately
accurate way of identifying congestus. One possible
error source results from our assumption that the822

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Fig. 3. Snapshots of simulated radar reflectivity (dBZ) for the background (left column) and high (right column) aerosol
scenarios. Figs. 3a and 3b: t = 70 min; Figs. 3c and 3d: t = 390 min; and Figs. 3e and 3f: t = 600 min. The dashed lines
indicate the melting level. The hatched lines indicate the congestus pixels defined as a point with surface rainfall larger−1−3
than 0.1 mm h and ice phase particle mixing ratio less than 1 mg m at any point along the atmospheric column.

clouds are developing vertically, whereas in reality,
they often tilt. For example, in Fig. 3e, at x = 100 km,
one pixel that belongs to the dissipating stratiform rain
is mislabeled as congestus because of the tilting;however, this remains a small error.
The second congestus identification method uses
radar observations. The radar reflectivity is simulatedusing the modeled particle size distributions. The
cloud columns with surface radar reflectivity higher
than 5 dBZ and with the 0 dBZ (cloud top) heights
between 3 and 5 km are defined as congestus. The
threshold of 5 km for the cloud top is lower than mostof the previous studies (e.g., Jensen and Del Genio
2006); however, when we raise this upper limit, theresults tend to overestimate the congestus population
by mislabeling some of the dissipating stratiform area
as congestus, especially during the quasi-steady statelater in the simulation. In the current definition, thedifferences between the two methods are primarily atthe beginning of the simulation, where congestus
clouds are mislabeled as deep convection when theircloud top heights surpass 5 km.
Figure 4 shows the time series of the domain-meansurface rainfall rate (Fig. 4a) and the congestus percentage among the raining pixels (Fig. 4b). The thick lines(solid for the background aerosol case and dotted forthe high aerosol case) are the results using the first
congestus identification method, and the thin lines are

December 2013

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the results using the second method. The domainmean rain rates indicate that rain initiates earlier for the
background aerosol case. In addition, for the initial 100
min, when both cases produce congestus, thebackground case has slightly higher surface rainfall
than the high aerosol case, an indication of higher rainefficiency (e.g., Tao et al. 2007). However, once thedeep convection starts to form, the high aerosol caseproduces significantly larger surface rainfall becauseof ice production. Moreover, the background aerosol
case produces ice but with much less depth and vigor.
Therefore, the background case has more than 50% ofraining pixels occupied by congestus, whereas the highaerosol case produces mainly deep convection withtrailing stratiform anvil, as shown in Fig. 4b.
Figure 5 shows the domain-mean vertical profiles ofdifferent hydrometeor species in mass (the mixing ratioin Fig. 5a) and size (the mean diameter in Fig. 5b) for
all cloud types. Consistent with Figs. 3 and 4, the highaerosol case has much larger ice water content.
Graupel dominates the lower troposphere, whereas
pristine ice crystals are the main species at the upper
troposphere. There is very little pristine ice (blue lines)
in the background aerosol case, where the supercooled
water extends to around 9.5 km; for the high aerosol
case, it reaches 11 km because the probability offreezing at the same temperature is proportional todropletsʼ mass. Cloud droplets are smaller for the highaerosol case, as expected from the Twomey effect.
Figure 6 further elaborates the simulated cloud droplet
size distributions by plotting their averaged spectra.
The background aerosol case has a larger mean cloud
droplet diameter, with a wider spectrum, comparedwith the high aerosol case. Because heterogeneous
freezing dominates below 11 km, the simulated larger
pristine ice in the background case is primarily becauseof the immersion freezing of larger cloud droplets(Khain et al. 2000). This sensitivity agrees with someprevious studies (e.g., Fan et al. 2010; Morrison andGrabowski 2011). The pristine ice grows into snow
primarily by aggregation, and the mean snow size islarger for the clean background aerosol case possibly
because of the larger initial ice particles. The growth ofgraupel and hail involves both cloud droplets andraindrops. Although the mean cloud droplet size islarger for the background case, the mean raindrop size
is smaller (Fig. 5b). Several mechanisms are possibly
responsible for the size distributions of graupel, hail,and rain. For example, larger raindrops in the highaerosol case could form larger embryos for hail. In
addition, the high aerosol case has smaller droplets,
and these droplets extend higher, allowing for more

