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RESEARCH ARTICLE
ATMOSPHERIC PHYSICS

Substantial convection and
precipitation enhancements by
ultrafine aerosol particles
Jiwen Fan,1* Daniel Rosenfeld,2 Yuwei Zhang,1,3 Scott E. Giangrande,4 Zhanqing Li,3,5
Luiz A. T. Machado,6 Scot T. Martin,7 Yan Yang,1,8 Jian Wang,4 Paulo Artaxo,9
Henrique M. J. Barbosa,9,10 Ramon C. Braga,6 Jennifer M. Comstock,1 Zhe Feng,1
Wenhua Gao,1,11 Helber B. Gomes,12 Fan Mei,1 Christopher Pöhlker,13 Mira L. Pöhlker,13
Ulrich Pöschl,13,14 Rodrigo A. F. de Souza15

eep convective cloud (DCC) systems in the
tropics produce copious precipitation and
drive the global-scale circulation (1). Precipitation, latent heating, and cloud radiative forcing associated with DCCs are
strongly modulated by cloud microphysical processes (2). These processes in tropical DCCs are
initiated from droplet nucleation (which is determined by vapor supersaturation in updrafts and

Atmospheric Sciences and Global Change Division, Pacific
Northwest National Laboratory, Richland, WA 99352, USA.
Institute of Earth Sciences, Hebrew University of Jerusalem,
Jerusalem 91904, Israel. 3Department of Atmospheric and
Oceanic Science and ESSIC, University of Maryland, College
Park, MD 20742, USA. 4Environmental and Climate Sciences
Department, Brookhaven National Laboratory, Upton, NY,
USA. 5State Laboratory of Earth Surface Process and
Resource Ecology, GCESS, Beijing Normal University, Beijing,
China. 6Centro de Previsão de Tempo e Estudos Climáticos,
Instituto Nacional de Pesquisas Espaciais, Cachoeira
Paulista, Brazil. 7School of Engineering and Applied Sciences
and Department of Earth and Planetary Sciences, Harvard
University, Boston, MA, USA. 8Beijing Municipal Weather
Modification Office, Beijing 100089, China. 9Instituto de
Física, Universidade de São Paulo, Rua do Matão 1371,
05508-090 São Paulo, Brazil. 10Department of Physics,
University of Maryland Baltimore County, Baltimore, MD,
USA. 11State Key Laboratory of Severe Weather, Chinese
Academy of Meteorological Sciences, Beijing 100086, China.
12
Atmospheric and Climate Dynamics Laboratory, Institute of
Atmospheric Sciences, Federal University of Alagoas, Brazil.
13
Multiphase Chemistry and Biogeochemistry Departments,
Max Planck Institute for Chemistry, 55020 Mainz, Germany.
14
Johannes Gutenberg University Mainz, 55122 Mainz,
Germany. 15Department of Meteorology, School of
Technology, Amazonas State University (UEA), Manaus-AM,
Brazil.
2

*Corresponding author. Email: jiwen.fan@pnnl.gov

Fan et al., Science 359, 411–418 (2018)

26 January 2018

aerosol properties such as composition and size
distribution. Aerosol impacts on cloud processes
via this pathway are known as aerosol indirect
effects, referred to as aerosol-cloud interaction
in the most recent Intergovernmental Panel
on Climate Change report (3). Aerosol impacts
are a key uncertainty in understanding the current and future climate (3) as well as extreme
weather (4). DCCs have complicated dynamics
and microphysics; therefore, aerosol impacts on
them are extremely complex and hard to disentangle. Previous studies have shown that aerosols
could invigorate or suppress DCC intensity through
aerosol indirect effects, contingent on dynamical
and thermodynamical conditions [e.g., (4–12)].
In the case of warm-cloud bases (>15°C), increasing aerosol concentrations can suppress warm
rain because of a reduction in droplet size, which
allows more cloud water to be lifted to a higher
altitude; the freezing of this larger amount of
cloud water releases additional latent heat, thereby
invigorating convective updrafts [referred to as
“cold-phase invigoration” (13)]. The importance
of the process of enhanced condensation by aerosols
to deep-cloud development has been shown in
tropical maritime clouds (14–16). Enhancement
in DCC intensity favors enhanced storm electrification, larger precipitation rates, and taller clouds
with larger anvils.
Over the Amazon, unperturbed background
aerosol concentrations found within the region
of pristine rainforests are low, with values reported in the low hundreds per cubic centimeter
(17–19), similar to conditions thought to have

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Aerosol-cloud interactions remain the largest uncertainty in climate projections. Ultrafine
aerosol particles smaller than 50 nanometers (UAP<50) can be abundant in the
troposphere but are conventionally considered too small to affect cloud formation.
Observational evidence and numerical simulations of deep convective clouds (DCCs) over
the Amazon show that DCCs forming in a low-aerosol environment can develop very
large vapor supersaturation because fast droplet coalescence reduces integrated droplet
surface area and subsequent condensation. UAP<50 from pollution plumes that are
ingested into such clouds can be activated to form additional cloud droplets on which
excess supersaturation condenses and forms additional cloud water and latent heating,
thus intensifying convective strength. This mechanism suggests a strong anthropogenic
invigoration of DCCs in previously pristine regions of the world.

