ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-14433-2017Further evidence for CCN aerosol concentrations determining the height of
warm rain and ice initiation in convective clouds over the Amazon basinBragaRamon Camposramonbraga87@gmail.comRosenfeldDanielWeigelRalfhttps://orcid.org/0000-0003-1316-0292JurkatTinaAndreaeMeinrat O.https://orcid.org/0000-0003-1968-7925WendischManfredhttps://orcid.org/0000-0002-4652-5561PöschlUlrichhttps://orcid.org/0000-0003-1412-3557VoigtChristianehttps://orcid.org/0000-0001-8925-7731MahnkeChristophhttps://orcid.org/0000-0003-2606-1680BorrmannStephanhttps://orcid.org/0000-0002-4774-9380AlbrechtRachel I.https://orcid.org/0000-0003-0582-6568MollekerSergejhttps://orcid.org/0000-0002-2980-0330VilaDaniel A.https://orcid.org/0000-0002-1015-5650MachadoLuiz A. T.https://orcid.org/0000-0002-8243-1706GrulichLucasCentro de Previsão de Tempo e Estudos Climáticos, Instituto
Nacional de Pesquisas Espaciais, Cachoeira Paulista, BrasilInstitute of Earth Sciences, The Hebrew University of Jerusalem,
IsraelInstitut für Physik der Atmosphäre, Johannes
Gutenberg-Universität, Mainz, GermanyInstitut für Physik der Atmosphäre, Deutsches Zentrum für
Luft- und Raumfahrt (DLR), Oberpfaffenhofen, GermanyMultiphase Chemistry and Biogeochemistry Departments, Max Planck
Institute for Chemistry, 55020 Mainz, GermanyLeipziger Institut für Meteorologie (LIM), Universität
Leipzig, Stephanstr. 3, 04103 Leipzig, GermanyInstituto de Astronomia, Geofísica e Ciências
Atmosféricas, Universidade de São Paulo, Sao Paulo, BrazilParticle Chemistry Department, Max Planck Institute for Chemistry,
55020 Mainz, GermanyScripps Institution of Oceanography, University of California San
Diego, La Jolla, CA 92098, USAInstitut für Informatik, Johannes Gutenberg-Universität,
Mainz, GermanyRamon Campos Braga (ramonbraga87@gmail.com)5December20171723144331445622December20169February201727October20173November2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/14433/2017/acp-17-14433-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/14433/2017/acp-17-14433-2017.pdf
We have investigated how aerosols affect the height above
cloud base of rain and ice hydrometeor initiation and the subsequent vertical
evolution of cloud droplet size and number concentrations in growing
convective cumulus. For this purpose we used in situ data of hydrometeor size
distributions measured with instruments mounted on HALO aircraft during the
ACRIDICON–CHUVA campaign over the Amazon during September 2014. The results
show that the height of rain initiation by collision and coalescence
processes (Dr, in units of meters above cloud base) is linearly
correlated with the number concentration of droplets (Nd in
cm-3) nucleated at cloud base (Dr≈5⋅Nd). Additional cloud processes associated with Dr, such as GCCN,
cloud, and mixing with ambient air and other processes, produce deviations of
∼ 21 % in the linear relationship, but it does not mask the
clear relationship between Dr and Nd, which was also found at different
regions around the globe (e.g., Israel and India). When Nd exceeded
values of about 1000 cm-3, Dr became greater than 5000 m, and the
first observed precipitation particles were ice hydrometeors. Therefore, no
liquid water raindrops were observed within growing convective cumulus during
polluted conditions. Furthermore, the formation of ice particles also took
place at higher altitudes in the clouds in polluted conditions because the
resulting smaller cloud droplets froze at colder temperatures compared to the
larger drops in the unpolluted cases. The measured vertical profiles of
droplet effective radius (re) were close to those estimated by assuming
adiabatic conditions (rea), supporting the hypothesis that the
entrainment and mixing of air into convective clouds is nearly inhomogeneous.
Additional CCN activation on aerosol particles from biomass burning and air
pollution reduced re below rea, which further inhibited the
formation of raindrops and ice particles and resulted in even higher
altitudes for rain and ice initiation.
Introduction
Understanding cloud and precipitation forming processes and their impacts on
the global energy budget and water cycle is crucial for meteorological
modeling. Therefore, many studies have focused on improving cloud
parameterization in numerical weather and climate models
(e.g.,
Frey et al., 2011; Khain et al., 2005, 2000; Klein et al., 2009; Lee et al.,
2007; Machado et al., 2014).
Cloud droplets form when humid air rises and becomes supersaturated with
respect to water. Then water vapor condenses onto surfaces provided by
preexisting cloud condensation nuclei (CCN; a list of abbreviations and
symbols is given in Table 1) aerosols. For ice formation, the ambient
temperatures must reach values lower than 0 ∘C. At temperatures
between 0 and -36 ∘C, ice in convective
clouds mostly forms inhomogeneously on ice nuclei (IN) aerosols, often when
they interact with supercooled liquid water droplets
(Pruppacher et al., 1998). Ice multiplication is
an important mechanism that masks the primary ice nucleation activity when
cloud droplets are sufficiently large to also promote warm rain by
coalescence at temperatures of -3 to -8 ∘C (Hallet and
Mossop, 1974). At much colder temperatures (less than -37 ∘C), cloud particles freeze due to homogeneous ice nucleation
(Rosenfeld and Woodley, 2000).
List of abbreviations and symbols.
Abbreviation/notationDescriptionUnitsACRIDICON–CHUVA Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM [Global Precipitation Measurements])–CAS-DPOLCloud and Aerosol Spectrometer–CbhCloud base heightmCCP–CDPCloud Combination Probe–Cloud Droplet Probe–CCP–CIPCloud Combination Probe–Cloud Imaging Probe–CCNCloud condensation nucleicm-3CWCCloud water contentg m-3CWCaAdiabatic cloud water contentg m-3DcCloud depth – distance from cloud basemDrCloud depth at which first drizzle with drop shape was detectedmDr-1Nearest cloud depth below Dr without raindropmDiCloud depth where first drizzle with ice shape was detectedmDi-1Nearest cloud depth below Di without ice particlesmDWCDrizzle water contentg m-3DSDCloud droplet size distributioncm-3µm-1D13Cloud depth at which rea= 13 µmmINIce Nucleicm-3KThe collection kernel of a pair of dropletscm-3 s-1LWCLiquid water contentg m-3MPWCMixed-phase water contentg m-3MvMean volume cloud dropletµm-3MvaAdiabatic mean volume cloud dropletµm-3NaAdiabatic number concentration of dropletscm-3NdNumber concentration of dropletscm-3Nd∗Effective number of droplets concentration at cloud basecm-3NLSNumber of altitude levels sampled–PCASPPassive Cavity Aerosol Spectrometer Probe–PSDAerosol particle size distributioncm-3µm-1reThe effective radius of the cloud droplet spectraµmreaThe adiabatic effective radius of the cloud droplet spectraµmrvThe mean volume radius of the cloud dropletsµmRWCRainwater contentg m-3SSupersaturation%TTemperature∘CTrTemperature of rain initiation∘CTiTemperature of ice initiation∘CTi-1Nearest temperature greater than Ti without ice particles∘CWVertical velocitym s-1WmaxMaximum vertical velocity during the cloud profiling flightm s-1
A cloud predominantly consists of droplets with diameters larger than about
3 µm, except for transient smaller sizes right at cloud base. The
number concentration of cloud droplets (Nd in cm-3) at cloud base
mainly depends on the conditions below cloud base, i.e., the updraft wind
speed (W) and the supersaturation (S) activation spectra of cloud condensation
nuclei [CCN(S)] (Twomey, 1959). In very
clean conditions, values of Nd near cloud base are in the range of
∼ 50–100 cm-3, while in polluted conditions Nd may
reach values between 1000 and 2000 cm-3
(Andreae, 2009;
Rosenfeld et al., 2008, 2014a).
Below the freezing level, raindrops are formed due to cloud droplet
coagulation (collision–coalescence) processes (warm rain process). Mixed-phase precipitation results from interactions between ice particles and
liquid water droplets (Pruppacher et al., 1998).
Several studies based on aircraft, radar, and satellite measurements support
the idea
that warm rain formation requires that the cloud consist of droplets with
values of the effective radius (re) larger than 13–14 µm
(Freud
and Rosenfeld, 2012; Konwar et al., 2012; Prabha et al., 2011; Chen et al.,
2008; VanZanten et al., 2005; Pinsky and Khain, 2002; Gerber, 1996;
Rosenfeld and Gutman, 1994).
The effects of aerosol particles on clouds and precipitation have been
studied in different parts of the globe
(e.g.,
Fan et al., 2014; Li et al., 2011; Ramanathan et al., 2001; Rosenfeld and
Woodley, 2000; Rosenfeld et al., 2014a; Tao et al., 2012; Voigt et al.,
2017; Wendisch et al., 2016). A particularly interesting region is the
Amazon basin, which presents contrasting environments of aerosol particle
concentration between dry and wet seasons and steep aerosol
concentration gradients within regions with near-constant thermodynamic
conditions
(Andreae
et al., 2004; Artaxo et al., 2013). The background number concentrations of
aerosol particles and CCN over the pristine parts of the Amazon region are
about a factor of 10 lower than those of polluted continental regions,
including polluted conditions over the Amazon (Martin et al., 2016). During
the dry-to-wet transition season in the Amazon region, total aerosol number
concentrations reach values up to 10 000 cm-3, mostly due to
forest fires
(Andreae,
2009; Andreae et al., 2012; Artaxo et al., 2002). On the other hand, in the
rainy season aerosol number concentrations are about 500–1000 cm-3 with
CCN concentrations on the order of 200–300 cm-3 for 1 %
supersaturation, mainly consisting of forest biogenic aerosol particles
(Artaxo,
2002; Martin et al., 2016; Pöhlker et al., 2016; Pöschl et al.,
2010). Additionally, the city of Manaus, which is located at the central Amazon
basin, releases significant concentrations of urban pollution aerosol
particles (e.g., due to traffic, combustion-derived particles, or different
types of industrial activities). This increases CCN concentrations by up to
1 order of magnitude (for 0.6 % supersaturation) from the wet (Green
Ocean) to the dry season
(Kuhn et al.,
2010).
Rosenfeld et al. (2012b) showed that by estimating the adiabatic number of
droplets nucleated at cloud base (Na), the height above cloud base at
which the first raindrops evolve can be parameterized. This approach is
based on the assumption that the entrainment and mixing of air into
convective clouds is almost completely inhomogeneous
(Beals et al., 2015; Burnet and
Brenguier, 2007; Freud et al., 2011; Paluch, 1979). The inhomogeneous mixing
occurs when the evaporation rate of cloud droplets significantly exceeds the
mixing rate of the cloud with ambient air. This causes the droplets that are
at the boundary of the entrained air filament to evaporate completely and
moisten that air until it is saturated. Further mixing of the saturated
entrained air would not cause additional evaporation, but only decreases
Nd and LWC while maintaining re of the remaining droplets
as unaffected. This implies that the vertical profile of the actual cloud
droplet effective radius behaves nearly as in an idealized adiabatic cloud.