823

Fig. 4. Time series of domain-mean rainfall rate (a) andcongestus frequency among raining pixels (b). The solidlines are for the marine background aerosol case and thedotted lines are for the high aerosol case. For each aerosol
scenario, the thick lines use the lack of ice phase particles
as the congestus definition, whereas the thin lines use thesimulated radar reflectivity and cloud top heights to definecongestus.

collection. These could be the reasons for larger hailsizes simulated in the high aerosol case. On the other
hand, larger raindrops could occur by melting of larger
hail and graupel particles. Graupel often starts fromice/snow aggregates, which then collect cloud
droplets. Graupel initially might be larger for the
background case because of larger ice/snow aggregates; as they fall and collect both liquid and ice
particles, they might grow slower compared with thehigh aerosol case. Here at least two competing
processes are involved in graupel growth.
We have listed several mechanisms that might beresponsible for the simulated particle size differences.
However, more dedicated studies are required tocompletely understand how the cloud dynamics andmicrophysics interact and determine particle size
distributions.
3.2 Robustness of the baseline sensitivities
Additional simulations are performed in order to824

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Fig. 5. Domain-mean hydrometeor profiles of (a) mixing ratio and (b) mean particle size over the 10-h simulation period.
The solid lines represent the background aerosol case, and the dotted lines represent the high aerosol case. The blue lines
represent pristine ice, the green lines represent snow aggregates, the orange lines represent graupel, the red lines are for
hail, the black lines are for clouds, and the brown lines are for the rain.

Fig. 6. Normalized, mean cloud droplet size distributions
for the background and high aerosol cases.

establish the robustness of convection invigoration by
aerosol. These are not the ensemble simulations wherelarge numbers of cases are run with small random
perturbations (e.g., Morrison and Grabowski 2011);
rather, we seek the confirmation of two different quasisteady states achieved by high and low initial aerosol
concentrations. Each set of experiments differs only in
the initial aerosol spectra. The goal is to delineate therobustness of the baseline simulations with a relatively
small amount of sensitivity tests because of the highcomputational cost of running CRM with the explicit
bin microphysical scheme.
The convection is initiated using a cold pool
between the surface and 3 km for the first 10 min, with−1
a cooling rate of 0.01 K s . Our previous simulations
have shown some model sensitivities to the strengthsof the initial cold pool. In the experiment “cold pool,”−1
the cooling rate was increased to 0.02 K s , witheverything else identical to the baseline cases. The
time series of congestus percentages among the raining
pixels are plotted as the black lines in Fig. 7. The solidline represents the background aerosol case, and thedashed line represents the high aerosol case. The
bifurcated responses during the quasi-steady state arestill prominent, with the mean congestus percentage ofDecember 2013

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825

Fig. 7. Time series of domain-mean congestus frequency among the raining pixels. The definition of congestus isaccording to the presence of ice phase particles. The clean cases are represented by solid lines, and the dirty cases are
represented by dashed lines. The black lines (labeled cld pool) double the strengths of the initial cold pool. The reddashed line uses the continental background aerosol to represent a high aerosol environment over the oceans. The redsolid line uses half of the mean marine aerosol concentration to represent an ultraclean background. The blue lines
increase the vertical wind shear by about 50%. The green and yellow lines vary aerosol vertical distributions, with thegreen lines using a homogeneous concentration for all particle sizes and the yellow lines have an exponentiallydecreasing concentration above 1 km.

about 10% for the high aerosol case and more than
50% for the background aerosol case. With a muchstronger cold pool, the initial convection remains
congestus for a longer period compared with thebaseline cases. However, once the deep convection
starts, it surges quickly and remains strong with thehigh aerosol case. In the background aerosol case, theconvection slowly weakens to a congestus-dominantstate.
In the second set of sensitivity test, the initial aerosol
size spectra were varied. Here all environmental
settings were kept the same as the black lines in Fig. 7,
except for the background aerosol concentration,
which was reduced by half to represent an ultraclean
maritime environment. The high aerosol case now
used the remote continental aerosol spectrum, as
shown in Fig. 2, where the large-size tail is reduced
compared with the baseline simulation. The results areshown in Fig. 7 by the red lines. Similar to the case“cold pool,” perturbing initial aerosol concentrations
resulted in the same bifurcation.
Previous studies have shown the important role of
low-level wind shear in determining new cellregenerations and the formation of a squall line (e.g.,
Rotunno et al. 1988, Weisman et al. 2004). Fan et al.
(2009) showed that the vertical wind shear could