existed in preindustrial times (18). Responses of
cloud properties to aerosols are nonlinear and
most sensitive to the addition of particles when
the background concentration is very low [i.e.,
the “aerosol-limited regime” (17, 20, 21)]. Therefore, the aerosol effect on convective clouds over
the Amazon region is expected to be large, and
previous studies have demonstrated evident smoke
aerosol influence on convective clouds during
dry seasons in the Amazon (June to September)
(22–24). In addition to the low total aerosol concentration, ultrafine aerosol particles with diameters less than 50 nm (UAP<50) are typically nearly
absent over the Amazon rainforest, as new particle formation has rarely been observed in the
boundary layer there (25, 26). UAP<50 are often
transported downward from the upper troposphere and subsequently grow to the particles
larger than 50 nm that are traditionally considered as cloud condensation nuclei (CCN>50) (27).
However, the Manaus metropolis, a city of about
2 million people, produces a pollution plume that
generally follows the northeasterly trade winds
and is an important source of elevated UAP<50
concentrations (28).
The Observations and Modeling of the Green
Ocean Amazon (GoAmazon 2014/5) experiment
was carried out near the Manaus metropolis to gain
a better understanding of the impacts of pollution
emissions from Manaus on the hydrological cycle
and climate in the tropical rainforest (29, 30). We
took advantage of the unique observational data
from GoAmazon 2014/5 (e.g., the direct cloud updraft velocity measurements for DCCs occurring in
a similar convective environment but different
aerosol environment) to investigate the observed
aerosol effects, as well as to perform and analyze
high-resolution simulations of a sample case, using
the detailed spectral-bin microphysics scheme to
scrutinize the mechanism. We found that the
UAP<50 introduced by the Manaus pollution plume
enhanced convective intensity and precipitation
rates to a degree not previously observed or simulated. The detailed simulations show that the
drastic enhancement in convective intensity is
primarily attributable to the enhanced condensational heating, with the latent heat released
from enhanced ice-related processes at upper
levels playing a secondary role. This differs from
the previous “cold-cloud invigoration” concept (13),
which does not consider aerosol impacts on condensational heating. As illustrated in Fig. 1, the
enhanced condensational heating is driven mainly
by the activation of UAP<50 well above the cloud
base, which is attributable to high vapor supersaturation with respect to water (Sw) resulting
from fast droplet coalescence. The subsequent
condensational growth considerably lowers the
water supersaturation, liberating additional latent
heating in the lower atmosphere, which enhances
convective intensity drastically. The additional condensate from this process increases the amounts
of both warm rain and supercooled cloud water.
Furthermore, enhanced droplet freezing, including riming, occurs as a result of the production
of additional supercooled water, and intensified
convection enhances ice deposition at high levels.

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Cumulatively, these enhanced ice-related processes
at middle and upper levels further enhance convection, but to a much lesser degree relative to enhanced condensation at low levels. The effects of
UAP<50 contrast with those of CCN>50: UAP<50 increase warm rain rate and have no effect on the
timing of peak rain rate, whereas CCN>50 suppress warm rain rate and delay the timing of peak
rain rate.
Observed relationships of convective
intensity and precipitation with aerosols

Fig. 1. Illustration of the effect of ultrafine aerosol particles (UAP<50) on tropical convective
clouds. In clouds that lack UAP<50 (left), the clouds are highly supersaturated as a result of fast drop
coalescence that forms warm rain and reduces the integrated droplet surface area available for
condensation. With added UAP<50 (right, red dots), an additional number of cloud droplets are
nucleated above cloud base, which lowers supersaturation drastically by enhanced condensation,
releasing additional latent heat at low and middle levels, thus intensifying convection. The additional
condensate adds to both the warm rain and supercooled cloud water; when freezing occurs aloft,
this addition further enhances convection (i.e., a small increase in convection but enhancement of
precipitation and storm electrification).
Fan et al., Science 359, 411–418 (2018)

26 January 2018

cipitation intensity) also increased with increasing
Na throughout the vertical profile for D > 15 nm
(Fig. 2C, left), associated with hydrometeor size
and precipitation rate increases. Remarkably, the
increasing trend in updraft intensity and radar
reflectivity as Na increases does not hold well
when considering only those aerosols with D >
50 nm (Fig. 2A, bottom, and Fig. 2B, right; similar results were obtained for D > 100 nm). The
result suggests that UAP<50, not CCN>50, are
the primary drivers for intensified convection.
The probability density functions of w and rain
rate from the four aerosol groups indicate that
occurrences of stronger updraft velocities are
more frequent with the increase of Na for UAP<50
(fig. S1A, left) and that the maximum rain rates
also increase (fig. S2C).
To corroborate that UAP<50 are the main factor contributing to the observed DCC enhancements, we conducted additional analyses to help
isolate aerosol effects from thermodynamic controls. Locally driven Amazon deep convective events
within the wet season should initiate and evolve
under similar diurnal controls on their dynamical
and thermodynamical environments (33, 35). We
examined traditional radiosonde thermodynamic
forcing parameters such as the convective available potential energy (CAPE) and convective inhibition (CIN) before convection. The CAPE is very
similar for these events (Fig. 2A, black circles) and
is not strongly correlated with updraft intensity.
Under similar CAPE conditions, CIN magnitudes
should help to reveal how likely it is for DCCs to
initiate and to determine what may be their relative intensities. CIN varies quite a bit (Fig. 2A,
triangles) but again shows no correlation with the
enhanced convective intensity as Na of UAP<50
increases. Analyses of profiles of the temperature,
relative humidity (RH), and zonal U- and meridional V-components of the wind fields representative of the pre-storm environment also indicate
that these environmental profiles do not correlate with an increase of updraft intensity as Na of
UAP<50 increases (fig. S3). In fact, our lowest-Na
group exhibited higher RH at 2- to 5-km altitudes
than did the higher-Na groups, which should have
favored stronger convection and offset some aerosol
effects. This means that the trend for enhanced
updraft intensity with the increase of Na counting
UAP<50 should have potentially been more prominent if RH for the lowest-Na group is similar to
those of the higher-Na groups. Although we cannot
guarantee that RWP observations captured the
representative updraft cores for every single event,
these analyses do not show any covariation of aerosols with dynamics and thermodynamics for these
locally occurring systems. This provides clear evidence that the enhanced convective intensity seen
with the increase in Na of UAP<50 is not solely
controlled by factors other than UAP<50.
Modeled mechanism
To understand the physical processes and mechanisms responsible for the observed intensification
of updrafts by UAP<50, we conducted model simulations at a cloud-resolving scale of 0.5 km for a
typical wet season convective event, as on 17 March
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The measurements from the U.S. Department of
Energy (DOE) Atmospheric Radiation Measurement 1290-MHz Ultra High Frequency (UHF) Radar
Wind Profilers [RWP (31, 32)] during GoAmazon
2014/5 provide the most accurate data on vertical
air motions and precipitation rates for Amazon
DCCs, allowing us to directly examine the relationship of updraft intensity to aerosols. We focused
on convective cells of local origin (33, 34), favoring relatively simple and similar dynamics across