This uniquely connects the adiabatic drop number concentration, which is
approximated by Na at cloud base, with the adiabatic droplet effective
radius (rea) based on an adiabatic parcel model for which droplet
growth is dominated by condensation
(Freud
and Rosenfeld, 2012; Pinsky and Khain, 2002). This parameterization can be
applied to estimate the height above cloud base at which raindrops start to
form, when rea reaches 13 µm (D13) (Freud and Rosenfeld, 2012;
Konwar et
al., 2012; Rosenfeld et al., 2012b; Prabha et al., 2011; VanZanten et al.,
2005). However, uncertainties associated with the calculated Na decrease
the agreement between rea and re. Most of these uncertainties arise
when additional CCN activation of droplets happens above cloud base because
the adiabatic model does not predict that Nd will increase with height, but
will decrease due to evaporation and deviations from inhomogeneous cloud mixing
(Pinsky and Khain, 2012).
Braga et al. (2017) applied the methodology described by Freud et al. (2011)
to calculate Na at the base of growing convective cumulus clouds for the
Amazon region during the ACRIDICON–CHUVA campaign (Aerosol, Cloud, Precipitation, and
Radiation Interactions and Dynamics of Convective Cloud Systems –
Cloud processes of tHe main precipitation systems in Brazil: A contribUtion
to cloud resolVing modeling and to the GPM [Global Precipitation
Measurements]; Wendisch et al., 2016).
The Na is calculated from Na= CWCa/Mva,
where CWCa is the
adiabatic cloud water content (CWCa) as calculated from cloud base
pressure and temperature, and Mva is the adiabatic mean volume droplet
mass as approximated from the actually measured mean volume droplet mass
(Mv) by the cloud probe DSDs obtained during the cloud profiling
measurements. Measurements of Mv with height are considered only for
cloud passes for which CWC is greater than 25 % of the adiabatic CWC and
re is lower than 11 µm (i.e., for cloud droplets that have grown
mostly via condensation). The calculated Na values based on the measured
vertical profile of re agreed well (within 20–30 %) with the actual
measurements of cloud droplet number concentrations at cloud base. This
approach provides the opportunity to test the agreement between estimated
rea and the height above cloud base of warm rain initiation (Dr)
within clouds for the Amazon region. In addition, measurements of the height
above cloud base of ice initiation (Di) in convective clouds are also
available from flights that include cloud penetrations at ambient
temperatures as low as -60 ∘C with the HALO aircraft
(Wendisch et al., 2016).
This study analyzes the vertical development of cloud and precipitation
particles (water drops and ice crystals) in growing convective cumulus over
the Amazon based on measurements of cloud microphysical properties from
instruments mounted on HALO during ACRIDICON–CHUVA
(Wendisch et al., 2016). The vertical profile of
rea is used to estimate the depth above cloud base at which warm rain
initiation occurs. The dominance of inhomogeneous mixing causes the
re profile to behave almost as in adiabatic clouds, constrained by
Nd at cloud base (Burnet and Brenguier, 2007; Freud et al., 2011). This
means that the height above cloud base for reaching re of 13–14 µm,
which is required for rain initiation, is also determined by cloud base
Nd (Freud and Rosenfeld, 2012). Rain initiation depends strongly on
re because the rain production rate by collision and coalescence is
proportional to ∼re5 (Freud and Rosenfeld, 2012).
Here we test and quantify these relationships for the measurements conducted
with HALO aircraft during ACRIDICON–CHUVA.
The HALO flights during the ACRIDICON–CHUVA campaign were performed over the
Amazon region under various conditions of aerosol concentrations and land
cover (Wendisch et al., 2016). Figure 1a shows the flight tracks during
which cloud profile sampling in growing convective cumulus was performed.
Figure 1b shows a schematic sketch of the flight pattern while sampling
cloud clusters (the locations in three dimensions of each flight are
available at Fig. S1 in the Supplement). The aircraft obtained a
composite vertical profile by penetrating young and rising convective
elements, typically some 100–300 m below their tops.
(a) HALO flight tracks during the ACRIDICON–CHUVA
experiment. The flight number is indicated at the bottom by
colors; (b) flight patterns below and in convective clouds during
the ACRIDICON–CHUVA campaign.
Description of cloud probes, size range intervals, and hydrometeor
shapes observed on CCP–CIP images used to calculate CWC, DWC, RWC, and MPWC.
Abbreviation/notationInstrumentSize rangeHydrometeor shapesCWCCCP–CDP/CAS-DPOL3–50 µmCloud dropletsDWCCCP–CIP75–250 µmCloud droplets and raindropsRWCCCP–CIP250–960 µmCloud droplets and raindropsMPWCCCP–CIP75–960 µmCloud droplets and ice particles
The cloud droplet size distributions (DSDs) between 3 and 50 µm in diameter
were measured at a temporal resolution of 1 s by the CAS-DPOL and
CCP–CDP probes
(Baumgardner
et al., 2001; Lance et al., 2010; Brenguier et al., 2013). Each DSD spectrum
represents 1 s of flight path (covering ∼ 150 m of horizontal
distance for a typical aircraft speed). The value of re was calculated
for each 1 s DSD. The two probes (CAS-DPOL and CCP–CDP) were mounted on
opposite wings of HALO (horizontal distance of ∼ 15 m).
Similar values of Nd and derived re were measured by CAS-DPOL and
CCP–CDP (they agree within 30 %), even though they were mounted on
different wings. A previous study (Braga et al., 2017) showed that both
probes were in agreement within the measurement uncertainties with respect
to the measured cloud droplet number concentrations at cloud base and in
accordance with the expected values for different conditions of CCN
concentration and updraft wind speed below cloud base. In addition, the CWC
calculated from the measured DSDs shows similar values to those measured
with a hot-wire device for different heights above cloud base (the probes'
measurements agree within their uncertainty range of 16 % for probe DSDs and
30 % for the hot-wire device; Braga et al., 2017).
The determination of the height of rain initiation is based on the drizzle
water content (DWC) calculation from the CCP–CIP probe
(Brenguier et al., 2013). The DWC is defined as the
mass of the drops integrated over the diameter range of 75–250 µm
(Freud and Rosenfeld,
2012). This size range is selected because it includes only drops with
a terminal fall speed of 1 m s-1 or less, which maximizes the chance that
the drizzle was formed in situ and did not fall a large distance from above.
Rainwater content (RWC) is defined as the CCP–CIP integrated liquid water
mass of droplets with diameters between 250 and 960 µm. The CCP–CIP
images were used to distinguish raindrops and ice particles during cloud
passes. The hydrometeor type is identified visually by shape. The
phase of the smaller CCP–CIP particles cannot be distinguished. Therefore,
the precipitation is considered as mixed phase when ice particles are
identified, and the combined DWC and RWC are redefined as mixed-phase water
content (MPWC). Table 2 summarizes the calculated cloud microphysical
properties with respect to the instrumentation used and its size ranges.
The scientific motivation
The aircraft-based in situ measurements of cloud properties were collected within
convective clouds formed over the Amazon from cloud base up to cloud top
above the glaciated level. These measurements provided a unique opportunity
to evaluate previous theoretical knowledge about aerosol impacts on
convective clouds characteristics over the Amazon. In this study the impact
of Na (adiabatic cloud drop concentrations) in determining the
initiation of rain and ice within convective clouds is evaluated. This is
performed through the analysis of the calculated Na, Dr, and
Di for several different environmental conditions over the Amazon (cloud
base updrafts, aerosol concentration, surface cover). The relationship
of Na and Dr was previously analyzed for Israel and
India; a linear relationship was found (Dr≈4⋅Na; Freud and Rosenfeld, 2012). For the Amazon region a similar analysis is
performed here also taking into account the impact of Na on Di. This
is the first study that analyzes the impact of Na on Dr and
Di on the Amazon region using in situ measurements of convective cloud properties.
The results obtained from comparisons of Na estimates and the measured
effective number of droplets nucleated at cloud base (Nd∗), shown at
Braga et al. (2017) for the same flights in the Amazon region, support the
methodology of deriving Na based on the rate of re growth with cloud
depth under the assumption that the entrainment and mixing of air into
convective clouds is extremely inhomogeneous. This is important because the
characteristics of convective clouds based on Na values can be extended
in space and time by their application to satellite-calculated Na (which
is obtained with the same parameterization that has been recently developed
from the satellite-retrieved vertical evolution of re in convective
clouds; Rosenfeld et al., 2014b).
InstrumentationCloud particle measurements
The instrumentation used to measure cloud particles and rain or ice
formation consists of three cloud probes: CAS-DPOL, CCP–CDP, and CCP–CIP
(Brenguier et al., 2013). In this study, cloud particle
counts are accumulated for bin diameters larger than 3 µm from the
CCP–CDP and CAS-DPOL; the lower size bins from these probes overlap with
haze particles. Nucleated cloud drops in convective clouds grow quickly
beyond 3 µm. Details about the cloud probe measurement characteristics
are described in the following subsections and in Braga et al. (2017).
CCP–CDP and CCP–CIP measurements
The Cloud Combination Probe (CCP) combines two detectors, the Cloud Droplet
Probe (CDP) and the grayscale Cloud Imaging Probe (CIPgs). The CDP detects
the forward-scattered laser light of cloud particles when penetrating the CDP
detection area (Lance et
al., 2010). The CIP records 2-D shadow-cast images of cloud elements. In
this study, we deduced the existence of ice from the occurrence of visually
nonspherical shapes of the shadows. The particle detection size range is 2
to 960 µm when measuring with the CCP at a 1 Hz frequency
(Wendisch et al., 2016). The combination of CCP–CDP and CCP–CIP information
provides the ability to measure cloud droplets and raindrops within clouds
for nearly the same air sample volume. The maximum number of particles
measured by CCP–CDP and CCP–CIP are about 2000 and 500 cm-3 for a 1 Hz
cloud pass, respectively. For the data processing of the CIP measurements,
ice is assumed as the predominant particle phase in the mixed-state cloud
conditions throughout the ACRIDICON–CHUVA campaign. The assumption of ice
density instead of water density implies a slight overestimation
(∼ 10 %) of the calculated rainwater content for particles
greater than 75 µm. An additional data processing assuming water
density as the predominant particle phase was performed for flights during which
warm rain was initiated below the 0 ∘C isotherm.
