Introduction
Clouds are a key factor in the Earth's atmosphere and climate system (Bony
et al., 2015). Thus, sound scientific knowledge on the life cycle and highly
dynamic properties of clouds is of significant importance for our
understanding of atmospheric cycling and climate change (Seinfeld et al.,
2016). A number of recent overview studies summarize the various facets of
aerosol–cloud–precipitation–climate interactions in a detailed and
comprehensive way (e.g., Andreae and Rosenfeld, 2008; Tao et al., 2012;
Rosenfeld et al., 2014).
The Amazon Basin and its unique rain forest ecosystem are fundamentally
shaped by the intense and large-scale (re)circulation of water between
biosphere and atmosphere. Accordingly, the life cycle of shallow and deep
convective clouds in the Amazon has been subject of numerous previous studies
(e.g., Andreae et al., 2004; Freud et al., 2008; Rosenfeld et al., 2016;
Wendisch et al., 2016; Braga et al., 2017). In particular, the extent of
anthropogenic influence on the cloud life cycle through continuous land use
change and combustion-related aerosol emissions has been actively debated
(e.g., Roberts et al., 2003; Davidson et al., 2012; Goncalves et al., 2015).
It is well established that the properties and dynamics of clouds can be
fundamentally altered by changing cloud condensation nuclei (CCN) regimes,
which are a fraction of the total (tropospheric) aerosol population (e.g.,
Rosenfeld et al., 2008; Reutter et al., 2009).
To explore essential biogeochemical processes, such as aerosol–cloud
interactions, in the Amazon rain forest, the Amazon Tall Tower Observatory
(ATTO) was established in 2010/11 (for details see Andreae et al., 2015). The
central Amazon Basin is characterized by a pronounced seasonality in
atmospheric composition in response to the north–south oscillation of the
Intertropical Convergence Zone (ITCZ). During the wet season, the ATTO site
receives comparatively clear air masses of marine origin from the northeast
that travel over mostly untouched rain forest areas, whereas during the dry
season strongly polluted air masses are advected from the southeast,
originating from numerous fires in the Amazon's arc of deforestation (for
details see C. Pöhlker et al., 2018). Detailed information on
characteristic differences in the atmospheric state and processes for the
contrasting wet- vs. dry-season conditions in the ATTO region can be found in
a number of recent studies (e.g., Nölscher et al., 2016; Pöhlker et
al., 2016; Moran-Zuloaga et al., 2017; Saturno et al., 2017a;
Yañez-Serrano et al., 2018).
In terms of microphysical processes in cloud formation and development, the
number concentration of CCN, NCCN(S), and the peak water vapor
supersaturation, S, at the cloud base play a key role. Here, S is
predominantly determined by the updraft velocity, wb, of the
adiabatically rising air parcel at the cloud base. The relevant peak S
levels in the Amazon are assumed to range from ∼ 0.1 to
∼ 1.0 % (e.g., Andreae, 2009; Reutter et al., 2009; Pöschl et
al., 2010; Pöhlker et al., 2012; Farmer et al., 2015; Pöhlker et al.,
2016). Moreover, a recent study suggests that substantially higher S
(≫ 1 %) can also be reached in deep convective clouds under certain
conditions (Fan et al., 2018). However, a systematic and quantitative
assessment of relevant peak S distributions in the Amazonian atmosphere is
still lacking (see discussion in Sect. 3.9). Depending on NCCN(S)
and S, a certain number of cloud droplets at the cloud base,
Ndb, are formed (Rosenfeld et al., 2016). In the Amazon Basin,
Ndb ranges from a few hundred droplets per cubic centimeter for
clean conditions to 1000 and 2000 cm-3 for polluted conditions
(Pöschl et al., 2010; Rosenfeld et al., 2016; Braga et al., 2017). Upon
cloud development and rising air masses, the initial droplets grow by
condensation of water vapor, which can be observed as changes in the cloud
drop size distribution (DSD). Thus, the DSD is a function of thermodynamic
parameters (i.e., the updraft velocity, wb), aerosol conditions
(i.e., NCCN(S)), and the cloud evolution (i.e., the cloud depth,
H). Important bulk DSD properties are, in particular, the droplet number
concentration, Nd, and the effective droplet radius,
re. For re > 11 µm, the
probability of droplet collision and coalescence processes increases to
significant levels, and warm rain formation is initiated (Cecchini et al.,
2017a).
A detailed analysis of the properties and variability of the Amazonian CCN
population is a prerequisite for the understanding of cloud cycling in this
region. However, the CCN data from the basin is still sparse. Therefore, we
conducted a systematic characterization of the trends and properties of the
central Amazonian CCN population at the ATTO site. The first half of this
study has been published recently as Part 1 (Pöhlker et al., 2016).
The present paper represents Part 2 and focuses on the variability and
properties of periods and conditions that are characteristic for the
Amazonian atmosphere.
Brief summary of the Part 1 companion paper
The Part 1 paper (Pöhlker et al., 2016) focuses on the multi-month
variability in the Amazonian CCN population by presenting data from a full
seasonal cycle. In particular, it presents annual averages of the key CCN
parameters, a detailed analysis of the specific seasonal as well as diurnal
cycles, and a systematic analysis of different CCN parametrization schemes to
represent the Amazonian CCN cycling in modeling studies.