such DCCs in the wet season. The intrusion of
the Manaus pollution plume provides a natural
laboratory for exploring pollution aerosol effects.
Our analysis period was the 2014 wet season
(1 March to 31 May). Figure 2 shows the correlation of mean vertical velocity estimated for the
top 10th percentile of updrafts in each convective
event with the respective aerosol particle number concentration (Na), averaged over a 30-min
period before the start of convection at the T3
site (~70 km southwest of Manaus, downwind).
The Na at the T3 site varied substantially from
case to case. We found that convective updraft
velocity (w) increased with an increase of Na for
aerosols with a diameter (D) larger than 15 nm
(Fig. 2A, top). When the updraft velocity was segregated according to four aerosol groups (ranging
from lower to higher Na), we observed a monotonic and striking increase of w, which increased
from 4 m s−1 for the lowest-Na group to 10 m s−1
for the highest-Na group (Fig. 2B, left). The corresponding radar reflectivity (a measure of pre-

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2014 (fig. S4) (34). We used detailed spectral-bin
microphysics (36, 37) coupled with the Weather
Research and Forecasting model (38). The simulation for the observed case (“baseline”) is
P3_BG, using a value of 950 cm−3 (130 CCN>50 +
820 UAP<50) for the present-day background
aerosol concentration; the concentration in the
Manaus metropolis (fig. S5B, black box) was set
to be higher by a factor of 3 for both CCN>50 and
UAP<50 to account for the effect of the Manaus
pollution plume (Table 1). The aerosol size distribution (SD) is shown in fig. S5C. The power-law SD
for UAP<50 followed the observed shape in the
city of Manaus (fig. S6C, line 1). For CCN>50 (D >
50 nm), the SD is based on data from a remote
rainforest site known as the Amazon Tall Tower
Observatory (ATTO) in the central Amazon Basin
(39, 40), approximating a clean preindustrial (PI)
condition (C_PI). C_BG is the simulation based
on P3_BG, except with the Manaus pollution plume
removed. To explore the effects of UAP<50, we performed another pair of simulations, C_PI and
PL3_PI, based on C_BG and P3_BG (Table 1), respectively, except with UAP<50 removed (fig. S5C).
Therefore, C_PI represents the PI condition without UAP<50. The vertical distribution (VD) is shown
in fig. S6A, and the details are in (34). Besides the
four main simulations used to demonstrate the
mechanism and isolate the contributions, we also
conducted sensitivity tests (Table 1) with a different
aerosol VD (C_BG_VD and C_PI_VD) and SD
(P3_BG_SD, C_BG_SD, and C_PI_SD) to examine
how the proposed mechanism is affected by variously measured aerosol properties. The sensitivity
test C_PI2 was intended to show that the results
do not change qualitatively even if a small amount
of UAP<50, as observed in the pristine condition
over the Amazon, exists (34).
We evaluated the baseline simulation (P3_BG)
with available observations (figs. S7 to S9) (34).
Overall, the simulation is in good agreement
with the observed case in terms of profiles of
temperature, water vapor, and wind fields (fig.
S7); the precipitation rate and the timing of
peak precipitation (figs. S8 and S9A); and the
echo-top heights of 10 dBZ (fig. S9B). These observational metrics provide confidence in the model performance.
With the sensitivity tests based on the baseline simulation, we found that adding UAP<50
to the PI environment without UAP<50 markedly
invigorates convective intensity (Fig. 3A, C_PI
versus C_BG and PL3_PI versus P3_BG). The
probability density functions of updraft velocity
clearly convey the same point (fig. S10). Therefore, the model replicates results that were observed: UAP<50, which are abundant in the
Manaus pollution plume, lead to enhanced convection around the T3 area. The convective intensity is
more than 50% weaker in the PI environment
(C_PI) when compared to the perturbed environment by the urban plume (P3_BG). As a result,
the peak rain rate is greater by a factor of nearly
2.5 (Fig. 3C). Even under the present-day Manaus
regional background condition that already contains an appreciable amount of UAP<50 (820 cm−3),
further increasing UAP<50 due to the Manaus

Fig. 2. Observed updraft velocity (w) and radar reflectivity (Ze) as aerosol increases. (A) Vertical profiles of updraft velocity averaged over the 90th to 100th percentile of updrafts in each
selected local convective case as a function of Na with D > 15 nm (top) and D > 50 nm (bottom).
Each profile column represents one case. The circles and triangles denote the CAPE and CIN values
of each case, respectively. The aerosol value for each case is listed in table S1. (B) Vertical profiles
of the updraft velocity averaged over the top 10 percentiles of updrafts for each case for the four
aerosol groups from lower to higher Na for D > 15 nm (left) and D > 50 nm (right). The number of
cases for each group is marked. (C) Same as (B) except for reflectivity. Values of (B) and (C) are
the means of the collection of the top 10 percentiles of updrafts from each case. The top 10 percentiles
of the updrafts have 20 to 40 data points for most of the vertical levels and a few for some high
levels in each case. The data have a vertical resolution of ~120 m and time resolution of ~6 s. Shaded
areas in (B) and (C) represent the standard error of the data.