CAS-DPOL measurements
The CAS-DPOL measures particle size distributions between 0.5 and 50 µm at
a 1 Hz time resolution (Baumgardner et al., 2011; Voigt et al., 2010, 2011). Number concentrations are derived using the probe air
speed measured at the instrument. Particle inter-arrival time analysis did
not show influences of coincidence (Lance,
2012). The data analysis and uncertainties are described in detail in Braga
et al. (2017).
Braga et al. (2017) have shown sufficient agreement between the CAS-DPOL
and CCP–CDP measurements of cloud droplet number concentration to
distinguish convective clouds that develop above clean vs. polluted regions
during the ACRIDICON–CHUVA campaign. In addition, the CWC estimated by
integration of the DSDs measured with both probes showed good agreement with
hot-wire CWC measurements (Braga et al., 2017).
Meteorological data
The HALO aircraft was equipped with a meteorological sensor system (BAsic
HALO Measurement And Sensor System – BAHAMAS) located at the nose of the
aircraft (Wendisch et al., 2016). The uncertainties for measurements of
temperature, relative humidity, and vertical wind speed are 0.5 K, 5 %, and
0.3 m s-1, respectively
(Mallaun et al., 2015).
Aerosol measurements
Aerosol particle measurements were performed using the Passive Cavity
Aerosol Spectrometer Probe 100X (PCASP-100X), which is an airborne optical
spectrometer that measures aerosol particles in the 0.1 to 3 µm
diameter range (Liu et al., 1992). The maximum number of particles measured
by PCASP is about 3000 cm-3 for a 1 Hz cloud pass. During the
ACRIDICON–CHUVA campaign, PCASP was not operated with a heated inlet, and thus
the measured aerosol particles below cloud base (about 200 m) can be larger
than the original dry size due to swelling.
Methods
The analyses are performed along the following general steps.
The relationship between re and the probability of drizzle is found. The
value of re is calculated from the size distributions measured by the
CAS-DPOL and the CCP–CDP (two different values). DWC, RWC, and MPWC are
obtained from the CCP–CIP data. The calculations of these cloud properties
are detailed in Sect. 4.1.
The Na at cloud base is estimated through the vertical profile of
re. The calculation of Na is detailed in Sect. 4.2.
The height of rain initiation based on the modeled adiabatic growth of
re with height is estimated for different aerosol conditions as a
function of estimated Na. The value of D13 is estimated as the
cloud depth for which the adiabatic re reaches 13 µm (as
described also in Sect. 4.2).
The extent of agreement between the directly measured Dr within
convective clouds and the estimated D13 based on the assumption of
adiabatic re growth and on the measured re is discussed in Sects. 5
and 6.
Estimation of re, rain, and ice initiation
Rain is initiated during the warm phase of growing convective cumulus by
the intensification of the collision and coalescence (coagulation) processes
with height. The efficiency of the process of droplet coalescence is
determined by the collection kernel (K) of the droplets and their
concentrations (Pruppacher et al., 1998). Freud
and Rosenfeld (2012) have shown through model simulations and aircraft
measurements that K∝rv4.8, where rv is the mean volume
radius obtained from the cloud probe DSDs in the absence of ice; rv is
defined as follows:
rv=3CWC4πρNd13,
where ρ is the water density (1 g cm-3), CWC is in g m-3,
and Nd is in cm-3. The values are obtained from the 1 Hz data of
droplet size distributions from the cloud probes. The calculation of CWC is
performed separately with the CAS-DPOL and CCP–CDP probe droplet concentrations
as follows:
CWC=4π3ρ∫N(r)r3dr,
where N is the droplet concentration and r the droplet radius. The calculations
of DWC, RWC, and MPWC are done in a similar fashion to CWC but with different
cloud probes and particle size ranges (see Table 2).
The definition of re is
re=∫N(r)r3dr∫N(r)r2dr.
Freud and Rosenfeld (2012) showed that rv≈1.08⋅re, depending on the droplet size distribution. Using this relationship,
they derived re from rv and showed that warm rain initiates within
clouds when re is about 13–14 µm
(Klein
et al., 2009; Rosenfeld and Gutman, 1994; Rosenfeld and Lensky, 1998;
Rosenfeld et al., 2012a, 2014c).
Classification of each flight as a function of Na at
cloud base. The values of cloud base height (Cbh) and temperature (T),
D13, Dr and Di in meters, and temperatures
in ∘C are also shown for the convective cloud measurements of each
flight. Additionally, information about the height of Dr-1,
Di-1, Tr, Ti, Ti-1, NLP, and
Wmax is also shown for each flight. The uncertainties of
Na and D13 estimates are described in Appendix A.
Only measurements with CWC larger than 25 % of the adiabatic water content
are considered in order to exclude convectively diluted or dissipating
clouds. It is assumed that rain (or ice) formation starts when calculated
DWC exceeds 0.01 g m-3 (Freud and Rosenfeld, 2012). For rain initiation
in liquid phase, the DWC threshold is ∼ 10 % greater due to
the overestimation of DWC during CIP measurements in warm clouds (as stated
in Sect. 3.2.1). The small terminal fall speed of the drizzle drops (≤ 1 m s-1) allows for a focus on in situ rain (or ice) initiation while
minimizing the amount of DSDs affected by raindrops falling from above into
the region of measurements. In addition, cloud passes with rain were
eliminated when cloud tops were visibly much higher than the penetration
level (> ∼ 1000 m) based on the videos recorded by
the HALO cockpit forward-looking camera. However, cloud tops higher than
a few hundred meters above the penetration level occurred only rarely.
Table 3 shows the cloud depth above cloud base at which warm rain initiation
(Dr) occurs (i.e., DWC > 0.01 g m-3) for all flights as
a function of estimated Na. The Dr is taken as the cloud depth for
ice initiation (Di) if ice particles are evident in the CCP–CIP images.
Here, the Di is visually ascribed for sizes greater than ∼ 0.25 mm
and it does not mean that frozen smaller particles cannot be
present. The assumption of water or ice density as the predominant particle
phase in DWC calculation based on the CCP–CIP probe did not impact the Dr and
Di measured because the DWC threshold (i.e., DWC > 0.01 g m-3)
for warm rain or ice initiation was achieved at the same cloud
depth for both particles densities. Additional details about the cloud
profiling characteristics for each flight, such as the number of altitude levels
sampled (NLS) and the highest cloud depth without raindrop (Dr-1) or ice
particles (Di-1), are also available in Table 3. Furthermore,
Appendix A discusses the uncertainty calculations of the estimated
parameters of cloud properties.
Estimating Na and adiabatic re
The Na for the convective clusters is estimated based on the slope
between the calculated adiabatic CWC (CWCa) and the mean volume mass of
the droplets (Mv), which is the mass of a water sphere having the radius
rv. Mv is calculated for 1 s DSD measurements of CAS-DPOL and CCP–CDP for
nonprecipitating cloud passes (Braga et al., 2017). The underlying
assumption is that the measured rv is approximating the adiabatic rv
(rva) due to the nearly inhomogeneous mixing behavior of the clouds with
the ambient air (Beals et al., 2015). Therefore, the measured Mv
approximates the adiabatic Mv (Mva, where Mva= CWCa/Na). This methodology does not account for cloud mixing losses from
droplet evaporation or additional drop activation. Both incur an
overestimation of Na. It was found that the calculated Na
values based on the vertical profile of re commonly overestimate the measured
Na near cloud base by 30 % (Freud et al., 2011). Therefore, in
calculating Na a factor of 0.7 is applied to Na estimates.
Braga et al. (2017) have shown that this estimated Na was in reasonably good
agreement with the directly measured cloud base droplet number
concentration, Nd, as obtained from the CCP–CDP and CAS-DPOL during
ACRIDICON–CHUVA. Once Na is estimated, the adiabatic re (rea)
can be calculated based on a simple adiabatic parcel model in which droplet
growth is dominated by condensation (Pinsky
and Khain, 2002), where rea=1.08⋅rva. The value of
D13 is defined as the cloud depth for which rea reaches 13 µm.
The uncertainty calculations of cloud properties estimated from cloud
probes were described in Braga et al. (2017). The uncertainties of
re, rv, rea, rva are about 10 %, while for CWC and
Mv the uncertainties are about 30 %. The Na calculation does not
take into account the possibility of new nucleation above cloud base (Freud
et al., 2011). Braga et al. (2017) have shown that the assumption of
the adiabatic growth of droplets via condensation from cloud base to higher
levels within cloud can lead to an overestimation by ∼ 20–30 % of
the number of droplets at cloud base when calculating Na in
cases with additional droplet nucleation above cloud base.
The Na calculated for cloud base was used to classify clouds as having
developed in clean, polluted, or very polluted regions. A clean cloud case
was defined as Na < 500 cm3, polluted as
500 cm-3 < Na < 900 cm-3, and very polluted
as Na > 900 cm-3. During ACRIDICON–CHUVA, a flight in clean clouds
(AC19) was performed over the Atlantic Ocean. Clouds observed during flights
over the northern Amazon were classified as polluted, mainly due to diluted
smoke from biomass burning advected by long-range transport. This region
represents the Amazon background condition for aerosol concentration during
the dry season. Very polluted conditions were met over the central Amazon,
which was strongly affected by biomass burning over the Amazonian
deforestation arc (southern Amazon).
ResultsThreshold of re for warm
rain initiation
The values of re derived from integrating the cloud probe DSDs were used
to identify rain initiation. Some caution is required to eliminate possible
bias resulting from peculiar shapes of the drop size spectrum. An re
value of 13–14 µm represents the rain initiation threshold for growing
convective cumulus observed at different locations in the world as long as
there is no significant influence from giant CCN (GCCN; dry soluble diameter
> 1 µm; Freud and Rosenfeld,
2012).The presence of GCCN during cloud droplet formation at cloud base can
lead to a faster formation of raindrops due to both the rain embryo effect
and the competition effect that reduces cloud base maximum supersaturation
and consequently reduces Nd
(Rosenfeld, 2000; Segal et
al., 2007). Such cases are very common over the ocean due to sea spray
aerosols; there, the values of re at which raindrops start to form are
commonly smaller than the usual threshold of 13–14 µm
(Freud and Rosenfeld,
2012). In our study the DSDs from flight AC19 performed over the Atlantic
Ocean did not show a large drop tail near cloud base (see Fig. S2). The cumulative sample volume from the CCP–CDP probe at
cloud base was about 5.8 L-1 for 176 s of measurements. The figure
shows the scarcity of large cloud droplets (with diameters > 20 µm)
near cloud base where the mean concentration of such droplets is
smaller than 0.1 drop cm-3. Such a small concentration of large droplets
at cloud base is insufficient to have any significant effect on
supersaturation.
(a) Precipitation probability as a function of
re for the CCP–CDP probe for different DWC thresholds (black
DWC > 0.01 g m-3; blue
DWC > 0.02 g m-3; green
DWC > 0.03 g m-3; gold
DWC > 0.05 g m-3; red
DWC > 0.1 g m-3). The dashed line indicates the number of
cases (in seconds for each 1 s cloud pass) for each re size
interval (right axis); (b) similar for the CAS-DPOL probe.