The major findings of Part 1 can be summarized as follows: (i) the CCN
population in the central Amazon is predominantly defined by the overall
aerosol concentration as well as the shape of the characteristic bimodal
aerosol size distribution. Accordingly, a key property that has to be taken
into account for the interpretation of the CCN results is the relative
proportion of the Aitken and accumulation modes (mode maxima at
DAit ≈ 70 and DAcc ≈ 150 nm). (ii) The
hygroscopicity parameters, κ(S,Da) with Da as the midpoint
activation diameter, of the two modes were found to be remarkably stable for
most of the observation period (κAit = 0.14 ± 0.03 vs.
κAcc = 0.22 ± 0.05), with only weak seasonal and no
diurnal variability. Accordingly, we concluded in Part 1 that the shape of the
aerosol size distribution is the predominant factor, whereas κ(S,Da) is only of secondary importance for the variability in the
Amazonian CCN population, in agreement with previous studies (see references
in Pöhlker et al., 2016). (iii) Furthermore, Part 1 summarizes the
CCN key parameters that allow for efficient modeling of the Amazonian CCN
population. The prediction of CCN concentrations is particularly reliable
when time series of total aerosol concentration and/or the aerosol size
distribution are available.
We emphasized CCN efficiency spectra, which can be regarded as CCN
signatures for a particular aerosol population, by describing their behavior
for the atmospherically relevant S range. Here, a rather simple analytical
expression (i.e., single- or double-error-function fits) suffices to
represent the essence of the CCN-relevant properties of an aerosol
population, which includes the characteristic shape of the aerosol size
distribution and the κ(S,Da) size dependence. Furthermore, the
CCN efficiency spectra are independent of the total aerosol number
concentration (in contrast to CCN spectra) and, thus, can be flexibly scaled
to the concentration range of interest to obtain CCN concentrations at a
certain S level. Finally, and beyond their potential use in models as CCN
parametrization, the shape of the CCN efficiency spectra is very instructive
for visualization of the specific behavior of contrasting aerosol population in
cloud formation. This aspect will be one focal point of the present study.
Aims and scope of this study
To complete the analysis started in Part 1, this paper analyzes the CCN
variability at the original time resolution (∼ 4.5 h), which is
sufficient to resolve its short-term variability in relation to air mass
changes as well as aerosol emission and transformation processes. In the
present work, we will zoom into specific periods of the 1-year CCN data
set in two steps: first, we discuss the aerosol and CCN variability for two
contrasting 2-month periods that characterize the pollution minimum and
maximum in relation to complementary trace gas and aerosol parameters.
Second, we analyze the following four case studies, which represent
characteristic events and conditions in the central Amazon region:
During certain wet-season episodes, when no tracers of pollution aerosols
are detectable anymore, the aerosol population can be regarded as
empirically not distinguishable from pristine, i.e., completely unpolluted
rain forest conditions. This empirically pristine state of the rain forest
(PR) aerosol prevails during 10 to 40 days per year (depending on PR
definition; see Sect. 2.7).
Long-range-transport (LRT) aerosol advection during the wet season brings
Saharan dust, African biomass burning smoke, and marine aerosol
particles from the transatlantic passage. The LRT case study represents
conditions that prevail between 50 and 60 days per year (see Moran-Zuloaga
et al., 2017).
Biomass burning (BB) smoke from man-made forest fires in the various
deforestation hot spots in the basin influences the atmospheric state at ATTO
almost permanently during the dry season and for extended episodes during
the transition periods (> 100 days per year) (Saturno et al.,
2017a). The BB case study in this work analyzes large deforestation fires in
the southeastern basin, whose smoke reached ATTO after a few days of
atmospheric processing. Accordingly, the BB case study characterizes the
typical conditions of aged smoke influencing the atmospheric state at ATTO.
Mixed pollution (MPOL) from African LRT and local/regional fires represents a
frequent aerosol scenario at ATTO (Saturno et al., 2017a). The advected
African aerosols mainly comprise biomass and fossil fuel combustions
emissions, although the exact composition of these dry-season LRT plumes is
still poorly analyzed. The MPOL case study focuses on a period when African
volcanogenic aerosols were advected to ATTO – an event that has been well
documented in Saturno et al. (2017b). We selected this episode since the
microphysical properties of the volcanogenic aerosol are characteristic
enough to discriminate them from the local/regional smoke emissions.
Accordingly, the alternating pattern of LRT vs. local/regional pollution can be
clearly resolved for the MPOL period. However, note that volcanogenic plumes are
comparatively rare events, whereas African combustion emissions, which are
much harder to discriminate from the local/regional emissions, are a more
common scenario. Accordingly, the MPOL case study is an example of a complex
aerosol mixture due to alternating African vs. local/region influences
during the dry season.
In summary, the overall purpose of this study is to link the measured CCN
abundance and properties with the characteristic emissions and
transformation processes that govern the Amazonian aerosol population. With
the CCN parametrization strategies developed in Part 1, we provide a basis
for effective CCN prediction under characteristic aerosol and CCN conditions
in the Amazon Basin.