pollution plume by a factor of 3 still drastically
enhances convective intensity (by about 40%)
in the downwind area (P3_BG versus C_BG). The
corresponding increase in the peak precipitation
rate is nearly 100%, much more substantial than
that of convective intensity. This result highlights
the ubiquity of convective invigoration by aerosols

in Amazonia (i.e., in both PI and present-day
Manaus regional background environments) and
the nonlinear response of precipitation rates to
convective intensity. The ubiquity of convective
invigoration by the Manaus pollution plume is
also corroborated by much more frequent strong
updrafts over the entire domain from C_PI to C_BG
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(fig. S11). Another notable feature is that the
timing of the peak rain rate is delayed when
increasing CCN>50, consistent with previous
studies. However, adding UAP<50 to the same CCN
condition does not change the timing, although
it greatly enhances the rain rate resulting from
enhanced convective intensity. The reason for no
change in the timing of rain by UAP<50 is discussed later.
Further analysis shows that the mechanism
responsible for such strongly enhanced convective intensity by UAP<50 is different from the
“cold-phase invigoration” previously proposed (13).
We used comparisons between C_PI and C_BG
to demonstrate our proposed mechanism (similar conclusions were drawn from comparisons
between PL3_PI and P3_BG). The warm- and
deep-cloud periods were examined. The vertical
velocity increase under the deep-cloud period correlates with the increase in total buoyancy (no
correlation with cold pool intensity is seen), although the condensate loading effect partially
offsets the thermal buoyancy (Fig. 4B, bottom).
The thermal buoyancy increase is attributed to
the increased net latent heating (fig. S12A).
Next, we detail how latent heating is increased
and which microphysical processes are the major
contributors. In the PI environment (C_PI), for
the warm-cloud period after warm rain initiates,
the mean Sw for the top 10th percentile of updrafts can be nearly 4% (Fig. 4A, top) because of
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26 January 2018

the efficient warm rain formation due to fast
droplet coalescence resulting from large droplet size, which reduces the integrated droplet
surface area for condensation (Fig. 5A, top). The
addition of UAP<50 (C_BG) brings the Sw down
to 1%, because UAP<50 form an additional number of droplets above the cloud base—a result of
smaller critical aerosol size for activation under
high Sw (Fig. 4A, second from top). These droplets increase the integrated surface area above
the cloud base by more than 40% (Fig. 5A, top)
and therefore promote condensational growth
even at the warm-cloud period (Fig. 4A, third from
top). Note that enhanced condensation leads not
only to the increased cloud water but also to a
rainwater increase under the warm-cloud period
(Fig. 5A, bottom). This result is corroborated by
aircraft measurements of warm clouds during
GoAmazon2014/5, showing larger liquid water
content in the plume-affected clouds relative to
background clouds; enhanced condensation efficiency is part of the explanation (41). Under the
deep-cloud period, the condensation effect is further amplified; the Sw reaches up to 15% in C_PI
because of a lack of droplet surface area for condensation and decreases to 7% in C_BG (Figs. 4B
and 5B, top). This allows aerosols as small as
10 nm to be activated into cloud droplets (Fig. 4B,
second from top). The activation of UAP<50 leads
to a factor of 2 to 4 increase of the integrated
droplet surface area and a doubled condensa-

tional heating rate (Fig. 4B, third from top). The
decrease of supersaturation by UAP<50 is similarly
evident even over the entire convective period
1400–1900 UTC (Fig. 3B).
Because of the very small critical size for droplet nucleation at 2.5- to 3-km altitude, another
droplet nucleation peak appears above the peak
at the cloud base in C_BG; this is attributable
to the presence of a large amount of UAP<50
(Fig. 4B, top, pink solid line). Without UAP<50,
cloud droplet nucleation occurs predominantly
at the cloud base because almost all of the aerosol particles can be activated there (Fig. 4B,
top, pink dashed line). The addition of UAP<50
makes droplet nucleation occur at a large rate
above the cloud base and up to an altitude of
8 km as a result of transport of UAP<50 from low
levels. The enhanced droplet nucleation over the
vertical profile from C_PI to C_BG leads to an increase of up to a factor of 2 to 3 in condensational
heating occurring throughout the lower and middle troposphere. The magnitude of condensational heating in C_BG is nearly twice that of
depositional heating, indicating the predominance
of the condensation process in the release of latent
heat. The increase in depositional heating from
C_PI to C_BG is also much less than that of condensational heating and is only located at 10- to
12-km altitude, whereas condensational heating
is located at low and middle levels and has a
deep layer of increase. The latent heating rates
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Fig. 3. Simulated aerosol effects on the DCCs.
(A and B) Vertical profiles of updraft velocity
w (A) and water supersaturation (B) averaged
over the top 10 percentiles (i.e., 90th to 100th)
for the updrafts with w > 2 m s−1 during
1400–1900 UTC from the convective clouds
around the T3 site (red box in fig. S8).
(C) Time series of mean surface rain rate
averaged over the red box area from
simulations of C_PI (blue dashed curve),
C_BG (blue solid curve), PL3_PI (red
dashed curve), and P3_BG (red solid
curve). The right-side y axis in (A) shows
the temperature profile. The convective
clouds over T3 were chosen for
analysis because they are affected
by the Manaus pollution plume in
P3_BG and evaluated by observations
as shown in figs. S7 to S9. Comparisons
with radar-retrieved rain rates at
2.5-km altitude are shown in figs. S8
and S9A. Shaded areas represent the
standard error of the data.