Figure 2a–b show the precipitation initiation probability as a function of
re calculated from the CCP–CDP and CAS-DPOL probes for all flights
analyzed over the Amazon. The probability of precipitation is the fraction
of 1 Hz in-cloud measurements that exceed certain DWC thresholds (i.e., for
DWC > 0.01 g m-3). This was calculated as a function of the re
value. These figures show that for the CCP–CDP probe rain initiation is
expected to occur at re > 13 µm, whilst for CAS-DPOL
the rain initiation threshold is re > 12 µm.
The differences of the two instruments in the re range below ∼ 7 µm
and above ∼ 11 µm have been discussed in
Braga et al. (2017). For re < 7 µm, they are related to a
higher sensitivity of the CAS-DPOL for small cloud and aerosol particles,
whereas for re > 11 µm CAS-DPOL has lower sensitivity
to large particles than CCP–CDP; however, the differences are not significant
within the uncertainties of the measurements. Because the CCP–CDP was
mounted very close to the CCP–CIP, the results from this probe are shown in
subsequent sections; similar results were found from data collected with the
CAS-DPOL probe.
Comparing estimated rea with measured re
The comparison between the values of rea (calculated from the estimated
Na at cloud base described in Sect. 4.2) with the measured re is
the basis for analyzing the evolution of cloud particle size until rain or
glaciation initiation occurs within the cloud.
Rosenfeld et al. (2012b) showed that a tight
relationship between the Na calculated for cloud base and the evolution
of rea with height (rea-Dc) provides a useful proxy for the depth
in convective clouds at which raindrops start to form.
Case study: flight AC07 over the Amazon deforestation arc
Flight AC07 was performed over the deforestation arc (see Fig. 1a). Figure 3
shows the number of droplets measured at different heights in the
convective clouds. Droplet concentrations reaching ∼ 2000 cm-3 were measured at cloud base, which is characteristic of very
polluted clouds. The cloud base was located at about 1900 m a.s.l. (above sea level),
with ambient air temperature at about 15 ∘C. Figure 4a shows
the mean DSD for a cloud penetration at cloud base. It emphasizes the higher
number concentration of small droplets (< 10 µm) that are
observed in convective clouds forming in polluted environments. Figure 4b
shows the evolution of re measurements and estimated rea as a
function of temperature. The figure also shows that the values of re do
not exceed the 13 µm threshold at warm temperatures. These results
suggest that cloud droplets formed at cloud base grow mainly via
condensation and no raindrops were formed during the warm phase of
convective cloud development. However, to rule out coalescence processes as
a possible reason for droplet growth, further analysis using CCP–CIP images
was done.
Cloud droplet concentration measured with CCP–CDP as a function of
temperature for flight AC07. Each dot indicates a 1 Hz average
concentration. The sample number (N) and the approximate start time of the
cloud profile are shown at the top of the panel.
(a) Mean cloud droplet size distribution calculated from
the CCP–CDP data for a cloud pass at cloud base during flight AC07. The
flight number, initial time of cloud pass, and duration in seconds are shown
at the top of graph. The mean total number of droplets (Ndmean),
the maximum total number of droplets (Ndmax) in 1 s for this
cloud pass, and the approximate height (H) and temperature (T) are shown
at the upper right corner of the graph; (b) cloud droplet effective
radius (re) calculated from CCP–CDP as a function of temperature
is indicated with dots. The black line indicates the estimated adiabatic
effective radius (rea) as a function of temperature.
(a–c) Droplet size distribution composite from the CCP–CDP
and CCP–CIP probes (left panel). Similar for indicated cloud water content
(CWC) in the right panel. Indicated at the top of the panels are the HALO
flight number, date, time of flight (UTC), duration of cloud pass in seconds,
temperature (T) and altitude (H) above sea level, and the mean values for
the total number of droplets (Nd), CWC, DWC, RWC, and
re. The color bars indicate the height of HALO during the cloud
pass. On the right side of the panels, CCP–CIP images corresponding to the
cloud pass are shown.
Figure 5a–c show the evolution of DSD and CWC (mean values) as a function
of height above cloud base and the cloud particle images from the CCP–CIP.
Figure 5a plots the data for a cloud pass at warm temperatures and Fig. 5b–c
result from measurements during cloud passes at cold temperatures. The
DSDs show that most droplets have a diameter smaller than 20 µm, and
only very few large droplets are observed for warm temperatures. The CCP–CIP
detected only cloud droplets and no raindrops, as is evident from both RWC and
DWC values of < 0.01 g m-3. At cold temperatures, the CCP–CIP images show
the irregular shapes of large ice particles. No spherical raindrop shapes
were found in these data for any of the cloud passes, including those
collected at warm temperatures. The DWC and RWC calculated from the mean
DSDs show values greater than zero only when ice particles were observed on
the CCP–CIP images. Also, for a cumulative sample volume of 1.24 m-3
from 89 s of CCP–CIP measurements, no raindrops were observed between the
heights above cloud base of 2900 m (0 ∘C) and 7100 m
(-26.25 ∘C). This means that the raindrop concentration, if
any, was smaller than 1 drop m-3. This is a negligible rain rate and
supports the notion of a practical shut-off of coalescence. Furthermore, the
CCP–CIP did not detect any raindrops at lower levels (warm temperatures) for
a cumulative sample volume of 5.9 m-3 from 426 s of measurements.
These results indicate a strong inhibition of raindrop formation within
growing convective cumulus for this flight over the deforestation arc of the
Amazon. Even though some of the indicated effective radii values are larger
than 13 µm for colder temperatures, these values do not indicate rain
formation when only ice particles are observed. This does not exclude the
possibility that small raindrops froze soon after their formation in such
low temperatures.
The mean DSD and CIP images shown in Fig. 5c result from a passage through
a convective cloud with lightning activity. Figure 6 shows a photo of the
cloud taken from the HALO cockpit just before the cloud penetration. The
CCP–CIP has imaged graupel in this case. The presence of these types of ice
particles within convective clouds is very common in thunderstorms, and
previous studies highlight the large frequency of lightning occurrence
during the dry-to-wet season over the deforestation arc region of the Amazon
(Albrecht et al., 2011; Williams et al., 2002). These results also highlight the role of aerosols from biomass
burning in warm rain inhibition and in the aerosol invigoration effect due
to the generation of large ice particles and lightning
(Rosenfeld et al., 2008).
Image taken from the HALO cockpit just before the aircraft
penetration of a convective cloud with lightning activity during flight AC07.
In this case, the cloud pass height was 9022 m (temperature
∼-25 ∘C) and the maximum CWC measured was
0.55 g m-3.
Regarding the values of re as a function of Dc, Fig. 7a shows the
estimated rea (calculated from the adiabatic CWC shown in Fig. 7b) and
measured re. The figure shows that the estimated values for rea are
close to the re measurements for convective cloud passes at different
Dc. Even though no raindrops were observed in the convective cloud, the
figure shows similar values of rea and measured re (with rea
slightly larger) as a function of Dc.
(a) Cloud droplet effective radius (re) as a
function of cloud depth (Dc) for flight AC07. The line indicates
the re estimated for adiabatic growth (rea) from
cloud base (dashed lines indicate the rea values considering the
uncertainty of the estimate). The height of 0 ∘C is indicated by a
black horizontal bar across the rea line. The estimated adiabatic
number of droplets (Na) at cloud base is shown at the top of the
figure. (b) Similar to panel (a) for cloud water content
(adiabatic values are shown by lines).
Results of analysis of re and Dc in clean and polluted regionsClean region
Figure 8a shows the measured Nd of a convective cluster over the
Atlantic Ocean off the Brazilian coast (flight AC19). This region was
classified as clean because Na is about 300 cm-3 (see Table 3). The
cloud base was located at 600 m a.s.l. at a temperature of 22 ∘C. Given
the clean conditions over the ocean, the high
relative humidity at surface level and the low concentration of CCN lead to
the formation of large droplets close to cloud base. Figure 8b shows
the estimated rea and the measured re as a function of Dc.
Several cloud passes showed large droplets with re∼ 13 µm at only 1660 m above cloud base.
Figure 9a–b show the DSDs and
CCP–CIP images for the cloud passes at the height at which rain starts to form
and at the greatest height measured above cloud base, respectively. Figure 9a shows
that rain is already initiated (DWC > 0.01 g m-3)
when the droplets become larger than about re > 12 µm.
This is probably due to the presence of GCCN over this maritime region.
(a) Cloud droplet concentrations measured with the CCP–CDP
as a function of temperature for flight AC19. Each dot indicates 1 Hz
average concentration. The sample number in seconds (N) and the start time
of the cloud profile are shown at the top of the panel; (b) similar
to Fig. 7 for flight AC19.
(a, b) Similar to Fig. 5a–c for flight AC19.
Figure 10 shows the mean aerosol particle size distribution (PSD) as
measured by the PCASP just below cloud base for clean, polluted, and very
polluted regions. The mean total number concentration of aerosol particles
with sizes larger than 0.1 µm is about 1000 cm-3 over the Atlantic
Ocean, whilst for the polluted (very polluted) case this value is about 3
(10) times larger. In addition, the mean total number concentration of
particles measured by the CCP–CDP shows a concentration 10 times greater for
particles larger than 10 µm over the ocean in comparison with the inland
Amazon region. This figure indicates the presence of large aerosol
particles with sizes greater than 1 µm (possibly GCCN) over the
ocean. When it nucleates droplets, this type of aerosol accelerates the
growth of droplets during the warm phase, leading to a faster formation of
raindrops than predicted by the adiabatic parcel model. About 3500 m above
cloud base, large raindrops are observed in the CCP–CIP images (see Fig. 9b).
The low CWC indicates that most of it was already converted into
raindrops. These results highlight the fact that under clean conditions, raindrops
were formed mainly by the warm-phase processes of cloud development. Even if the
convective clouds reach colder temperatures, the low remaining amount of
cloud water reduced a key ingredient for cloud electrification.
Cumulative aerosol size distribution below cloud base calculated
from the PCASP probe for typical clean, polluted, and very polluted regions
(solid line) for flights AC12 (very polluted), AC18 (polluted), and AC19
(clean). Similar for cumulative cloud droplet size distribution calculated
with CCP–CDP (dashed line). The flight numbers are indicated by colors at the
top of the panel.
Before raindrops start to form (Dc∼ 1660 m) updrafts were
observed with most values < 4 m s-1, and when rain starts
downdrafts start to be evident (see Fig. S3g).