Experimental section
Aerosol and trace gas measurements at the ATTO site
The present study is mostly based on in situ measurements at the remote ATTO site,
which has been described in detail by Andreae et al. (2015). Further
relevant information regarding the site, the measurement period, and the
aerosol and trace gas instrumentation can be found in the Part 1 paper by
Pöhlker et al. (2016). The time frame of the present analysis, including
the four in-depth case studies, overlapped with the two intensive
observation periods (IOPs) of the international Green Ocean Amazon 2014/5
(GoAmazon2014/5) campaign (Martin et al., 2016a, b). Specific details on
the measurements of equivalent black carbon (BCe) mass concentrations,
MBCe, with the Multiangle Absorption Photometer (MAAP) can be found in
Saturno et al. (2017a, c). Specific details on the measurements of the
mole fraction of carbon monoxide (CO), cCO, with a G1302 analyzer
(Picarro Inc. Santa Clara, CA, USA) can be found in Winderlich et al. (2010).
Details on the Aerosol Chemical Speciation Monitor (ACSM, Aerodyne Research
Inc., Billerica, MA, USA) measurements – which provide online information on
the mass concentrations, Mspecies, of organics (OA), sulfate
(SO42-), nitrate (NO3-), ammonium (NH4+), and
chloride (Cl-) – can be found in Ng et al. (2011). A detailed
description of the long-term operation of the ACSM at the ATTO site can be
found in Carbone et al. (2017). For the selected case study and seasonal
time frames, we calculated the mean values of Mspecies as well as
corresponding mass fractions, fspecies, according to
fspecies=MspeciesMOA+MSO4+MNH4+MNO3+MCl+MBCe.
Furthermore, predicted hygroscopicity parameters, κp, were
calculated based on the ACSM and MAAP results according to the following
procedure adapted from Gunthe et al. (2009), Rose et al. (2011), and
Pöhlker et al. (2016):
κp=fOA⋅0.1+finorg⋅0.71+fBCe⋅0,
with finorg including SO42-, NO3-, NH4+,
and Cl-. Note that MNH4 ranged below its detection limit (i.e.,
0.28 µg m-3, for 30 min averaging time) during the clean Amazon
wet-season months, making the obtained results unreliable (the questionable
periods are marked in Table 3, which is discussed later in this study). For
these periods with questionable results, MNH4 was omitted in the
calculation of the mass fractions, which has to be kept in mind in the
interpretation of the results. MCl and MBCe were also below
detection limits for certain conditions. Accordingly, κp was
calculated without MNH4, MCl-, and/or MBCe. The nominal
size range of the ACSM spans from 75 to 650 nm, and the measurements are
conducted size-integrated. Accordingly, the ACSM results tend to be
dominated by larger particles with relatively high masses, which makes the
Mspecies results mostly representative for the accumulation mode
composition. Accordingly, the calculated κp was compared to the
hygroscopicity parameter for the lowest measured S level, κ(0.11 %), corresponding to the largest measured critical diameter
(Da≈ 170 nm).
CCN measurements and data analysis
A detailed description of the operation of the CCN counter (CCNC) and the
subsequent data analysis can be found in the companion paper (Pöhlker
et al., 2016), which is the basis for the present study. Briefly,
size-resolved CCN measurements were conducted using a continuous-flow
streamwise thermal-gradient CCN counter (model CCN-100, DMT, Longmont, CO,
USA) in combination with a differential mobility analyzer (DMA, model M,
Grimm Aerosol Technik, Ainring, Germany) and a condensation particle counter
(Grimm Aerosol Technik). The DMA-selected size range spans from 20 to 245 nm.
The analyzed supersaturation range spans from 0.11 to 1.10 %. A
complete measurement cycle with scanning of all particle diameters and
supersaturations took ∼ 4.5 h. For further CCN-relevant
information, we refer the reader to Rose et al. (2008) and Krüger et al. (2014).
The CCN efficiency spectra parameterization as introduced in Part 1 plays a
key role in the present paper. Note that we slightly revised and
improved the fitting procedure from the Part 1 companion paper. The main
change implies that the fits are now forced through zero, which is
physically more plausible and makes the single-error-function (erf) fit
parametrization more applicable for modeling studies. The erf fit
(mode = 1) is represented by the following function:
NCCN(S)NCN,10=a12+a12⋅erflnSS1w1,
with a1 as a prefactor; S1 as the supersaturation, at which half of
the maximum activated fraction (MAF) of the aerosol particles acts as CCN
(e.g., 50 % for a1 = 1); and w1 as the width of the erf fit. To
simplify the fitting procedure, a1 = 1 was assumed. For
a1 = 1 the erf converges against unity, corresponding to an activation
of all particles as CCN at high S, which is adequate in most cases.
Analogously, the double-erf fit (mode = 2) is represented by the function
NCCN(S)NCN,10=a12+a22⋅erflnSS1w1+a1-a22⋅erflnSS2w2,
with index 1 and 2 specifying the variables for both modes. To simplify
the fitting procedure, a1 = 1 was assumed.
Note further that in Part 1 we tested different reference aerosol number
concentrations, NCN,Dcut (e.g., NCN,10 and NCN,50), for the CCN
efficiency spectra parametrization. In this study, we use only one reference
concentration for clarity – namely NCN,10. The choice of NCN,10 can
be explained by the fact that it is experimentally rather easily accessible
(e.g., via condensation particle counter, CPC, measurements), whereas
reference concentrations such as NCN,50 require more elaborated
experimental setups (e.g., Scanning Mobility Particle Sizer, SMPS, data).