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Summary and discussion
The retrieved updraft velocity from RWP has
allowed us to directly examine and constrain
aerosol impacts on updraft intensity for DCCs
occurring in a similar dynamic and thermodynamic
environment except for aerosols, revealing the
potentially substantial convective updraft and
precipitation enhancements by UAP<50 from the
Manaus pollution plume. We used real-case threeFan et al., Science 359, 411–418 (2018)

26 January 2018

dimensional simulations to reproduce observed
convective cloud characteristics and to observe
convective updraft and precipitation enhancements
by UAP<50 over the Amazon region. The physical
mechanism for such strong intensification of convection stems from the strong capacity of these
DCCs in activating UAP<50 that usually have a much
higher number concentration than CCN>50, because fast droplet coalescence in pristine condi-

tions decreases integrated droplet surface area for
condensational growth, producing highly supersaturated conditions. The subsequent condensational growth of an additional number of droplets
considerably lowers the water supersaturation,
liberating a large amount of additional latent
heat at the low and middle levels of DCCs and
considerably enhancing updraft strength. This
“warm-phase invigoration” has much stronger

Fig. 4. Analysis of
thermodynamics and
microphysics. (A and
B) Vertical profiles
for the warm cloud (A)
and deep cloud (B)
from C_PI (dashed
curves) and C_BG
(solid curves). From
top to bottom: water
supersaturation; critical diameter for activation; latent heating
from condensation
(blue), deposition
(red), drop freezing
(orange), and riming
(green); and buoyancy
terms. The dashed and
solid pink curves in
the top panel of (B)
show droplet nucleation rates from C_PI
and C_BG, respectively
(upper x axis). The
thermal buoyancy
includes contributions
from both temperature
and water vapor
variations. The values
for the warm cloud are
averaged over the top
10 percentiles (i.e.,
90th to 100th) of the
updrafts with w > 1 m s−1
from a 30-min
duration after the
warm rain starts and
the rain rate exceeds
0.5 mm hour−1 for the
convective clouds in
the red box in fig. S8.
The values for the
deep cloud are averaged over the top
10 percentiles (i.e.,
90th to 100th) of
the updrafts with
w > 2 m s−1 from a
30-min duration with
15 min before and
after the strongest
convection. Therefore,
the specific time for
the warm- and deep-cloud periods varies by simulation because of the time shift of convective clouds
between the simulations. Shaded areas represent the standard error of the data.

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from riming and drop freezing increase considerably from C_PI to C_BG as well. The increases in drop freezing and riming rates result
from the transport of additional droplets from
low levels. Although the increasing rate in latent
heating from drop freezing is the largest, the
actual magnitude is too small (relative to the
other processes) for latent heat to play an important role. The latent heat from riming is also
substantially smaller than that from condensation and deposition.
To understand the relative role of the enhanced condensational heating and latent heating from ice-related processes in intensifying
convection, we conducted three sensitivity tests
based on P3_BG (34). These tests show that enhanced condensational heating is the predominant player in the intensification of convection
and precipitation, while enhanced latent heating
at upper levels plays a secondary role (figs. S13
and S14). This is different from “cold-phase invigoration” (i.e., enhanced ice-related processes),
which is caused by the addition of CCN that
can be activated at the cloud base via suppressing coalescence and delaying warm rain. UAP<50
do not suppress the initial droplet coalescence
and delay the rain, because UAP<50 can only be
activated above the cloud base when rain has already formed and supersaturation is enhanced
by a reduced droplet surface area available for
condensation. Although previous idealized simulations suggested that smaller aerosol particles
can enhance DCC intensity through condensational heating (14), our observations and simulations show that it actually occurs and has
surprisingly large magnitude and importance.
Because of this greatly enhanced convection
associated with UAP<50, the mass mixing ratios
of all hydrometeors are nearly doubled under the
deep-cloud period (Fig. 5B, bottom). The activation of UAP<50 leads to an increase—up to an
order of magnitude—in droplet number concentrations (Fig. 5B, middle), as well as a drastic
increase in ice-phase particle concentrations
resulting from enhanced freezing including
riming. All ice-phase particle number concentrations (ice, snow, and graupel) increase by a
factor of ~2 generally, but the increase of the
mass mixing ratio of ice-phase particles is largely
driven by graupel, with snow mass reduced (fig.
S12B, left), particularly in mixed-phase regions
(6 to 10 km), resulting from a larger amount of
supercooled liquid droplets in the cases of UAP<50.
Falling graupel particles contribute to warmphase increases in rain mass under the deep-cloud
period (Fig. 5B, bottom), thereby contributing to
increased surface precipitation as in Fig. 3C.