The values of vertical velocities measured for flight AC19 (clean region)
were smaller than measured for flight AC07 (very polluted region). However,
for both cases updrafts are more evident during droplet growth via
condensation and downdrafts are stronger when precipitation particles are
observed in the cloud. Strong updrafts (∼ 10 m s-1) are
observed in polluted cases after ice starts to form (see Fig. S3a), probably due to the latent heat release during
freezing processes. An alternative explanation for updraft enhancement due to
environmental conditions in these cases cannot be excluded.
Polluted regions
The flights AC09 and AC18 were classified as polluted (see Table 3). These
flights were performed over the northern Amazon region (see Fig. 1a).
Figure 11a shows the measured Nd from flight AC09. The cloud base was
located about 1200 m a.s.l. at a temperature of 19.5 ∘C. Figure 11b shows the estimated rea and the measured
re as a function of Dc. Values of re > 13 µm
were observed for temperatures around 0 ∘C, indicating the
possibility of rain starting at this height. Figure 12a–b show the DSDs and
CCP–CIP images from flight AC09 at the height at which rain starts to form
(Dr∼ 3000 m) and at the greatest height with measurements
above cloud base. The CIP image in Fig. 12b shows the first pass in which
ice hydrometeors are observed mixed with supercooled raindrops. For lower
levels only raindrops were observed. For flight AC18 cloud base was located
about 1700 m a.s.l. at a temperature of 17 ∘C, and
rain started to form in convective clouds when Dr∼ 3800 m.
The measured Nd and the estimated rea and measured re as a
function of Dc from flight AC18 are shown in Fig. S4a–b.
Figure S5a shows that
the first raindrops in AC18 are observed at the -5.7 ∘C
isotherm and that they still remain liquid or at least spherical at the
-11.4 ∘C isotherm (see Fig. S5b). Larger raindrops and a high amount of DWC were observed on AC09
for warmer temperatures than on flight AC18 (not shown). These results show
that differences in cloud particle formation are associated with the
Dc at which convective clouds start to form raindrops or ice, defined
earlier as Dr and Di. Flight AC18 has a droplet concentration,
Nd, of up to 100 cm-3 greater than the measurements during AC09 (see Fig. S4a).
With higher Nd at cloud base,
droplet growth via condensation in convective clouds is a less pronounced
function of height due to the water vapor competition between droplets.
Under these conditions, the collision and coalescence process and the freezing
of droplets are initiated at higher Dc
(Freud
and Rosenfeld, 2012; Rosenfeld et al., 2008). For this reason, the
formation of raindrops and ice particles on flight AC09 starts at lower
Dc than on flight AC18 (assuming nonsignificant additional CCN
activation above cloud base).
(a) Cloud droplet concentrations measured with the CCP–CDP
as a function of temperature for flight AC09. Each dot indicates 1 Hz
average concentration. The sample number in seconds (N) and the start time
of the cloud profile are shown at the top of the panel; (b) similar
to Fig. 9 for flight AC09.
(a, b) Similar to Fig. 5a–c for flight AC09.
(a) Cloud droplet concentration measured with the CCP–CDP
probe as a function of temperature for flight AC13. Each dot indicates a
1 Hz average concentration. The sample number and the approximate time of
the cloud profile are shown at the top of the panel; (b) similar to
Fig. 7 for Flight AC13.
Similar to Fig. 5a–c for flight AC13.
Cloud depth (Dc) as a function of the estimated
adiabatic number of droplets (Na) at cloud base. The
Dc values for adiabatic cloud droplet effective radius
(rea) equal 13 µm (or D13) and are indicated by
triangles. Similar for cloud depth of rain initiation (Dr)
(indicated by circles) and cloud depth for ice initiation (Di)
(indicated by an asterisk). The flight numbers are indicated by colors on the
right side of the panel. The values of D13, Dr, and
Di are shown in Table 1. The uncertainties of Na
estimates are shown by horizontal error bars. The vertical error bars
indicate the cloud depth between Dr and Dr-1 or
Di and Di-1. The black line indicates the linear
equation for D13 as a function of Na for all flights, where
Dr= (5±1.06)Na.
Very polluted regions
Five flights were classified as very polluted (see Table 3): AC07, AC08,
AC12, AC13, and AC20. The microphysical analysis of the measurements
collected in growing convective cumulus during flight AC07 was already
presented in Sect. 5.2.1. Figure 13a shows the measured Nd from flight
AC13, which was made in the same region as flight AC07. The figure shows
that the values of Nd near cloud base on flight AC13 reach 2000 cm-3,
similar to AC07. However, the rate of decrease in Nd with
height above cloud base is much smaller in AC13 compared to AC07. During
flight AC13 the measurements of large updrafts (which increase
supersaturation and induce new droplet activation) and large aerosol
concentrations above cloud base suggest the occurrence of additional CCN
activation, leading to the observed relative increase in Nd with height.
This is supported by the fact that the observed re values are smaller than the
calculated rea, as shown in Fig. 13b. Only values below 13 µm
are observed (maximum of 12 µm), indicating the suppression of
raindrop formation. Indeed, no raindrops were observed in the CCP–CIP images
from growing convective cumulus passes on this flight, and only cloud
droplets and ice particles were detected. Figure 14 shows the DSD and
CCP–CIP images at the start of glaciation (Di∼ 4800 m).
These results highlight the role of aerosols in the inhibition of raindrop
formation due to inducing a larger Nd and respective lower re, which
leads to the suppression of collision and coalescence processes in very polluted
regions.
The measured Nd during flights AC08, AC12, and AC20 was greater above
cloud base than at cloud base on several cloud passes (especially in flights
AC08 and AC20; see Figs. S6 and S7 for these
flights). In these flights the estimated rea values were larger than the
measured re as a function of Dc and strong updrafts (up to 15 m s-1)
were observed above cloud base (see Fig. S3b, d, and h). The acceleration of updrafts above the height of
cloud base increase supersaturation and can thus induce additional CCN
activation. For flights during which we observed the increase in Nd with
height, a high aerosol concentration was observed, indicating additional CCN
activation above cloud base. During these flights, cloud profiling was
performed up to Dc∼ 3500 m, and the values of measured
re were smaller than 13 µm, indicating the suppression of
raindrop formation. The analysis of the data from the cloud probe DSDs and
CCP–CIP images indicates that indeed no raindrops were present on these
flights (not shown). The measurements from AC07 and AC13 over very polluted
regions in the Amazon suggest that no raindrops are formed in growing
convective clouds under these conditions. Instead, large precipitation
particles are formed at cold temperatures in the form of ice. The Dc at
which these ice particles are formed depends on the size of the cloud
droplets (re) at colder temperatures (larger droplets freeze earlier or
at lower Dc; Pruppacher et al., 1998). This
was previously documented by satellite retrievals
(Rosenfeld et al., 2011) in which the glaciation temperatures
of convective clouds were strongly dependent on re at the -5 ∘C isotherm,
and smaller re values were correlated with lower glaciation
temperatures.
Discussion
The results from cloud probe measurements under clean, polluted, and very
polluted conditions highlight the role of aerosol particles in rain and ice
formation for growing convective cumulus. Figure 15 summarizes the estimated
depths above cloud base at which the initiation of rain and ice formation is
observed (Dr and Di) and the estimated Dc for rain
initiation as indicated from rea by D13. This figure shows a close
relationship between Na and Dr of Dr= (5±1.06)⋅Na, demonstrating the capability to predict the minimum
height at which raindrops are expected to form based on cloud base drop
concentrations. For flights in which rain was observed (AC19, AC18, and
AC09), Dr occurs at heights slightly greater than D13. For cases
in which neither rain nor ice was observed (AC08, AC12, and AC20), the
estimated D13 was not reached during the HALO cloud profiling flights.
In addition, D13 and Di show similar values for flight AC07,
whereas for flight AC13 the values are less comparable (probably due to an
overestimation of Na and thus D13 caused by additional CCN
activation above cloud base).
The linear relationship between Na and D13 indicates a regression
slope of about 5 m (cm-3)-1 between D13 and the calculated
Na for the Amazon during the dry-to-wet season. This value is slightly
larger than the values observed by Freud and Rosenfeld (2012) for other
locations around the globe (e.g., India and Israel). These clear linear
relationships found between Na and Dr (∼D13) for different
regions highlight the efficiency of the adiabatic parcel model to estimate
the height of rain initiation within convective clouds in this study.
Additional associated cloud processes, such as GCCN, cloud, and mixing with
ambient air and other processes, which are not accounted for in this study,
would produce deviations that are likely to be the cause of the observed
scatter in the results.
For the flight in the cleanest conditions (AC19), the presence of larger aerosol
particles (possibly GCCN from sea spray) below cloud base leads to a faster
growth of cloud droplets via condensation with height, and consequently
re is smaller than 13 µm (see Fig. 9a) for warm rain initiation.
A similar decrease in re for rain initiation over ocean was observed by
Konwar et al. (2012). While Dr is explained by Nd and well
correlated with it, there is no correlation between Nd and Di.
CDP-measured cloud droplet effective radius (re)
(colored dots) and estimated cloud droplet adiabatic effective radius
(rea) (colored lines) as a function of cloud depth
(Dc) for all flights (indicated by colors). The height of
0 ∘C is indicated by a horizontal bar across the rea
line. The circles indicate the approximate values of drizzle water content
(DWC) calculated from the CCP–CIP data; the range of DWC values is indicated
in the table in panel (b). The star symbols indicate the approximate
mixed-phase drizzle water content (MPWC) values calculated from the CCP–CIP data
(indicated in the table in panel c). The temperature in ∘C of rain
or ice initiation (Dr and Di, respectively) is
indicated by colored numbers close to the circle or star symbols.
General characteristics of growing convective cumulus formed over
the Amazon basin during the dry season. The heights of cloud base are higher
over the continental Amazon due to the smaller relative humidity in comparison
with the maritime region. Convective clouds formed over the Atlantic Ocean
near the Brazilian coast have smaller cloud droplet concentrations
(Nd) at cloud base due to the smaller concentration of aerosol
and updraft speeds below cloud base. The initiation of warm rain
(Dr) is observed at lower cloud depths (∼ 2 km or
∼ 10 ∘C) from collision and coalescence processes. When
convective clouds are more continental, larger aerosol concentration and
updrafts are observed below cloud base, leading to larger Nd
nucleated at cloud base (as observed above forested and deforested regions).
Over the forest Dr is observed near 0 ∘C, whilst for the
deforestation arc region the collision and coalescence processes are totally
suppressed and the formation of ice particles took place at higher altitudes
in the clouds in very polluted conditions because the resulting smaller
cloud droplets froze at colder temperatures compared to the larger drops in
the less polluted cases.