The κ distributions were calculated according to the procedure
reported in Su et al. (2010) for every individual CCN measurement cycle and
subsequently averaged for time periods of interest. The corresponding
NCN κ distributions were obtained by multiplication of the
average κ distributions with the average NCN size distributions
within the same time frame. The entire CCN analysis was conducted in
IGOR Pro (Wavemetrics, Inc., Portland, OR, USA).
Backward trajectories
The backward-trajectory (BT) analysis and classification in this study has
been adopted from C. Pöhlker et al. (2018), where an in-depth description
of the procedure can be found. Briefly, the BT analysis is based on the
Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT,
NOAA-ARL) with meteorological input data from the Global Data Assimilation
System (GDAS1) (Draxler and Hess, 1998). Three-day BTs have been calculated
every 1 h with a starting height of 1000 m above ground level (a.g.l.) at
the ATTO site for the time period of 1 January 2008 until 30 June 2016. A
sensitivity test confirmed that starting heights of the BTs at 200 and
1000 m a.g.l. gave similar results. Accordingly, the chosen start height at
1000 m appears to be a good representation of the origin of the boundary
layer (BL) air masses at ATTO. Subsequently, the resulting ensemble of
74 496 individual BTs was classified into 15 clusters with k-means cluster
analysis (CA). Figure S1 in the Supplement shows a map of the northeastern
Amazon Basin with the ATTO site and the mean track of the 15 BT clusters. It
illustrates that the air masses arrive almost exclusively in a rather narrow
easterly wind sector (between 45 and 120∘). Within this sector, four
main directions of air mass advection can be identified: (i) a northeasterly
(NE) track including the clusters NE1, NE2, and NE3; (ii) an
east-northeasterly (ENE) track including the clusters ENE1, ENE2, ENE3, and
ENE4; (iii) an easterly (E) track including the clusters E1, E2, E3, and E4;
and (iv) a group of “inland” trajectories in east-southeasterly (ESE)
directions including clusters ESE1, ESE2, and ESE3 as well as one cluster
towards the southwest (SW1). For a detailed characterization of the land
cover, including potential trace gas, aerosol, and CCN sources, within the
BT-derived footprint region of the ATTO site, we refer the reader to
C. Pöhlker et al. (2018).
Satellite data and analysis
The satellite data products used in this study were obtained from the NASA
Giovanni web interface (http://giovanni.gsfc.nasa.gov/; last access: 26 May
2017), developed and maintained by the NASA Goddard Earth Sciences Data and
Information Services Center (GES DISC) (Acker and Leptoukh, 2007). The
following satellite products were used:
Cloud top temperature data (AIRX3STD_v006 product) from the
atmospheric infrared sounder (AIRS) instruments on board the satellites
Terra and Aqua (data included from 4 July 2002 to 30 June 2016). For the
corresponding time series in this study, the Aqua and Terra data were
averaged per day for a representative region (i.e., ROIATTO; see
below).
Cloud cover data were obtained by the Moderate Resolution Imaging
Spectroradiometer (MODIS) on board the Terra and Aqua satellites
(included data from 4 July 2002 to 30 June 2016). The obtained Aqua and Terra
time series were averaged for the ROIATTO. Note that cloud cover
strongly depends on the spatial resolution of the instrument as outlined in
King et al. (2013).
Cloud droplet effective radius, re, data were calculated from MODIS products
(included data from 4 July 2002 to 30 June 2016) for the ROIATTO. Since
re varies with vertical cloud development and total CCN abundance, we
filtered the re data by cloud top temperature (King et al., 2013).
Precipitation rate data, PTRMM, are obtained from the Tropical Rainfall
Measuring Mission (TRMM) within the ROIATTO. The TRMM_3B42_Daily_v7 product was used for the
time period 1 January 1998 until 30 June 2016.
The satellite data were used as time series of area-averaged data products
within a region of interest around the ATTO site (ROIATTO:
3.5∘ S–2.4∘ N, 59.5–54∘ W) as specified in Fig. S1.
Seasonal cycles of remote-sensing and in situ data
To provide an overall picture of the seasonal cycle of selected aerosol,
meteorological, and cloud microphysical parameters representative for the
ATTO region, various multi-year data sets were analyzed and compared.
Remote-sensing data products were used in the time frames outlined in
Sect. 2.4. For MBCe, cCO, and the particle
concentrations in the accumulation mode range (Nacc), in the
Aitken mode range (NAit), and in the total particle population
(NCN,10), 4–6 years of ATTO site measurements were available.
Additionally, MBCe data measured at the ZF2 site,
located 40 km northwest of Manaus, were used to reflect the conditions back
to 2008 (e.g., Martin et al., 2010a). In terms of sources and conditions, the
ATTO and ZF2 sites are comparable (Saturno et al., 2017a; C. Pöhlker et
al., 2018). Accordingly, combined MBCe time series
from ATTO and ZF2 spanning from January 2008 to May 2017 were included here.
The cCO data include ATTO measurements from March 2012 to April
2017. The Nacc, NAit, and NCN,10 data are
based on SMPS measurements at ATTO from February 2014 to January 2017.
Aerosol sampling and scanning electron microscopy with X-ray
spectroscopy
Aerosol samples for electron microspectroscopy were collected by impaction.