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affect the convective invigoration beeffects than the “cold-phase invigolow it. However, the new VD causes
ration” previously proposed (13).
stronger convective invigoration at
UAP<50 increase the rainwater amount
upper levels because of higher aeromainly through enhanced accretion
sol concentrations. Another uncerof added cloud droplets and added
tainty results from the aerosol SD
graupel melting. Added UAP<50 do
assumed in the simulation. We also
not affect the timing of precipitation
carried out sensitivity tests by using
because UAP<50 can be activated to
a SD measured by a fast integrated
form additional cloud droplets only
mobility spectrometer [FIMS (47)]
after warm rain begins. In contrast,
on aircraft at ~20 km downwind
CCN>50 suppress and delay warm
from Manaus (34). The results were
rain and then delay peak precipitation.
similar to the previous simulations
The large magnitude of the conwith a different SD (fig. S15, C and
densation effects is likely a result of
D). Thus, we infer that any aerosol
the very warm, moist, and clean conscenario that contains a large ratio
ditions in the Amazon wet season.
of UAP<50 to CCN>50 in a humid enThis leads to a mechanism different
vironment would produce the UAP<50
from that associated with dry-season
effects we report in this study.
events over the Amazon [i.e., coldBecause all UAP<50 are removed
phase invigoration (13, 22–24)] or
in our simulations for examining the
other dryer or colder locations [e.g.,
UAP<50 effects, and because small
(11, 42, 43)]. A key factor is the amnumbers of UAP<50 are observed at
ple cloud droplet coalescence in the
the remote rainforest site in the centropical rainforest environment that
tral Amazon Basin [e.g., figure 6A
is not affected by human activities.
of (39)], we conducted an additionThis coalescence rapidly removes
al sensitivity test, C_PI2, in which
droplets; droplet removal leads to
UAP<50 (60 cm−3) were added to
suppressed condensation, leaving a
C_PI (Table 1) (34). We found that
high supersaturation for activating
the differences in updraft velocity
UAP<50. This is corroborated by sensitivity tests with droplet collisionand supersaturation between C_PI
coalescence processes shut off for
and C_PI2 are very small (fig. S16,
both background (C_BG) and plumeA and B). Thus, our conclusion and
affected (P3_BG) cases, which show
the proposed mechanism are not
small differences in water superaffected by the small amount of
saturation between the two cases,
UAP<50 in the PI condition. This
indicates that we should expect
in contrast to the large differences
similarly large UAP<50 effects for
when droplet collision-coalescence
pristine regions with low UAP<50
processes occur.
concentration. The intrusion of urIt has been believed that there is
ban areas such as the Manaus mea cutoff size—typically larger than
tropolis produces a large amount
60 nm (20)—for aerosol particles to
of UAP<50, which may drastically
be able to serve as CCN for a specific Fig. 5. Additional microphysical properties. (A and B) Analogous to
region or typical aerosol type (18). Fig. 4, showing vertical profiles for droplet surface area (top), droplet number change convective and precipitation
conditions over the Amazon Basin
Generally, UAP<50 are not considered (middle), and mass mixing ratios (bottom) for cloud droplets (blue), rain
to contribute to CCN because their drops (red), and ice-phase particles (green). The ice-phase particles include
by producing stronger convective
small sizes require high supersat- cloud ice, snow, and graupel. Shaded areas represent the standard error
clouds and precipitation.
uration. Our findings show that un- of the data.
All of our tests with different aeroder the present-day Manaus regional
sol properties (SD and VD) indicate
background, approximated PI, and
that observed and simulated UAP<50
effects and the proposed mechanism are robust.
present-day pristine environments over the Amazon
of UAP<50 through various mechanisms (44, 45);
Given the importance of the Amazon Basin in
region, UAP<50 can effectively serve as CCN for
for instance, interactions of anthropogenic sulfur
droplet nucleation well above the cloud base after
global circulation and hydrology, the ultrafine
and NOx with biogenic sources such as organic
coalescence starts. DCCs in the natural rainforest
pollution particles introduced by human activacids from aromatics enhance aerosol nucleation.
environment over the Amazon, where fine parities could have profound effects on other places
A recent study showed that aerosol concentraticles are scarce, may be especially susceptible to
around the globe. A lack of aerosol nucleation (or
tions over the Amazon peak at the upper tropothe addition of UAP<50, as shown by this work. The
a low concentration of ultrafine particles) and the
sphere (46), unlike the VD assumed in our model
dearth of new particle formation in the Amazon
existence of ample moisture may not be unique to
simulations. We conducted sensitivity tests (34)
boundary layer under natural conditions might
the Amazon Basin and may also occur in other
by using a VD similar to that in figure 7B of (46).
be a result of low sulfuric acid concentration;
tropical forests and oceans, further highlighting
Similar convective invigoration by increasing
another possible reason is that the products of
the global importance of our results. For example,
UAP<50 through the same mechanism is seen
gas-phase oxidation of isoprene do not facilitate
a recent study found a conspicuous enhancement
(fig. S15, A and B). The new VD does not affect
new particle formation as efficiently as those from
of lightning over shipping lanes in the equatorial
the conclusion because the maximum superoxidation of other biogenic volatile organic comIndian Ocean (48). We conclude that UAP<50 can
saturation peaks at an altitude of 6 km and the
modulate convection and rain-forming processes
pounds (e.g., monoterpenes). However, anthrolatent heating at low levels is the major conmore strongly than previously considered, espepogenic emissions in this region may induce
tributor to convection intensification, so the addicially in pristine regions. Therefore, the changes
aerosol nucleation and produce a large amount
tion of UAP<50 at upper levels would not greatly

R ES E A RC H | R E S EA R C H A R T I C LE

Table 1. Model simulations. Abbreviations: BG, present-day background aerosol condition in the Manaus region; PI, preindustrial aerosol condition; P3,
factor of 3 increase in Na for plume; C, clean condition (i.e., no plume); L in PL3, large aerosol particles (i.e., D > 50 nm). Peaked refers to a measured
size distribution that has a peak value over a size range; upper-level peak refers to a measured vertical distribution with aerosols peaked at upper levels (34).
N/A means that no plume is applied (i.e., aerosols are horizontally uniform over the domain at the model initial time).

Simulation

Manaus regional
background with
pollution plume
Manaus regional

Acronym

P3_BG

Size

Vertical

Hygroscopicity

Na of
domain (per

distribution

(k)

cubic centimeter)

Power law +
peaked

Exponential
decrease

0.12

Na of
Manaus pollution
plume (per
cubic centimeter)

CCN>50

UAP<50

CCN>50

UAP<50

130

820

390

2460

............................................................................................................................................................................................................................................................................................................................................

C_BG

Power law +

Exponential

0.12

130

820

N/A

background
peaked
decrease
............................................................................................................................................................................................................................................................................................................................................
PI condition without
C_PI
Peaked
Exponential
0.12
130
0
N/A
N/A
UAP
Sensitivity test for P3_BG

decrease
Exponential

0.12

Power law +

decrease
Upper-level peak

0.12

peaked
Peaked

Upper-level peak

<50
............................................................................................................................................................................................................................................................................................................................................