Figure 16 illustrates the vertical development of precipitation water
content by symbols representing the amount of DWC and MPWC as a function of
Dc and CDP-measured re. Also shown are the lines of rea as a
function of Dc. The figure shows that raindrops began to form at
re of 13 µm for AC09 and AC18. The re for rain initiation is
slightly smaller (12 µm) on AC19, probably due to the sea spray giant
CCN, which accelerates the coalescence for a given re. Mixed-phase
precipitation was initiated on flights AC07 and AC13, well below the height
of D13 at an re of 11.5 and 10.2 µm, respectively. Ice starts
to form at lower temperatures when the cloud droplets are smaller, as
manifested by Di of -9 and -14 ∘C for flights AC07 and AC13,
respectively. The remaining flights did not reach the height for rain
initiation (AC08, AC12, and AC20).
It is evident that raindrops form faster via collision and coalescence
process in a cleaner atmosphere. For the polluted cases, raindrops form at
colder temperatures (∼ 0 ∘C and colder) via
collision and coalescence than for clean conditions. Rain can initiate at
supercooled temperatures, e.g., -5 ∘C on AC18. The raindrops were
documented to start freezing at -9 ∘C in AC09. In very polluted
conditions, only cloud droplets and no raindrops were observed at Dc < 4000 m.
In these cases, precipitation was initiated as ice
particles at Dc > 4000 m. These flights with completely
suppressed warm rain were performed over the smoky deforestation arc.
Measurements of large updrafts that increase supersaturation within cloud
and the higher Nd above cloud base indicate new activation of cloud
droplets for flight AC13 (not observed at AC07) in the course of the
development of convective cumulus. This additional CCN activation leads to
smaller re. For flights during which additional CCN activation was significant,
the differences between the estimated rea and the re measurements at
the same height are larger because the adiabatic estimation does not consider
the additional CCN activation of droplets above cloud base and thus
overestimates the observed size.
Figure 17 summarizes the findings from the vertical profiling flights. It
illustrates the vertical microstructure of growing cumulus above the Amazon
and the adjacent ocean in varying aerosol conditions. The figure highlights
the differences in aerosol concentrations and cloud particle distribution
within convective clouds over the Amazon basin (including the Atlantic
Ocean, forested, and deforested regions). The aerosol concentration is
smaller over the Atlantic Ocean and increases significantly at the continental
Amazon, especially over the deforestation arc due to biomass burning
emissions from forest fires. The more polluted the atmosphere, the larger the
number of droplets nucleated at cloud base and the less efficient the growth
of cloud droplets via condensation with Dc. The new activation of CCN
above cloud base has also been shown to decrease the efficiency of cloud droplet
growth due to the higher competition for water vapor available. The increase
in aerosol concentration over the Amazon basin according to our findings has
been shown to suppress the warm rain formation because larger cloud depths were
necessary for raindrops to start to form (when cloud droplets have re∼ 13–14 µm). The additional aerosol concentrations
observed at polluted regions from forest fires suppress rain such that most
hydrometeors are ice when they are at a size that allows us to distinguish
their phase (∼ 0.25 mm). In addition, the formation of ice particles
was also delayed (or occurred at higher Dc) in more polluted atmospheres
because smaller cloud droplets freeze at lower temperatures.
Conclusions
This study focused on the effects of aerosol particle number concentration
on the initiation of raindrops and ice hydrometeors in growing convective
cumulus over the Amazon. Data from aerosol and cloud probes onboard the
HALO aircraft were used in the analysis. The values of the estimated
Na at cloud base were applied to classify the atmospheric conditions
under which convective clouds developed as a function of aerosol particle number
concentration (i.e., clean, polluted, and very polluted regions). From the
estimated Na, the evolution of rea and the theoretical re, assuming
adiabatic growth of droplets with cloud depth above cloud base
(Dc),
were compared with the observed re at the various heights. A DWC value
of 0.01 g m-3 was used as a threshold for rain initiation or glaciation
within clouds. Images from the CCP–CIP probe were used to detect the
presence of raindrops and/or ice hydrometeors. The results shown in previous
sections support the following conclusions.
The use of re∼ 13–14 µm as a threshold for rain
initiation is suitable for convective clouds formed at the Amazon basin
during the dry season. It is in agreement with re of rain initiation
elsewhere.
The evolution of the directly observed re generally follows that of the
calculated rea due to the nearly inhomogeneous mixing behavior of the
convective clouds with the ambient air. Convective clouds are usually
nonadiabatic systems because of strong wind and turbulence effects, heating, and
other factors, but the similarities of re and rea provided the
capability to estimate Dr over the Amazon and other regions around the
globe (e.g., India and Israel).
Rain initiation occurred higher in more polluted clouds, as manifested by
higher Dc. Rain was initiated at supercooled levels in moderately
polluted clouds. In very polluted conditions, warm rain was suppressed
completely. This was exacerbated by the occurrence of additional CCN
activation above cloud base, which further reduced re compared to
rea.
The initiation of ice hydrometeors is also delayed to greater Dc in more
polluted clouds because smaller drops freeze at colder temperatures due to
suppressed ice multiplication processes (Hallett and Mossop, 1974). Ice was
initiated mostly by freezing raindrops in cases when warm rain formation was
not completely suppressed.
Both the D13 and Dr increased linearly with Na, which is in agreement
with the theoretical considerations of Freud and Rosenfeld (2012). Despite
the suspected occasional additional CCN activation, re was sufficiently
close to rea to allow a linear relationship in the form of Dr= (5±1.06)⋅Na.
The deviation from an exact linear
relationship can be associated with additional cloud processes, such as GCCN,
cloud, and mixing with ambient air. The magnitude of these additional
processes is insufficient to mask the linear relationship. The observations
also suggest that, in the absence of new droplet activation above cloud
base, D13 is very similar to Di under very polluted
conditions in which raindrops are not formed at warmer temperatures.
These results show that even moderate amounts of smoke, which fills most of
the Amazon basin during the drier season, are sufficient to suppress warm
rain and elevate its initiation to above the 0 ∘C isotherm
level. This results in a suppression of rain from small clouds and an
invigoration in the deep clouds, as hypothesized by Rosenfeld et al. (2008).
While the net effect on rainfall amount is unknown, the redistribution of
rain intensities and the resulting vertical latent heating profiles are
likely to affect the regional hydrological cycle in ways that need to be
studied further.
The data used in this study are available at
https://halo-db.pa.op.dlr.de/mission/5 (DLR, 2014).
Cloud property uncertainties
The uncertainty calculations of cloud properties estimated from the CCP–CDP
probe were described in Braga et al. (2017). The uncertainties of re,
rv, rea, rva are ∼ 10 %, while for CWC and Mv the
uncertainties are about 30 %. The calculation of Na uncertainty is
described below.
Na uncertainty
The uncertainty of Na is calculated based on the uncertainty of slope
between CWCa and Mva. The two maximum and minimum acceptable slope lines of
Na can be used to estimate the uncertainty of the Na of the
best fit line. The principle behind this is that if we were to take another
complete set of data, we would find a new best fit slope. The maximum amount
by which it is likely to differ from our existing best fit slope is about
half the difference of the maximum and minimum acceptable slopes that we
have. This can be used as an uncertainty estimate:
Slopeuncertainty=[maximumslope-(minimumslope)]2.
The absolute values of Na uncertainty are shown at Table 3. The relative
uncertainty of Na values in mean terms is ∼ 21 % for
all flights analyzed.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-14433-2017-supplement.
The authors declare that they have no conflict of interest.
This article is part of the special issues “The ACRIDICON–CHUVA
campaign to study deep convective clouds and precipitation over Amazonia
using the new German HALO research aircraft (ACP/AMT inter-journal SI)”
and “BACCHUS – Impact of Biogenic versus Anthropogenic emissions
on Clouds and Climate: towards a Holistic UnderStanding (ACP/AMT/GMD
inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The first two authors of this study were supported by project BACCHUS,
European Commission FP7-603445. The generous support of the ACRIDICON–CHUVA
campaign by the Max Planck Society, the German Aerospace Center (DLR),
FAPESP (São Paulo Research Foundation), and the German Science
Foundation (Deutsche Forschungsgemeinschaft, DFG) within the DFG Priority
Program (SPP 1294) “Atmospheric and Earth System Research with the Research
Aircraft HALO (High Altitude and Long Range Research Aircraft)” is greatly
appreciated. This study was also supported by EU project HAIC under
FP7-AAT-2012-3.5.1-1. C. Mahnke and Ralf Weigel received funding from the German
Federal Ministry of Education and Research (BMBF, Bundesministerium für
Bildung und Forschung) within the joint ROMIC project SPITFIRE (01LG1205A).
In addition, the German Science Foundation contributed to supporting
this study (DFG SPP 1294 HALO,
contract no. VO1504/4-1 and contract no. JU 3059/1-1). The first author also acknowledges financial support from
the Brazilian funding agencies CAPES and CNPq during his PhD studies.
Edited by: Andrew Heymsfield
Reviewed by: Darrel Baumgardner, Ismail Gultepe, and two anonymous referees
ReferencesAlbrecht, R. I., Morales, C. A., and Silva Dias, M. A. F.: Electrification of
precipitating systems over the Amazon: Physical processes of thunderstorm
development, J. Geophys. Res., 116, D08209, 10.1029/2010JD014756,
2011.Andreae, M. O.: Correlation between cloud condensation nuclei concentration
and aerosol optical thickness in remote and polluted regions, Atmos. Chem.
Phys., 9, 543–556, 10.5194/acp-9-543-2009, 2009.Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo,
K. M., and Silva-Dias, M. A. F.: Smoking rain clouds over the Amazon,
Science, 303, 1337–1342, 10.1126/science.1092779, 2004.Andreae, M. O., Artaxo, P., Beck, V., Bela, M., Freitas, S., Gerbig, C.,
Longo, K., Munger, J. W., Wiedemann, K. T., and Wofsy, S. C.: Carbon monoxide
and related trace gases and aerosols over the Amazon Basin during the wet and
dry seasons, Atmos. Chem. Phys., 12, 6041–6065,
10.5194/acp-12-6041-2012, 2012.Artaxo, P., Martins, J. V., Yamasoe, M. A., Procópio, A. S., Pauliquevis,
T. M., Andreae, M. O., Guyon, P., Gatti, L. V., and Leal, A. M. C.: Physical
and chemical properties of aerosols in the wet and dry seasons in
Rondônia, Amazonia, J. Geophys. Res.-Atmos., 107, 1–14,
10.1029/2001JD000666, 2002.Artaxo, P., Rizzo, L. V, Brito, J. F., Barbosa, H. M. J., Arana, A., Sena,
E. T., Cirino, G. G., Bastos, W., Martin, S. T., and Andreae, M. O.:
Atmospheric aerosols in Amazonia and land use change: from natural biogenic
to biomass burning conditions, Faraday Discuss., 165, 203–235,
10.1039/C3FD00052D, 2013.