A homemade single-stage impactor (flow rate = 1–1.5 L min-1; nominal
cut-off: Dcut≈ 500 nm) was used for collection. The collection
efficiency below Dcut decreases steeply; however, a certain fraction of
particles in this size range is still collected. Moreover, a fraction of
very small particles is additionally collected via diffusive deposition and
therefore available for the microscopic analysis. Aerosol particles were
deposited onto silicon nitride substrates (Si3N4; membrane
width: 500 µm; membrane thicknesses: 100 or 150 nm; Silson Ltd.,
Northhampton, UK). Immediately after sampling, samples were stored in airtight containers at
-20 ∘C.
Without further treatment like sputter coating, particles were analyzed in a
high-resolution scanning electron microscope (SEM; FEI Quanta 200F, FEI,
Eindhoven, the Netherlands). An acceleration voltage of 12.5 kV with a spot
size of approximately 3 nm was used. X-ray emission was analyzed using
energy-dispersive X-ray analysis (EDX; Edax Genesis, Edax Inc.). The system
is able to record characteristic X-ray emissions for all elements with Z > 5.
Obviously, in the present work Si could not be quantified
due to the Si3N4 substrate (Kandler et al., 2011).
Definition of empirically pristine rain forest aerosol conditions at
ATTO
The term pristine is bound to a pre-human reference state with prevailing
natural atmospheric conditions, in the absence of any anthropogenic
influences. Andreae (2007) pointed out that in the present-day atmosphere
“there are no places where we can expect to find truly pristine
conditions”. This is particularly true with respect to long-lived trace
gases, such as CO2 and CH4, which have accumulated in the
atmosphere due to man-made activities. However, the aerosol abundance
and composition are also substantially perturbed by anthropogenic emissions. This
also includes aerosols at remote locations, which are altered to varying
degrees by a globally pervasive background pollution. It has been
controversially debated if and to what extent certain marine and remote
continental locations still approximate pristine atmospheric conditions
(e.g., Andreae, 2009; Martin et al., 2010b; Pöschl et al., 2010; Chi et
al., 2013; Hamilton et al., 2014). Frequently, conditions with low
anthropogenic influences are characterized as “clean” or “near pristine”,
although these terms are rather differently and often vaguely defined. The
discussion is inherently difficult since truly pristine conditions are not
available anymore to quantify how close the present-day atmosphere still
gets to its original pre-human state. Accordingly, truly pristine conditions
remain hypothetical, and any degree of a near-pristine state has to be
defined indirectly with respect to the absence of man-made emissions.
In the Amazon Basin, aerosol conditions during most of the wet season are
comparatively clean with low – though detectable – concentrations of
background pollution. During the multi-month wet-season period that can
overall be characterized as near pristine, certain episodes can be
identified where pollution concentrations drop even further and below
analytical detection limits. Under these conditions, the aerosol composition
can be regarded as empirically undistinguishable from a pristine state. The
present study uses these episodes to operationally define a pristine state
of the aerosol composition and properties in a tropical rain forest
environment, which we call empirically pristine rain forest (PR) aerosol.
The selection of a robust marker is crucial for the assessment of the PR
state. In previous studies, the concentrations of BC, CO, and CN as well as
the air mass origin by means of BTs have been used as corresponding markers
at other locations; however all of them are characterized by certain
limitations (Hamilton et al., 2014, and reference therein). For the present
study, we explored the suitability of BC, CO, CN, and BTs as well as
combinations of them as PR markers. Their strengths and limitations can be
summarized as follows:
BT-based PR filter: BTs help to identify periods with comparatively clean air
mass advection due to fact that the BTs which reach furthest to the northeast
across the South American continent overpass the largest fraction of
uninhibited and, thus, untouched forest regions (C. Pöhlker et al.,
2018). Accordingly, a BT filter allows local and regional sources of
anthropogenic pollution to be excluded. However, BTs do not allow
long-range-transport aerosols to be filtered out reliably, particularly from
African sources. Accordingly, BTs are not suitable as a stand-alone PR
filter; however, they are useful in combination with other PR markers as
outlined below.
CN-based PR filter: the total particle concentration per se is not a good filter for
clean or (near-)pristine states since natural aerosol conditions range from
very low (e.g., NCN in Antarctica reaching down to 10 cm-3;
Fiebig et al., 2014) to very high concentrations (e.g., NCN in the upper
troposphere (UT) reaching above 10 000 cm-3; Andreae et al., 2018).
Accordingly, NCN is also not a good stand-alone PR filter. However, with
respect to the “typical” Amazonian aerosol concentrations in the boundary
layer (i.e., a few hundred up to few thousand particles per cubic meter), the
total particle concentrations provide at least a helpful reference level
for the extent of pollution.