Peaked

130

390

based on C_BG
Sensitivity test for VD

C_BG_VD

130

820

N/A

C_PI_VD

0.12

130

N/A

based on C_PI
............................................................................................................................................................................................................................................................................................................................................
Sensitivity test for SD
P3_BG_SD
Peaked
Exponential
0.12
450
3350
1350
10,050
based on P3_BG
Sensitivity test for SD

decrease
Exponential

0.12

decrease
Exponential

0.12

decrease
Exponential

0.12

based on C_BG
Sensitivity test for SD

C_BG_SD

Peaked

450

3350

N/A

based on C_PI
PI condition with

C_PI_SD

Peaked

450

N/A

C_PI2

Peaked

130

N/A

UAP<50
decrease
............................................................................................................................................................................................................................................................................................................................................
Sensitivity test for C_PI
C_PI_ATTO
Peaked
Exponential
0.12
200
0
N/A
N/A
with mean ATTO
Sensitivity test for C_BG with

decrease
Exponential

CCN

C_BG_ATTO

from mean ATTO

Peaked

0.12

200

820

N/A

decrease

>50
............................................................................................................................................................................................................................................................................................................................................

of ultrafine pollution particles from preindustrial
conditions to the present may possibly have appreciably changed deep convective clouds.
RE FE RENCES AND N OT ES

1. R. A. Houze Jr., Cloud Dynamics (Elsevier/Academic
Press, ed. 2, 2014).
2. H. C. Barnes, R. A. Houze Jr., J. Geophys. Res. Atmos. 121,
8269–8296 (2016).
3. Climate Change 2013: The Physical Science Basis. Contribution
of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change (Cambridge Univ.
Press, 2013).
4. J. Fan, Y. Wang, D. Rosenfeld, X. Liu, J. Atmos. Sci. 73,
4221–4252 (2016).
5. W. K. Tao, J. P. Chen, Z. Li, C. Wang, C. Zhang, Rev. Geophys.
50, RG2001 (2012).
6. A. P. Khain, Environ. Res. Lett. 4, 015004 (2009).
7. S. S. Lee, L. J. Donner, V. T. J. Phillips, Y. Ming, J. Geophys.
Res. 113, D16202 (2008).
8. Z. J. Lebo, J. H. Seinfeld, Atmos. Chem. Phys. 11, 5407–5429 (2011).
9. R. L. Storer, S. C. van den Heever, J. Atmos. Sci. 70, 430–446
(2013).
10. S. C. van den Heever, G. L. Stephens, N. B. Wood, J. Atmos.
Sci. 68, 699–718 (2011).
11. Z. Li et al., Nat. Geosci. 4, 888–894 (2011).
12. X. Yang, Z. Li, J. Geophys. Res. Atmos. 119, 1835–1844 (2014).
13. D. Rosenfeld et al., Science 321, 1309–1313 (2008).
14. A. P. Khain, V. Phillips, N. Benmoshe, A. Pokrovsky, J. Atmos.
Sci. 69, 2787–2807 (2012).

Fan et al., Science 359, 411–418 (2018)

26 January 2018

15. Q. Chen et al., Atmos. Chem. Phys. 17, 9585–9598 (2017).
16. J. Fan et al., Proc. Natl. Acad. Sci. U.S.A. 110, E4581–E4590
(2013).
17. U. Pöschl et al., Science 329, 1513–1516 (2010).
18. M. O. Andreae, Science 315, 50–51 (2007).
19. S. T. Martin et al., Atmos. Chem. Phys. 10, 11415–11438 (2010).
20. U. Lohmann, J. Feichter, Atmos. Chem. Phys. 5, 715–737 (2005).
21. I. Koren, G. Dagan, O. Altaratz, Science 344, 1143–1146 (2014).
22. M. O. Andreae et al., Science 303, 1337–1342 (2004).
23. I. Koren, J. V. Martins, L. A. Remer, H. Afargan, Science 321,
946–949 (2008).
24. J. C. Lin, T. Matsui, R. A. Pielke Sr., C. Kummerow, J. Geophys.
Res. 111, D19204 (2006).
25. J. C. Zhou, E. Swietlicki, H. C. Hansson, P. Artaxo, J. Geophys.
Res. 107, 8055 (2002).
26. M. Kulmala et al., Atmos. Chem. Phys. 11, 13061–13143 (2011).
27. J. Wang et al., Nature 539, 416–419 (2016).
28. M. Pikridas et al., Atmos. Chem. Phys. 15, 10219–10237 (2015).
29. S. T. Martin et al., Bull. Am. Meteorol. Soc. 98, 981–997 (2017).
30. S. T. Martin et al., Atmos. Chem. Phys. 16, 4785–4797 (2016).
31. S. E. Giangrande et al., J. Appl. Meteorol. Climatol. 52,
2278–2295 (2013).
32. S. E. Giangrande et al., J. Geophys. Res. Atmos. 121,
12891–12913 (2016).
33. S. Tang et al., Atmos. Chem. Phys. 16, 14249–14264 (2016).
34. See supplementary materials.
35. J. A. Marengo et al., Atmos. Chem. Phys. Discuss. 10.5194/
acp-2017-22 (2017).
36. A. P. Khain, A. Pokrovsky, M. Pinsky, A. Seifert, V. Phillips,
J. Atmos. Sci. 61, 2963–2982 (2004).
37. J. Fan et al., J. Geophys. Res. 117, D00K36 (2012).