dominate the aerosol indirect effect in deep convection. In the “wind shear” case, we tested a similar idea
by increasing the low-level wind shear. In the baseline−1
simulations, the wind speed increases from 0 to 9 m sover the lowest 2 km, then gradually reverses to 2
−1
m s at 6 km. Above 6 km, the winds center around 0−1−1
m s with small variations (±2 m s ) because themean wind at mid-troposphere has been removed fromthe original sounding in order to keep the simulated
system centered in the domain. In the sensitivity test
“wind shear,” the vertical wind was increased
uniformly by about 50%, increasing the low-level−1−1−1
wind shear from ~4.5 m s km to about 6.5 m s−1km while keeping the mid- and upper-level wind
relatively intact. All other variables were identical to
the test “cold pool.” The results are shown in the bluelines in Fig. 7. In this set of simulations, the model
sensitivity to the initial aerosol largely disappeared.
For the initial 140 min, both tests exclusively produced
congestus. Thereafter, the simulations went into an
adjustment period when deep convection and congestus coexisted. After about 500 min, both cases reacheda quasi-steady state with congestus as the mode of
convection. Note that the precipitation systems withincreased low-level wind shear have different characteristics compared with all other simulations in that the826

Journal of the Meteorological Society of Japan

convective systemʼs propagation speed is much faster−1−1(~40 km h ) than the other simulations (~25 km h ).
Fovell and Tan (2000) varied the low-level wind shear
systematically and found similar results. Their results
pointed to a switch from multicell regime for the lowwind shear cases to a unicell regime for the very highwind shear case. In addition, they indicated that thethreshold wind shear that governs the switch depends
upon different sounding characteristics.
The observations of aerosol vertical distributions
remain scarce. Two types of profile were commonlyused in model simulations: the exponential andhomogeneous profiles. Some evidence suggested thatconcentrations of large particles had the former profile
while small particles had the latter (e.g., Fridlind et al.
2012). The baseline simulations used a combination ofthese profiles. Additional simulations were conductedto study the model sensitivity on aerosol verticalprofiles. The green lines in Fig. 7 represent the sensitivity tests, where the aerosol concentrations were kept
constant for all sizes (case “prof_const”). The yellow
lines kept constant aerosol concentrations within thelowest 1 km, and then reduced them exponentiallyabove it, with an e-folding factor of 2 km (case “prof_exp”). As shown in Fig. 7, both sensitivity tests havebifurcations in the congestus frequency and support
the conclusions from the baseline simulations.
Moreover, we notice that the congestus frequency is
consistently lower for case “prof_const” than that forcase “prof_exp,” given the same aerosol concentrationnear the ground. This may be an indication that CCN
entrained from the sides and top of the clouds may also
contribute to convection invigoration.
The sensitivity tests show that the convection
invigoration by increasing aerosol concentration fromthe clean marine background level is a robust conclusion for the TOGA-COARE sounding. Furthermore,
the single TOGA-COARE sounding used in this study
is very similar to the mean sounding during congestus
formation taken at the ARM Nauru Island site
compiled by Jensen and Del Genio (2006). Nevertheless, the aerosol sensitivity diminished when the lowlevel vertical wind shear was increased. It remains
unclear to what extent aerosol invigorating convection
can happen for different environmental conditions
over the vast interior of the tropical oceans. More casestudies are required in the future.
4.

Mechanisms

Several mechanisms have been previously proposed
to explain convection invigoration by increasing
aerosol loading. In this section, we test theseVol. 91, No. 6

Fig. 8. Same as the “cld pool” case in Fig. 7, except that allice phase microphysics are turned off. The definition ofcongestus in this case is according to the simulated radarreflectivity.