BC-based definition of PR, called PRBC: BC represents a unique indicator
for combustion aerosol particles, which in the Amazon Basin almost entirely
originate from anthropogenic sources (i.e., fossil fuel and biomass burning
in South American and Africa). Accordingly, we defined PRBC episodes by
means of the absence of detectable BCe. Petzold and Schönlinner (2004) determined the detection limit of the MAAP “as the minimum
resolvable absorbance” by considering “the variability of blank filter
optical properties”. The detection limit corresponds to the resulting mean
absorbance of the blank filter +3× the standard deviation (SD)
resulting in an absorption coefficient of 0.132 Mm-1 for 30 min
averages. The MBCe was calculated by using mass absorption cross
sections (MACs) retrieved by fitting MAAP absorption coefficients at 637 nm
and Single Particle Soot Photometer (SP2) rBC mass measurements during the
different seasons as explained in Saturno et al. (2017a, c). Using the MAC values measured
at ATTO (MAC for the wet season: 11.4 ± 1.2 m2 g-1; MAC for
the dry season: 12.3 ± 1.3 m2 g-1), the conversion to
MBCe corresponds to 0.011–0.012 µg m-3. Note that this
threshold would be higher if a “traditional” MAC value of 6.6 m2 g-1 were used to calculate MBCe
(∼ 0.019 µg m-3). Here, we selected a threshold concentration of
MBCe∗ = 0.01 µg m-3
when the ATTO-specific MAC values are taken
into account. Accordingly, PRBC periods fulfill the criterion
MBCe < MBCe∗ for ≥ 6 h (after applying a
five-point running average to the 1 h MBCe data).
CO-based definition of PR, called PRCO: the BC approach has the potential
drawback that it may overemphasize periods with strong precipitation, since
heavy rain removes the BC/aerosol content in actually polluted air masses
irrespective of the gas phase composition (see Hamilton et al., 2014).
Accordingly, the PRCO filter is based on the gas phase combustion marker,
CO. In our definition, PRCO conditions prevailed during periods in which
the ATTO cCO concentrations dropped below the monthly background CO
levels at the following reference stations
(https://www.esrl.noaa.gov/gmd/dv/site/CPT.html; last access: 7 April 2018):
whenever the ATTO cCO data were associated with northern hemispheric (NH) BTs
(i.e., NE or ENE BT clusters, Fig. S1) and dropped below the average of the
monthly CO levels obtained at the three background stations at Ragged Point
in Barbados (RPB), Assekrem in Algeria (ASK), and Izaña in Cape Verde
(IZO), the episode was flagged as PRCO. Analogously, whenever the ATTO
cCO data were associated with southern hemispheric (SH) BTs (i.e., E and ESE
BT clusters) and dropped below the monthly CO levels obtained at the
background station on Ascension Island (ACS), the episode was also flagged
as PRCO. Note that the time series of monthly cCO levels from the
reference stations was linearly interpolated to hourly values for the
comparison with ATTO cCO data. The BTs from the northern hemisphere
(i.e., NE and ENE BT clusters) account for ∼ 70 % of all
BTs during the wet season. Among these ∼ 70,
∼ 30 % can be attributed to the NE BT clusters. The
locations of the CO background stations, the wet-season ATTO BT ensemble,
and the hemispheric CO distribution are shown in Fig. S2.
The seasonality in the frequency of occurrence, f, of the PRBC and
PRCO filters is generally very similar, as shown in Fig. 1: the first
PRBC and PRCO episodes occur in February. Their highest f is reached
around the second half of April and the first half of May. Afterwards, f
decreases steeply and PR conditions occur only occasionally in June and July.
Overall, PRBC and PRCO episodes overlap ∼ 25 % of
the total time that is flagged by at least one of the two filters. Since
both approaches are associated with certain limitations and uncertainties,
we further analyzed two combinations of PRBC and PRCO: the union of
both sets (PRBC∪CO) and the intersection of both sets
(PRBC∩CO) as shown in Fig. 1. For the in-depth analysis of aerosol
and CCN properties in this work, we chose the PRBC∩CO filter, which
we consider as the most strict and, thus, robust since the criteria of both
filters have to be fulfilled.
Seasonality of frequency of occurrence, f, of empirically pristine
rain forest (PR) aerosol conditions at ATTO by means of PRBC,
PRCO, PRBC∩CO, and
PRBC∪CO filters (see Sect. 2.7).
PRBC∩CO as the most robust case is used as
the main PR filter throughout this work. Data are shown as weekly averages
for the time period from March 2012 until June 2016. An analogous representation for the year 2014 as the CCN focal
period of this study is shown in Fig. S4.
With respect to the PR filters, the following aspects are worth mentioning:
PR episodes at ATTO exclusively occur during the wet season due to a
combination of two effects. First, the ATTO site is very remote and the
characteristic wet-season air mass advection occurs mostly over uninhabited
areas (see C. Pöhlker et al., 2018). Second, the high precipitation rates
entail strong scavenging and, thus, relatively short aerosol particle
lifetimes, which reduces the long-range transport of the background pollution
aerosol load. In contrast, dry-season PR episodes have almost never been
observed at ATTO due to the extensive biomass burning emissions in South
America and Africa in combination with low scavenging rates and, thus, long
atmospheric aerosol lifetimes. This is relevant since wet- vs. dry-season
conditions were likely associated with different atmospheric states, for
example with respect to volatile organic compound (VOC) concentrations and
aerosol populations as well as photochemical conditions. While wet-season
PR states are still experimentally accessible as outlined in this work, the
dry-season PR state appears to be entirely swamped by pollution.