Baumgardner, D., Brenguier, J. L., Bucholtz, A., Coe, H., DeMott, P.,
Garrett, T. J., Korolev, A., Krämer, M., Petzold, A., Strapp, W.,
Pilewskie, P., Taylor, J., Twohy, C., Wendisch, M., Bachalo, W., and Chuang,
P.: Airborne instruments to measure atmospheric aerosol particles, clouds and
radiation: A cook's tour of mature and emerging technology, Atmos. Res., 102,
10–29, 2011.Beals, M. J., Fugal, J. P., Shaw, R. A., Lu, J., Spuler, S. M., and Stith, J.
L.: Holographic measurements of inhomogeneous cloud mixing at the centimeter
scale, Science, 350, 87–90, 10.1126/science.aab0751, 2015.Braga, R. C., Rosenfeld, D., Weigel, R., Jurkat, T., Andreae, M. O.,
Wendisch, M., Pöhlker, M. L., Klimach, T., Pöschl, U., Pöhlker,
C., Voigt, C., Mahnke, C., Borrmann, S., Albrecht, R. I., Molleker, S., Vila,
D. A., Machado, L. A. T., and Artaxo, P.: Comparing parameterized versus
measured microphysical properties of tropical convective cloud bases during
the ACRIDICON–CHUVA campaign, Atmos. Chem. Phys., 17, 7365–7386,
10.5194/acp-17-7365-2017, 2017.
Brenguier, J. L., Bachalo, W. D., Chuang, P. Y., Esposito, B. M., Fugal, J.,
Garrett, T., Gayet, J. F., Gerber, H., Heymsfield, A., Kokhanovsky, A.,
Korolev, A., Lawson, R. P., Rogers, D. C., Shaw, R. A., Strapp, W., and
Wendisch, M.: In Situ Measurements of Cloud and Precipitation Particles, in:
Airborne Measurements for Environmental Research: Methods and Instruments,
edited by: Wendisch, M. and Brenguier, J.-L., Weinheim, Germany,
225–301, 2013.Burnet, F. and Brenguier, J. L.: Observational Study of the Entrainment-Mixing
Process in Warm Convective Clouds, J. Atmos. Sci., 64, 1995–2011,
10.1175/JAS3928.1, 2007.Chen, R., Wood, R., Li, Z., Ferraro, R., and Chang, F.: Studying the vertical
variation of cloud droplet effective radius using ship and spaceborne remote
sensing data, J. Geophys. Res., 113, D00A02, 10.1029/2007JD009596, 2008.DLR (Deutsches Zentrum für Luft- und Raumfahrt): HALO database, available
at: https://halo-db.pa.op.dlr.de/mission/5 (last access:
4 December 2017), 2014.Fan, J., Leung, L. R., DeMott, P. J., Comstock, J. M., Singh, B., Rosenfeld,
D., Tomlinson, J. M., White, A., Prather, K. A., Minnis, P., Ayers, J. K.,
and Min, Q.: Aerosol impacts on California winter clouds and precipitation
during CalWater 2011: local pollution versus long-range transported dust,
Atmos. Chem. Phys., 14, 81–101, 10.5194/acp-14-81-2014,
2014.Freud, E. and Rosenfeld, D.: Linear relation between convective cloud drop
number concentration and depth for rain initiation, J. Geophys. Res.-Atmos.,
117, 1–13, 10.1029/2011JD016457, 2012.Freud, E., Rosenfeld, D., and Kulkarni, J. R.: Resolving both
entrainment-mixing and number of activated CCN in deep convective clouds,
Atmos. Chem. Phys., 11, 12887–12900,
10.5194/acp-11-12887-2011, 2011.Frey, W., Borrmann, S., Kunkel, D., Weigel, R., de Reus, M., Schlager, H.,
Roiger, A., Voigt, C., Hoor, P., Curtius, J., Krämer, M., Schiller, C.,
Volk, C. M., Homan, C. D., Fierli, F., Di Donfrancesco, G., Ulanovsky, A.,
Ravegnani, F., Sitnikov, N. M., Viciani, S., D'Amato, F., Shur, G. N.,
Belyaev, G. V., Law, K. S., and Cairo, F.: In situ measurements of tropical
cloud properties in the West African Monsoon: upper tropospheric ice clouds,
Mesoscale Convective System outflow, and subvisual cirrus, Atmos. Chem.
Phys., 11, 5569–5590, 10.5194/acp-11-5569-2011, 2011.
Gerber, H.: Microphysics of marine stratocumulus clouds with two drizzle
modes, J. Atmos. Sci., 53, 1649–1662, 1996.
Hallett, J. and Mossop, S. C. C.: Production of secondary ice particles
during the riming process, Nature, 249, 26–28, 1974.Khain, A., Rosenfeld, D., and Pokrovsky, A.: Aerosol impact on the dynamics
and microphysics of deep convective clouds, Q. J. Roy. Meteor. Soc., 131,
2639–2663,
10.1256/qj.04.62, 2005.Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.:
Notes on the state-of-the-art numerical modeling of cloud microphysics,
Atmos. Res., 55, 159–224, 10.1016/S0169-8095(00)00064-8, 2000.Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., Boer,
G. d., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster, M. J.,
Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C.,
Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M.,
Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M.,
Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C.,
Turner, D. D., Veron, D. E., Salzen, K. V., Walker, G. K., Wang, Z., Wolf, A.
B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model
simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic
Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135,
979–1002, 10.1002/qj.416, 2009.Konwar, M., Maheskumar, R. S., Kulkarni, J. R., Freud, E., Goswami, B. N.,
and Rosenfeld, D.: Aerosol control on depth of warm rain in convective
clouds, J. Geophys. Res.-Atmos., 117, 1–10, 10.1029/2012JD017585, 2012.Kuhn, U., Ganzeveld, L., Thielmann, A., Dindorf, T., Schebeske, G., Welling,
M., Sciare, J., Roberts, G., Meixner, F. X., Kesselmeier, J., Lelieveld, J.,
Kolle, O., Ciccioli, P., Lloyd, J., Trentmann, J., Artaxo, P., and Andreae,
M. O.: Impact of Manaus City on the Amazon Green Ocean atmosphere: ozone
production, precursor sensitivity and aerosol load, Atmos. Chem. Phys., 10,
9251–9282, 10.5194/acp-10-9251-2010, 2010.Lance, S.: Coincidence errors in a cloud droplet probe (CDP) and a cloud and
aerosol spectrometer (CAS), and the improved performance of a modified CDP,
J. Atmos. Ocean. Technol., 29, 1532–1541, 10.1175/JTECH-D-11-00208.1,
2012.Lance, S., Brock, C. A., Rogers, D., and Gordon, J. A.: Water droplet
calibration of the Cloud Droplet Probe (CDP) and in-flight performance in
liquid, ice and mixed-phase clouds during ARCPAC, Atmos. Meas. Tech., 3,
1683–1706, 10.5194/amt-3-1683-2010, 2010.Lee, G. W., Seed, A. W., and Zawadzki, I.: Modeling the variability of drop
size distributions in space and time, J. Appl. Meteorol. Climatol., 46,
742–756, 10.1175/JAM2505.1, 2007.Li, Z., Li, C., Chen, H., Tsay, S. C., Holben, B., Huang, J., Li, B., Maring,
H., Qian, Y., Shi, G., Xia, X., Yin, Y., Zheng, Y., and Zhuang, G.: East
Asian Studies of Tropospheric Aerosols and their Impact on Regional Climate
(EAST-AIRC): An overview, J. Geophys. Res.-Atmos., 116, D00K34,
10.1029/2010JD015257, 2011.
Liu, P. S. K., Leaitch, W. R., Strapp, J. W., and Wasey, M. A.: Response of
Particle Measuring Systems airborne ASASP and PCASP to NaCl and latex
particles, Aerosol Sci. Tech., 16, 83–95, 1992.Machado, L., Silva Dias, M., Morales, C., Fisch, G., Vila, D., Albrecht, R.,
Goodman. S., Calheiros, A., Biscaro, T., Kummerow, C., Cohen, J.,
Fitzjarrald, D., Nascimento, E., Sakamoto, M., Cunningham, C., Chaboureau,
J., Petersen, W., Adams, D., Baldini, L., Angelis, C., Sapucci, L., Salio,
P., Barbosa, H., Landulfo, E., Souza, R., Blakeslee, R., Bailey, J., Freitas,
S., Lima, W., and Tokay, A.: The Chuva Project: How Does Convection Vary
across Brazil?, B. Am. Meteorol. Soc., 95, 1365–1380,
10.1175/BAMS-D-13-00084.1, 2014.Mallaun, C., Giez, A., and Baumann, R.: Calibration of 3-D wind measurements
on a single-engine research aircraft, Atmos. Meas. Tech., 8, 3177–3196,
10.5194/amt-8-3177-2015, 2015.Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch,
G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva
Dias, M. A., Smith, J. N., and Wendisch, M.: Introduction: Observations and
Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16,
4785–4797, 10.5194/acp-16-4785-2016, 2016.
Paluch, I. R.: The entrainment mechanism in Colorado cumili, J. Atmos. Sci.,
36, 2467–2478, 1979.Pinsky, M. B. and Khain, A.: Effects of in-cloud nucleation and turbulence on
droplet spectrum formation in cumulus clouds, Q. J. Roy. Meteor. Soc., 128,
501–533, 10.1256/003590002321042072, 2002.Pöhlker, M. L., Pöhlker, C., Ditas, F., Klimach, T., Hrabe de
Angelis, I., Araújo, A., Brito, J., Carbone, S., Cheng, Y., Chi, X.,
Ditz, R., Gunthe, S. S., Kesselmeier, J., Könemann, T., Lavric, J. V.,
Martin, S. T., Mikhailov, E., Moran-Zuloaga, D., Rose, D., Saturno, J., Su,
H., Thalman, R., Walter, D., Wang, J., Wolff, S., Barbosa, H. M. J., Artaxo,
P., Andreae, M. O., and Pöschl, U.: Long-term observations of cloud
condensation nuclei in the Amazon rain forest – Part 1: Aerosol size
distribution, hygroscopicity, and new model parametrizations for CCN
prediction, Atmos. Chem. Phys., 16, 15709–15740,
10.5194/acp-16-15709-2016, 2016.Pöschl, U., Martin, S. T., Sinha, B., Chen, Q., Gunthe, S. S., Huffman,
J. A., Borrmann, S., Farmer, D. K., Garland, R. M., Helas, G., Jimenez, J.
L., King, S. M., Manzi, A., Mikhailov, E., Pauliquevis, P., Petters, M. D.,
Prenni, A. J., Roldin, P., Rose, D., Schneider, J., Su, H., Zorn, S. R.,
Artaxo, P., and Andreae, M. O.: Rainforest Aerosols as Biogenic Nuclei of
Clouds and Precipitation in the Amazon, Science, 329, 1513–1516,
10.1126/science.1191056, 2010.
Prabha, T. V., Khain, A., Maheshkumar, R. S., Pandithurai, G., Kulkarni, J.