mechanisms systematically, as well as investigate
possible new mechanisms, through sensitivity tests
and diagnostic studies.
4.1 Latent heating through freezing
Perhaps the most popular mechanism for convection
invigoration is the freezing of supercooled water (e.g.,
Rosenfeld et al. 2008). In the high aerosol case, there
are more smaller cloud droplets, which reduce cloud
collision/coalescence and result in delayed rain
formation. More cloud water is carried above themelting level by the updraft compared with the clean
background aerosol case. The additional latent heatreleased by cloud droplet freezing may increase thecloud buoyancy at the upper levels, thus invigorate
convection and increase cloud top heights.
In order to verify this, we conducted a set ofsensitivity tests, where all the ice phase microphysicalprocesses are turned off. This removes the latentheating by freezing and its feedbacks that may
invigorate the convection. The resulted congestus timeseries are plotted in Fig. 8. After omitting all ice
processes, the congestus percentages still bifurcate,albeit to a lesser extent. The high aerosol case has
about 40% of congestus compared with only 10% for
the “cold pool” case in Fig. 7. The background aerosol
case has a mean congestus population of about 80%.
The smaller differences simulated in Fig. 8 indicatesthat freezing does contribute to the convection
invigoration. However, on the basis that bifurcation
still exists in Fig. 8, we conclude that the freezing
mechanism contributes to convection invigoration;
however, it is not the determinant factor.

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4.2 Latent heating through condensation
When the water vapor is abundant but CCN
concentration is low, as in the TOGA COARE case,aerosol concentration may become the limiting factor
in cloud condensational growth (Reutter et al. 2009).
Cloud droplet concentration is low in the marine
background case and cannot keep pace with the rapidly
increasing supersaturation in the updraft cores. To
activate smaller aerosols that have survived the cloudbase activation, higher supersaturation is required.
Another characteristic of marine convection is thatthey are very efficient rain producers. Sea sprayparticles contribute significantly to coarse mode
aerosols (r > 0.5 μm) (Fitzgerald 1991). These large
CCN are activated at relatively low supersaturationand can serve as collectors, efficiently removing small
cloud droplets and keeping the cloud droplet
concentrations low. Thereafter, the condensational
growth rate becomes less compared with a high aerosol
scenario where plenty of cloud droplets are available tosoak up the supersaturation. Wang (2005) proposed
this mechanism for their simulated convection
invigoration using a CEPEX case study. In addition, it
has been confirmed by Lebo et al. (2012) and Storer
and van den Heever (2013).
To test this mechanism, the condensational growth
rates are plotted against the in-cloud supersaturation(%), as shown in Fig. 9. For the same in-cloudsupersaturation, the condensational growth rate ismostly higher in the high aerosol case (the red
triangles). Assuming that all saturated water vapor
condenses into cloud droplets (as in the majority ofsaturation adjustment of the bulk microphysicalschemes), the condensational rates vs. supersaturation
should align along a straight line, as shown by thedashed line in Fig. 9. When the cloud droplet concentrations are low, the black squares in Fig. 9 generally
fall below the idealized line. In this case, the vapor
pressure increases to above saturation, sometimes
large enough to activate smaller aerosols that havesurvived the cloud base activation. This is called “incloud activation.” In this situation, the total latent heatrelease is reduced, resulting in weaker convection.
4.3 Cloud top evaporation
Over the tropical oceans, the atmospheric instability
is relatively low. In addition, a weak stable layer often
exists near the 0°
C level. The TOGA COARE sounding2 −2
has both features, with a CAPE of 1776 m s , and aweak inversion near 5 km. The clouds formed underthese conditions may sometimes be considered as
successive thermals released from the boundary layer.

827

Fig. 9. Condensational growth rate vs. in-cloud supersaturation (%) at 1 km height. Each data point represents thevalue at one grid cell, between t = 20 min (rain initiation)and t = 60 min. The black squares represent the background aerosol case. The red triangles represent the highaerosol case. The dashed line is the maximum growth rate
where all supersaturation is converted into cloud water
within one time step. This represents one simple assumption made in some bulk microphysical schemes.

Each thermal reaches the inversion layer and detrains,cooling and moistening the air at the cloud top,
destabilizing the layer, and enabling the next thermal
to grow further. Simulated radar reflectivity animations (not shown) demonstrated this behavior. For thehigh aerosol case, the mean cloud droplet sizes are
smaller and cloud water content is higher. Smaller
droplets evaporate faster and produce more intense
cooling/moistening at its top, destabilizing the cloud
top layer more efficiently than the background aerosolcase. This allows for more rapid development of deep
convection. Once the cloud overcomes the stable layer
near 5 km, the atmosphere above it remains neutral orslightly unstable, resulting in deep convection withsignificant ice productions. The extra heating fromcloud droplet freezing in the high aerosol case further
enhances the convection.
To illustrate this mechanism, Fig. 10 shows the timeseries of mean cloud top evaporation rates during theinitial cloud development, when both cases have
similar cloud top heights and produce congestus
exclusively (cf. Figs. 3a and 3b). To calculate the cloud
top evaporation rate, we took the cloud top grids where−1
the updraft is more than 1 cm s , the cloud water−4−3
mixing ratio is larger than 1 × 10 g m , and the air