38. W. C. Skamarock et al., A Description of the Advanced Research
WRF Version 3 (National Center for Atmospheric Research, 2008).
39. M. L. Pöhlker et al., Atmos. Chem. Phys. Discuss. 10.5194/
acp-2017-847 (2017).
40. M. L. Pöhlker et al., Atmos. Chem. Phys. 16, 15709–15740 (2016).
41. M. A. Cecchini et al., Atmos. Chem. Phys. 16, 7029–7041 (2016).
42. R. Zhang, G. Li, J. Fan, D. L. Wu, M. J. Molina, Proc. Natl. Acad.
Sci. U.S.A. 104, 5295–5299 (2007).
43. Y. Wang et al., Proc. Natl. Acad. Sci. U.S.A. 111, 6894–6899 (2014).
44. R. Zhang, A. Khalizov, L. Wang, M. Hu, W. Xu, Chem. Rev. 112,
1957–2011 (2012).
45. M. Shrivastava et al., Rev. Geophys. 55, 509–559 (2017).
46. M. O. Andreae et al., Atmos. Chem. Phys. Discuss. 10.5194/
acp-2017-694 (2017).
47. J. Wang, M. Pikridas, S. R. Spielman, T. Pinterich, J. Aerosol
Sci. 108, 44–55 (2017).
48. J. A. Thornton, K. S. Virts, R. H. Holzworth, T. P. Mitchell,
Geophys. Res. Lett. 44, 9102–9111 (2017).
AC KNOWLED GME NTS

This study was supported by the U.S. DOE, Office of Science,
Atmospheric System Research Program. The Pacific Northwest
National Laboratory (PNNL) is operated for DOE by Battelle
Memorial Institute under contract DE-AC06-76RLO1830. This
research used PNNL Institutional Computing resources. Y.Z. and
Z.L. were supported by NSF grant AGS1534670 and National
Science Foundation of China grant 91544217. D.R. was
supported by project BACCHUS European Commission
FP7-603445. S.E.G. represents Brookhaven Science Associates
LLC under DOE contract DE-SC0012704. The DOE Atmospheric
Radiation Measurement (ARM) Climate Research Facility’s

7 of 8

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without UAP
Sensitivity test for VD

PL3_PI

R ES E A RC H | R E S EA R C H A R T I C LE

GoAmazon field campaign data were used. The x-band and s-band
(SIPAM) radar data were supported by the CHUVA project. We
thank the GoAmazon team and the CHUVA team for their effort to
produce the observational data. We acknowledge support from
the Central Office of the Large Scale Biosphere Atmosphere
Experiment in Amazonia (LBA), Instituto Nacional de Pesquisas da
Amazonia (INPA), Universidade do Estado do Amazonas (UEA),
and the local Research Foundation (FAPEAM). L.A.T.M., P.A., and
H.M.J.B. were supported by FAPESP grants 2009/15235-8,
2013/05014-0, and 2013/50510-5. The work was conducted under
authorization 001030/2012-4 of the Brazilian National Council
for Scientific and Technological Development (CNPq). For
the operation of the ATTO site, we acknowledge support by the
German Federal Ministry of Education and Research (BMBF

contract 01LB1001A), the Brazilian Ministério da Ciência,
Tecnologia e Inovação (MCTI/FINEP contract 01.11.01248.00), and
the Amazon State University (UEA), FAPEAM, LBA/INPA, and
SDS/CEUC/RDS-Uatumã. We thank C. Schumacher and A. Funk
at Texas A&M University for the SIPAM data, T. Biscaro for the
x-band data, S. Tang at Lawrence Livermore National Laboratory
for the input of local convective system selection, S. Hagos at
PNNL for the input of model configuration, and C. Kuang at
Brookhaven National Laboratory for help in understanding the
uncertainty of Scanning Mobility Particle Sizer data. The
observational data including x-band and SIPAM radar data from
CHUVA can be obtained from DOE ARM data archive www.archive.
arm.gov/discovery/#v/results/s/fiop::amf2014goamazon, which
is available to the community. The model simulation data are

archived at PNNL PIC and are available at https://dtn2.pnl.gov/
data/jiwen/GoAmazon_simulations_sci/.
SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/359/6374/411/suppl/DC1
Materials and Methods
Supplementary Text
Figs. S1 to S17
Table S1
References (49–53)
26 May 2017; accepted 22 December 2017
10.1126/science.aan8461

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Fan et al., Science 359, 411–418 (2018)

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8 of 8

Substantial convection and precipitation enhancements by ultrafine aerosol particles
Jiwen Fan, Daniel Rosenfeld, Yuwei Zhang, Scott E. Giangrande, Zhanqing Li, Luiz A. T. Machado, Scot T. Martin, Yan Yang,
Jian Wang, Paulo Artaxo, Henrique M. J. Barbosa, Ramon C. Braga, Jennifer M. Comstock, Zhe Feng, Wenhua Gao, Helber
B. Gomes, Fan Mei, Christopher Pöhlker, Mira L. Pöhlker, Ulrich Pöschl and Rodrigo A. F. de Souza

Science 359 (6374), 411-418.
DOI: 10.1126/science.aan8461

ARTICLE TOOLS

http://science.sciencemag.org/content/359/6374/411

SUPPLEMENTARY
MATERIALS

http://science.sciencemag.org/content/suppl/2018/01/24/359.6374.411.DC1

REFERENCES

This article cites 49 articles, 9 of which you can access for free
http://science.sciencemag.org/content/359/6374/411#BIBL

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Up with ultrafine aerosol particles
Ultrafine aerosol particles (smaller than 50 nanometers in diameter) have been thought to be too small to affect
cloud formation. Fan et al. show that this is not the case. They studied the effect of urban pollution transported into the
otherwise nearly pristine atmosphere of the Amazon. Condensational growth of water droplets around the tiny particles
releases latent heat, thereby intensifying atmospheric convection. Thus, anthropogenic ultrafine aerosol particles may
exert a more important influence on cloud formation processes than previously believed.
Science, this issue p. 411

Meteorologia