R., Konwar, M., and Goswami, B. N.: Microphysics of premonsoon and monsoon clouds
as seen from i n situ measurements during the Cloud Aerosol Interaction and
Precipitation Enhancement Experiment (CAIPEEX), J. Atmos. Sci., 68, 1882–1901, 2011.Pruppacher, H. R., Klett, J. D., and Wang, P. K.: Microphysics of Clouds and
Precipitation, Aerosol Sci. Technol., 28, 381–382,
10.1080/02786829808965531, 1998.Ramanathan, V., Crutzen, P. J., Lelieveld, J., Mitra, A. P., Althausen, D.,
Anderson, J., Andreae, M. O., Cantrell, W., Cass, G. R., Chung, C. E.,
Clarke, A. D., Coakley, J. A., Collins, W. D., Conant, W. C., Dulac, F.,
Heintzenberg, J., Heymsfield, A. J., Holben, B., Howell, S., Hudson, J.,
Jayaraman, A., Kiehl, J. T., Krishnamurti, T. N., Lubin, D., McFarquhar, G.,
Novakov, T., Ogren, J. A., Podgorny, I. A., Prather, K., Priestley, K.,
Prospero, J. M., Quinn, P. K., Rajeev, K., Rasch, P., Rupert, S., Sadourny,
R., Satheesh, S. K., Shaw, G. E., Sheridan, P., and Valero, F. P. J.: Indian
Ocean Experiment: An integrated analysis of the climate forcing and effects
of the great Indo-Asian haze, J. Geophys. Res., 106, 28371,
10.1029/2001JD900133, 2001.Rosenfeld, D.: Suppression of Rain and Snow by Urban and Industrial Air
Pollution, Science, 287, 1793–1796,
10.1126/science.287.5459.1793, 2000.Rosenfeld, D. and Gutman, G.: Retrieving microphysical properties near the
tops of potential rain clouds by multispectral analysis of AVHRR data,
Atmos. Res., 34, 259–283, 10.1016/0169-8095(94)90096-5, 1994.Rosenfeld, D. and Lensky, I. M.: Satellite-Based Insights into Precipitation
Formation Processes in Continental and Maritime Convective Clouds, B. Am.
Meteorol. Soc., 79, 2457–2476,
10.1175/1520-0477(1998)079<2457:SBIIPF>2.0.CO;2, 1998.Rosenfeld, D. and Woodley, W. L. W.: Deep convective clouds with sustained
supercooled liquid water down to -37.5 degrees C, Nature, 405, 440–442,
2000.Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi,
S., Reissell, A., and Andreae, M. O.: Flood or drought: how do aerosols
affect precipitation?, Science, 321, 1309–1313,
10.1126/science.1160606, 2008.Rosenfeld, D., Yu, X., Liu, G., Xu, X., Zhu, Y., Yue, Z., Dai, J., Dong, Z.,
Dong, Y., and Peng, Y.: Glaciation temperatures of convective clouds
ingesting desert dust, air pollution and smoke from forest fires, Geophys.
Res. Lett., 38, 2006–2010, 10.1029/2011GL049423, 2011.Rosenfeld, D., Wang, H., and Rasch, P. J.: The roles of cloud drop effective
radius and LWP in determining rain properties in marine stratocumulus,
Geophys. Res. Lett., 39, 1–6, 10.1029/2012GL052028, 2012a.Rosenfeld, D., Williams, E., Andreae, M. O., Freud, E., Pöschl, U., and
Rennó, N. O.: The scientific basis for a satellite mission to retrieve
CCN concentrations and their impacts on convective clouds, Atmos. Meas.
Tech., 5, 2039–2055, 10.5194/amt-5-2039-2012, 2012b.Rosenfeld, D., Andreae, M. O., Asmi, A., Chin, M., De Leeuw, G., Donovan, D.
P., Kahn, R., Kinne, S., Kiveks, N., Kulmala, M., Lau, W., Schmidt, K. S.,
Suni, T., Wagner, T., Wild, M., and Quaas, J.: Global observations of
aerosol-cloud-precipitation-climate interactions, Rev. Geophys., 52,
750–808, 10.1002/2013RG000441, 2014a.Rosenfeld, D., Fischman, B., Zheng, Y., Goren, T., and Giguzin, D.: Combined
satellite and radar retrievals of drop concentration and CCN at convective
cloud base, Geophys. Res. Lett., 41, 3259–3265, 10.1002/2014GL059453,
2014b.Rosenfeld, D., Liu, G., Yu, X., Zhu, Y., Dai, J., Xu, X., and Yue, Z.:
High-resolution (375 m) cloud microstructure as seen from the NPP/VIIRS
satellite imager, Atmos. Chem. Phys., 14, 2479–2496,
10.5194/acp-14-2479-2014, 2014c.Segal, Y., Pinsky, M., and Khain, A.: The role of competition effect in the
raindrop formation, Atmos. Res., 83, 106–118,
10.1016/j.atmosres.2006.03.007, 2007.Tao, W., Chen, J., and Li, Z.: Impact of aerosols on convective clouds and
precipitation, Rev. Geophys., 50, RG2001, 10.1029/2011RG000369, 2012.Twomey, S.: The nuclei of natural cloud formation part II: The
supersaturation in natural clouds and the variation of cloud droplet
concentration, Geofis. Pura e Appl., 43, 243–249, 10.1007/BF01993560,
1959.
VanZanten, M., Stevens, B., Vali, G., and Lenschow, D.: Observations of
drizzle in nocturnal marine stratocumulus, J. Atmos. Sci., 62, 88–106, 2005.Voigt, C., Schumann, U., Jurkat, T., Schäuble, D., Schlager, H., Petzold,
A., Gayet, J.-F., Krämer, M., Schneider, J., Borrmann, S., Schmale, J.,
Jessberger, P., Hamburger, T., Lichtenstern, M., Scheibe, M., Gourbeyre, C.,
Meyer, J., Kübbeler, M., Frey, W., Kalesse, H., Butler, T., Lawrence, M.
G., Holzäpfel, F., Arnold, F., Wendisch, M., Döpelheuer, A.,
Gottschaldt, K., Baumann, R., Zöger, M., Sölch, I., Rautenhaus, M.,
and Dörnbrack, A.: In-situ observations of young contrails – overview
and selected results from the CONCERT campaign, Atmos. Chem. Phys., 10,
9039–9056, 10.5194/acp-10-9039-2010, 2010.Voigt, C., Schumann, U., Jessberger, P., Jurkat, T., Petzold, A., Gayet, J.-F., Krämer, M.,
Thornberry, T., and Fahey, D. W.: Extinction and
optical depth of contrails, Geophys. Res. Lett., 38, L11806,
10.1029/2011GL047189, 2011.Voigt, C., Schumann, U., Minikin, A., Abdelmonem, A., Afchine, A., Borrmann,
S., Boettcher, M., Buchholz, B., Bugliaro, L., Costa, A., Curtius, J.,
Dollner, M., Dörnbrack, A., Dreiling, V., Ebert, V., Ehrlich, A., Fix,
A., Forster, L., Frank, F., Fütterer, D., Giez, A., Graf, K., Grooß,
J., Groß, S., Heimerl, K., Heinold, B., Hüneke, T., Järvinen, E.,
Jurkat, T., Kaufmann, S., Kenntner, M., Klingebiel, M., Klimach, T., Kohl,
R., Krämer, M., Krisna, T., Luebke, A., Mayer, B., Mertes, S., Molleker,
S., Petzold, A., Pfeilsticker, K., Port, M., Rapp, M., Reutter, P., Rolf, C.,
Rose, D., Sauer, D., Schäfler, A., Schlage, R., Schnaiter, M., Schneider,
J., Spelten, N., Spichtinger, P., Stock, P., Walser, A., Weigel, R.,
Weinzierl, B., Wendisch, M., Werner, F., Wernli, H., Wirth, M., Zahn, A.,
Ziereis, H., and Zöger, M.: ML-CIRRUS – The airborne experiment on
natural cirrus and contrail cirrus with the high-altitude long-range research
aircraft HALO, B. Am. Meteorol. Soc., 98, 271–288, 10.1175/BAMS-D-15-00213.1, 2017.Wendisch, M., Pöschl, U., Andreae, M. O., Machado, L. A. T., Albrecht,
R., Schlager, H., Rosenfeld, D., Martin, S. T., Abdelmonem, A., Afchine, A.,
Araùjo, A., Artaxo, P., Aufmhoff, H., Barbosa, H. M. J., Borrmann, S.,
Braga, R., Buchholz, B., Cecchini, M. A., Costa, A., Curtius, J., Dollner,
M., Dorf, M., Dreiling, V., Ebert, V., Ehrlich, A., Ewald, F., Fisch, G.,
Fix, A., Frank, F., Fütterer, D., Heckl, C., Heidelberg, F., Hüneke,
T., Jäkel, E., Järvinen, E., Jurkat, T., Kanter, S., Kästner,
U., Kenntner, M., Kesselmeier, J., Klimach, T., Knecht, M., Kohl, R.,
Kölling, T., Krämer, M., Krüger, M., Krisna, T. C., Lavric, J.
V., Longo, K., Mahnke, C., Manzi, A. O., Mayer, B., Mertes, S., Minikin, A.,
Molleker, S., Münch, S., Nillius, B., Pfeilsticker, K., Pöhlker, C.,
Roiger, A., Rose, D., Rosenow, D., Sauer, D., Schnaiter, M., Schneider, J.,
Schulz, C., de Souza, R. A. F., Spanu, A., Stock, P., Vila, D., Voigt, C.,
Walser, A., Walter, D., Weigel, R., Weinzierl, B., Werner, F., Yamasoe, M.
A., Ziereis, H., Zinner, T., and Zöger, M.: The ACRIDICON-CHUVA campaign:
Studying tropical deep convective clouds and precipitation over Amazonia
using the new German research aircraft HALO, B. Am. Meteorol. Soc.,
97, 1885–1908,
10.1175/BAMS-D-14-00255.1, 2016.
Williams, E., Rosenfeld, D., Madden, N., Gerlach, J., Gears, N., Atkinson,
L., Dunnemann, N., Frostrom, G., Antonio, M., Biazon, B., Camargo, R.,
Franca, H., Gomes, A., Lima, M., Machado, R., Manhaes, S., Nachtigall, L.,
Piva, H., Quintiliano, W., Machado, L., Artaxo, P., Roberts, G., Renno, N.,
Blakeslee, R., Bailey, J., Boccippio, D., Betts, A., Wolff, D., Roy, B.,
Halverson, J., Rickenbach, T., Fuentes, J., and Avelino, E.: Contrasting
convective regimes over the Amazon: Implications for cloud electrification,
J. Geophys. Res., 107, 1–19, 10.1029/2001JD000380, 2002.