828

Journal of the Meteorological Society of Japan

Fig. 10. Variations of mean cloud top evaporation rate withtime. Each data point is an average of cloud top grids thatare within the updrafts but are sub-saturated due toentrainment. The black line represents the backgroundaerosol case. The grey line represents the high aerosolcase.

is sub-saturated. These points represent the grids
where the detraining cloud droplets evaporate at thecloud top. These data points are then averaged for eachtime step. Figure 10 shows that the high aerosol casehas a consistently higher cloud top evaporation ratecompared with that of the background case, supporting
the proposed mechanism. The combination of the
reduced latent heating and cloud top evaporation forthe background aerosol case keep the convection
weaker and lower.
4.4 Rain evaporation
Tao et al. (2007) proposed the mechanism of rain
evaporation enhancement according to their model
simulations. The convective cell regeneration andpropagation are closely related to the cold pool, which
in turn is produced and maintained by the storm
downdraft and rain evaporation in its outflow (e.g.,
Rotunno et al. 1988; Weisman et al. 2004). A stronger
convection, as simulated in the high aerosol case, isable to induce stronger compensatory downdraft, thus
stronger rain evaporation and cold pool. In turn, thestronger cold pool results in stronger secondary
convection. While this is not the initial cause of
convection invigoration, once the convection becomesstronger, the feedback mechanism helps to maintain it
through enhanced secondary convection.
Among the four mechanisms described in thisVol. 91, No. 6

section, the latent heat release through condensation isprobably the crucial one. The change in latent heating
through freezing can be important when the convectionis already deep, but it does not accelerate the transition
from congestus to deep convection. The cloud top
evaporation mechanism can act independently tofacilitate the subsequent convection, whereas the rain
evaporation mechanism must act through a positive
feedback. Cloud/rain evaporation mechanisms havebeen inferred from the model data. However, direct
proof remains a challenge because evaporation cannot
be turned off in the simulations. Nevertheless, ourstudy shows that sophisticated microphysical schemes
are required to delineate details of aerosol indirect
effects, especially in the representations of vapor
condensation and evaporation. The spectral bin
microphysical scheme used in this study is a suitable
tool. It solves the cloud droplet growth and theenvironmental supersaturation simultaneously at timeintervals one tenth of the model time step of 5s and canbe even shorter in the case of strong updrafts (Khain etal. 2008). This represents an accurate water vapor
uptake/release by droplets. On the other hand, a bulk
microphysical scheme must make assumptions oncondensation/evaporation growth. Nevertheless, someof the more sophisticated two-moment bulk schemes,
e.g., Wang (2005), Lebo et al. (2012), and Storer andvan den Heever (2013), did simulate enhanced
condensation due to increased aerosol concentration.
Because of the wide range of assumptions made withdifferent bulk microphysical schemes, a bin scheme isgenerally required to represent various mechanisms
described in this section.
5.

Feedbacks to the environment

We have shown that aerosol serving as CCN cansignificantly modulate tropical maritime convection.
Using the identical TOGA COARE sounding, themodel produces mainly cumulus congestus in a clean
background aerosol scenario. When the aerosol
concentration increases to a level similar to the remotecontinental background, the convection invigorates to
produce mainly deep convection with trailing stratiform rain. These sensitivity tests offer a unique
opportunity to quantitatively study how these twodifferent cloud types affect their environment. Because
all simulations began with an identical sounding, we
can obtain a clear picture of how different cloud types
modify their ambient environments by merely
observing the temperature and water vapor profiles
after the cloud system passes through.
Figure 11 shows the temperature and water vapor

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829

Fig. 11. Mean environmental temperature (left panel) and water vapor (right panel) profiles before and after simulated
convective system passage. The solid lines represent the initial profiles, the dashed lines represent the backgroundaerosol simulation, and the dash-dotted lines represent the high aerosol simulation. In addition, temperature profile
differences before and after the storm passage are plotted in the left panel (with the axis at the top) in order to show moredetails.