The primary filters are based on the combustion markers BC and CO, which do
not allow a discrimination between wildfires and man-made fires. Accordingly,
the contribution of wildfire emissions, which were part of an originally
pristine atmosphere in the Amazon, is erroneously filtered out. Generally,
wildfires in the tropical Amazonian forests are rare events due to the fact
that the dense and moist canopies – if unperturbed – effectively maintain a
fire-immune state (e.g., Cochrane, 2003; Nepstad et al., 2008). Nevertheless,
wildfires play a certain, although minor, role for the ATTO observations,
since the wet-season BTs cover the Guianan savanna ecoregions that account
for ∼ 8 % of the ATTO footprint region (Olson et al., 2001;
C. Pöhlker et al., 2018). Within these regions, the infrequent occurrence
of wildfires is part of the savanna-specific fire regime (de Carvalho and
Mustin, 2017). Figure S3 illustrates the relevance of wildfires for ATTO
under wet-season conditions.
Moreover, Saharan dust as well as marine aerosols from the Atlantic Ocean
are advected towards the ATTO region via wet-season LRT plumes, which are
most frequent in February and March (Moran-Zuloaga et al., 2017). Saharan
dust and marine aerosols can also be considered as part of an original
pristine atmospheric state in the Amazon. However, all LRT plumes arriving
at ATTO are filtered out by the PRBC and PRCO approaches due to the
fact that virtually all LRT plumes contain a substantial fraction of smoke from
mostly man-made fires in West Africa (Moran-Zuloaga et al., 2017).
Accordingly, the role of the present-day LRT plumes arriving at ATTO has to
be differentiated carefully as they represent a (partly internal) mixture of
natural and anthropogenic aerosols.
Results and discussion
Aerosol and cloud microphysical seasonality in the Amazon
Prior to the in-depth analysis of the aerosol and CCN cycling for
characteristic conditions, this section provides an overview of the aerosol
and cloud microphysical seasonality in the ATTO region. The pollution
markers cCO and MBCe in Fig. 2a show a pronounced seasonal cycle
with a prevalence of clean conditions in the wet season vs. the
biomass-burning-related pollution maximum in the dry season (Andreae et al., 2015).
The annual minimum in MBCe levels occurs at the end of April (with weekly
MBCe means of ∼ 0.03 µg m-3), whereas its
highest concentrations were observed in August and September (with weekly
MBCe means of ∼ 0.40 µg m-3). The seasonal
cycle of cCO shows a temporal shift of about 1 month with its minimum in
the beginning of June (with weekly cCO means ∼ 100 ppb)
and largest values from October to December (with weekly cCO means
∼ 200 ppb). The phase shift between the cCO and
MBCe seasonality can be explained by the spatiotemporal interplay of
combustion-related BC and CO sources, aerosol wet scavenging, and
alternating advection of NH vs. SH air
masses (Martin et al., 2010b; Andreae et al., 2012, 2015). Similar to the
MBCe trends, the total aerosol particle concentration, NCN,10,
tracks the seasonality in biomass burning activities (in South America and
Africa) with lowest concentrations in March and April (NCN,10 weekly
means from 200 to 300 cm-3) and highest values between August and
November (NCN,10 weekly means from 1000 to 2000 cm-3) as shown in
Fig. 2b. The CCN concentrations at a supersaturation of 0.5 %,
NCCN(0.5 %), which were calculated based on long-term SMPS data and
the κ-Köhler parametrization as outlined in our Part 1 paper,
mostly tracked the seasonal trends in NCN,10. Its minimum around March
and April showed weekly mean NCCN(0.5 %) values around 200 cm-3,
whereas the maximum showed values between 1000 and 2000 cm-3.
Seasonal cycle of selected trace gas, aerosol, and cloud
microphysical parameters. (a) Pollution tracers,
MBCe and cCO. (b) Total aerosol
number concentration, NCN,10; Aitken mode number concentration,
NAit; accumulation mode concentration, Nacc; and CCN
number concentration at S=0.5 %, NCCN(0.5 %).
(c) Precipitation products, PBT, representing cumulative
precipitation along BT tracks, and PTRMM, representing
TRMM-derived precipitation within the ROIATTO as defined in
Fig. S1. (d) Satellite-derived cloud fraction and cloud top
temperature within the ROIATTO. (e) Satellite-derived
cloud droplet effective radius, re, within the
ROIATTO. For a detailed characterization of the land use and
recent land use change in the ATTO footprint, including the
ROIATTO, we refer to C. Pöhlker et al. (2018). Data in
(a) to (d) are shown as weekly averages. Data in
(e) are shown as monthly averages. Error bars represent 1 standard
deviation. Vertical orange shading represents 2-month time in 2014 frames of
representative clean and polluted conditions as shown in detail in Figs. 3
and 4.
Figure 2c shows the seasonal cycles of two precipitation data products:
first, PTRMM data represent the area-averaged precipitation rate
in the ROIATTO (see Fig. S1). The PTRMM data reveal a
rather broad wet-season precipitation maximum from March to May. The smallest
precipitation rates are observed from September to November. Second, the
PBT data represent the cumulative precipitation along the BTs
arriving at ATTO (for details see C. Pöhlker et al., 2018). Thus,
PBT represents a measure for the extent of rain-related aerosol
scavenging – particularly of long-range-transport aerosols – during air
mass transport towards ATTO. A pronounced maximum in PBT and the
related scavenging is observed for the months April and May, which coincides
with the minimum in aerosol parameters (i.e., MBCe
and NCN,10) (see also Moran-Zuloaga et al., 2017).