profiles before and after the convective system passing
through a 100 km swath of the model domain. The
solid lines represent the initial profiles. At the end of
the 10-h simulation, when the model has achieved aquasi-steady state, the profiles in the 100 km domain
immediately behind the cloudy area are averaged torepresent the mean environmental condition after it ismodified by either the congestus or deep convection.
The dashed line uses the marine background aerosol,and the dash-dotted line uses the high aerosol scenario.
In order to show details, the temperature differences
are also plotted with the x-axis labeled at the top of theframe. Both simulations stabilize the lower atmosphere by cooling near the surface and warming
between 1 and 4 km. However, the congestus cools the
mid-troposphere between 4 and 8 km, whereas thedeep convection case produces deep heating up to 14
km. Although the storm passage stabilizes theatmosphere in both cases, the congestus case has moreinstability in the mid-level, providing a more favorable
condition for the next batch of convection to growdeep. The water vapor profiles show similar results.
Both cases dry the boundary layer effectively. The
drying continues up to about 4 km for the deep
convection case. However, the congestus case quickly
switches from drying to moistening at about 2 km. It
remains a larger moisture source than the deep
convection up to about 6 km. Above 6 km, deep
convection acts as the moisture source up to thetropopause, whereas the congestus shows limited
influence in both temperature and water vapor.
These model results agree with many previous
observations on the roles of congestus in the tropics,
especially in the context of the MJO lifecycle. The
observations and analyses have shown that prior to thepeak MJO phase, which is characterized by deep
convection with large anvils, there is a gradual buildup
of mid-tropospheric water vapor (e.g., Kemball-Cookand Weare 2001; Kiladis et al. 2005; Tian et al. 2006).
This water vapor buildup is mainly attributed to the
cumulus congestus population that leads to the peakphase (e.g., Masunaga et al. 2006; Lau and Wu 2010).

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Journal of the Meteorological Society of Japan

Our model simulations confirm the roles congestus
play in moistening and destabilizing the midtroposphere, setting up favorable conditions for the
deep convection during the MJOʼs peak phase. A moresignificant and new aspect of this study is thatvariations in aerosol number concentrations alone
could change the state of convection from thecongestus dominant regime to deep convection
dominant regime.
6.

Summary and discussion

6.1 Summary
This study examines the significance of aerosol(serving as CCN) in modulating strengths of thetropical oceanic convection during a TOGA COARE
convection period, with the focus on cumulus
congestus population. The GCE cloud-resolving
model with a spectral bin microphysical scheme isused to investigate how increasing the aerosol number
concentration from the mean maritime backgroundlevel can invigorate convection and shift the cloud
population from the cumulus congestus dominant
regime to deep convection regime. It is argued thatover the tropical oceans, where water vapor isabundant, atmospheric instability is low, and a weakstable level often exists near the 0°
C, the maritime
background environment combined with a low aerosol
concentration is conducive for the formation ofcongestus. The sensitivity tests that vary the initial cold
pool strength, low-level wind shear, aerosol spectra,
and the model horizontal resolution (not shown) showrobust responses of convection invigoration whenaerosol concentrations are raised from their clean,background values. Several mechanisms for convection invigoration by aerosol are identified and tested
using model data and additional sensitivity tests. They
include increased latent heat release by both condensation and freezing, enhanced cloud top evaporation, andincreased downdrafts and cold pool strengths. Amongthese mechanisms, the enhanced latent heating invapor condensation is probably the most importantfactor.
6.2 Discussions
In this section, we offer some general discussions on
tropical convection that stemmed from this study.
Although we feel this is a relevant and potentially
important concept, it remains a hypothesis and much
more work is required in the future.
The early theory on tropical clouds consists of two
types: deep towers that reach the tropopause and small
cumuli confined below the trade wind inversions (e.g.,

Vol. 91, No. 6

Simpson 1992). The radar observations have significantly expanded our understanding of tropicalprecipitation. On the basis of radar-observed cloud top
height distributions, Johnson et al. (1999) proposed thetri-modal tropical convection, which added cumulus
congestus to the earlier proposed theory by, e.g., Riehl
and Malkus (1958). Figure 12 shows a schematic plotof the tri-modal convection with aerosol lifecycle
added in the image. Note that the deep tower (thecumulonimbus in Fig. 12) has an extensive stratiform
anvil attached. This stratiform region is important bothdynamically and thermodynamically in the tropics(e.g., Houze 2004). The congestus population andstratiform region are complementary in terms of theirflow patterns (mid-level outflow vs. mid-level inflow),
as well as in their heating profiles (bottom heavy vs.
top heavy). Furthermore, melting of ice phase particles
in the stratiform region may contribute to the stability
near the 0°
C level in the tropics (Mapes and Zuidema
1996; Posselt et al. 2008). How different cloud populations form and evolve in the tropics remains an active
research topic. In this study, we have added anotherlayer of complication, i.e., maritime aerosols and theireffects on convection intensity. The TOGA COARE
case study has shown that clean background aerosol
may help maintain the congestus population. In addition, we have introduced some speculations in Fig. 12
to show how different types of clouds might affectmaritime aerosolsʼ budget. The current GCE model
does not include the full aerosol life cycle with all
sources and sinks. However, this does not affect theconclusions in this study because we used open lateralboundary conditions. There is significant wet removalof aerosols in the precipitating area. However, theclean air mass generally leaves the convective systemand does not participate in any new development. In
the future, we plan to add the full maritime aerosol
physics and chemistry in the cloud-resolving model inorder to quantify the marine aerosol budget.
The spectrum of convection shown in Fig. 12 andtheir dynamical interactions pose a serious challenge
in global climate simulations, where individual
convection generally cannot be resolved. Modelingstudies have shown large sensitivities in the generalcirculation models (GCMs) to different cumulus
parameterization schemes. Nevertheless, improvements in cumulus parameterization that limit convection strengths in some way often led to better
representations of the tropical convection and more
realistic MJO signals. For example, Boyle et al. (2008)
reported better forecasts for the TOGA COARE casewhen using a modified Zhang̶McFarlane scheme with

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X. LI et al.

Fig. 12. North̶south schematic view of the three inversion layers and their associated major cloud populations over thetropical oceans. The dark arrows indicate airflow directions and strengths. The red arrows indicate source (upward) andsink (downward) of marine aerosols associated with different clouds.

a relative humidity threshold for convection trigger inthe community climate model (CAM3). Bechtold et al.
(2008) found improved forecasting scores and more
realistic spectrum of convectively coupled equatorial
waves when the convective entrainment rate wasadjusted on the basis of environmental humidity in the
ECMWF Integrated Forecasting System. A superparameterized CAM3, where convection is explicitly
resolved by a 2D cloud-resolving model within a GCM
grid, produced better MJO signals than the original
CAM3 (Thayer-Calder and Randall 2009). Moreover,
the reason is attributed to the improved representationsof the vertical distributions of water vapor. There ismounting evidence that some sort of convection“limiter” or “damper” is required in the GCMs toprevent the atmosphere from releasing its instability
too quickly. This would allow for a gradual building up
of moisture at the mid-troposphere, setting up
conditions for deep convection episodes with extensive stratiform regions. Although the GCMs may
simulate the “limiter” by, e.g., increasing the convection entrainment rate in the cumulus parameterization
scheme, a clear picture of physical mechanisms is still
missing.
On the basis of the modeling studies, we propose aphysical mechanism that may act as a convection“limiter,” i.e., the very low marine background aerosol
concentrations. The model simulations show that thelack of marine aerosol serving as CCN may limit
convection strengths and promote congestus formation. This hypothesis points to a possible mechanism
for convection damping. This limiter also applies to theglobal cloud-resolving simulations. For example, withthe superparameterization in a GCM, the convection
still shows hyperactivity (Thayer-Calder and Randall2009). Adding the damping effect related to low
aerosol concentration over the tropical oceans may
reduce this hyperactivity. Several studies are required,including both cloud-scale and global-scale modeling,
as well as observational support, in order to make these
speculations relevant. Understanding maritime aerosol
lifecycles, moisture source/sink, as well as theirinteractions with different cloud types, are some of the
key issues.
Acknowledgments
This research is mainly supported by NASA
headquarters and the NASA PMM Mission. We thanktwo anonymous reviewers for their detailed suggestions that significantly improved the clarity of thismanuscript. The first author wishes to thank Professor
Rob Fovell of UCLA for helpful discussions, Amy
Houghton of USRA, Lisa Nalborczyk, and Stephen
Palm of SSAI for editing the manuscript, and Jenny
Zeng for beautifully rendering Fig. 12. This study is
dedicated to Joanne Simpson, who remains an832Journal of the Meteorological Society of Japan

inspiration for many of us.
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