Figure 2d shows the seasonal cycles in cloud fraction and cloud top
temperature within the ROIATTO. Both are predominantly influenced by
the position of the ITCZ with its belt of
extended deep convective cloud systems and strong precipitation (e.g.,
Moran-Zuloaga et al., 2017). According to Fig. 2d, the densest cloud cover
and deepest convection (represented by lowest cloud top temperature) occurs
upon northerly passage of the ITCZ in the middle of the wet season (i.e.,
March and April) as well as upon southerly passage of the ITCZ at the end
of the dry season (around November). The maximum in deep convection in March
and April – expectedly – corresponds to the peak in PTRMM. A global
picture of the spatiotemporal trends in cloud microphysical properties can
be found in King et al. (2013).
Figure 2e presents the satellite-derived effective radius, re, of liquid
cloud droplets that links the seasonality in aerosol and cloud properties.
The re data have been filtered by cloud top temperature to discriminate
the different re growth states at different heights of the convective
clouds. It is well established that increased CCN loads entail an influence
on cloud properties, which typically results in a corresponding decrease in
droplet diameter (e.g., Freud et al., 2008; Stevens and Feingold, 2009).
Figure 2e underlines this behavior by means of a clear seasonality in
re with its maximum during the clean wet season (i.e., April and May)
and its minimum during the polluted dry season (i.e., August to November). A
detailed understanding of the impact of the CCN loading on the cloud
microphysical properties, however, is the subject of ongoing studies at ATTO
and further locations worldwide.
In essence, Fig. 2 provides a coherent picture of the aerosol, cloud, and
precipitation seasonality as well as their corresponding linkages. The
following sections will zoom into this overall picture by presenting detailed
aerosol and CCN data from characteristic wet- and dry-season conditions of
the year 2014.
Aerosol and CCN time series for representative wet-season
conditions
During the Amazonian wet season (February to May), the influence of local and
regional anthropogenic pollution (i.e., from biomass burning) decreases to a
minimum, and simultaneous strong precipitation causes efficient aerosol
scavenging (see Fig. 2). The combination of both effects results in a
comparatively clean state of the Amazonian atmosphere (Martin et al., 2010b;
Andreae et al., 2015). During this time of the year, biogenic aerosols from
the surrounding rain forest ecosystem, such as secondary organic aerosol
(SOA) from the oxidation of biogenic volatile organic compounds (BVOCs) as
well as primary biological aerosol particles (PBAP), prevail (Pöschl et al., 2010; Huffman et al., 2012;
Yañez-Serrano et al., 2015). However, the regionally and biogenically
dominated background state of the atmosphere is frequently perturbed by the
episodic advection of LRT aerosols from Africa in air masses that bypass the
major rain fields and, therefore, “survive” the intense scavenging
(Moran-Zuloaga et al., 2017). The frequent intrusion of LRT aerosols is a
characteristic feature during the Amazonian wet season and represents a
strong and important influence on the rain forest ecosystem (e.g., Chen et
al., 2009; Bristow et al., 2010; Baars et al., 2011; Abouchami et al., 2013;
Yu et al., 2015; Rizzolo et al., 2017). These LRT plumes mostly comprise a
complex mixture of Saharan dust, African biomass burning smoke, and marine
aerosols from the transatlantic air passage (e.g., Talbot et al., 1990; Swap
et al., 1992; Gläser et al., 2015).
Overview plot illustrating selected meteorological, trace gas,
aerosol, and CCN time series for representative wet-season conditions in the
central Amazon. The shown time period from 23 March to 31 May covers a
comparatively clean and extended time frame throughout the entire CCN
measurement period. Individual panels represent (a) daily frequency of
occurrence of 15 different BT clusters, fBT,cluster, with color code
corresponding to Fig. S1; (b) precipitation rate (PATTO) measured
locally at ATTO and cumulative precipitation from BT analysis (PBT) as
a measure for aerosol wet deposition; (c) SMPS-derived time series of number
size distributions spanning nucleation, Aitken, and accumulation modes; (d) CCNC-derived time series of
κ(S,Da) size distributions; (e) concentrations of biomass burning tracers, CO mole fraction (cCO) and
BCe mass concentration (MBCe); (f) total number concentrations of
the entire aerosol population (NCN,10), Aitken mode particles
(NAit); and accumulation mode particles (Nacc); (g) CCN concentrations,
NCCN(S), for selected supersaturations S; (h) hygroscopicity parameter,
κ(S,Da), for selected S; (i) CCN efficiencies,
NCCN(S)/NCN,10, for selected S; and (j) maximum activated fraction,
MAF(S), for selected S. All CCN data are provided at the original time resolution
of about 4.5 h. Light blue vertical shadings represent empirically pristine
rain forest (PR) aerosol conditions according to the
PRBC∩CO filter as defined in Sect. 2.7. Vertical
lines highlight PR1, PR2, and PR3 periods selected for detailed analysis in Sect. 3.4. Light orange shading represents LRT episodes
according to Moran-Zuloaga
et al. (2017). Vertical lines highlight LRT case study for detailed analysis in
Sect. 3.5.
The 2-month period in Fig. 3 can be regarded as representative for typical
wet-season conditions in the ATTO region as it includes both scenarios:
periods with a prevalence of the local (biogenic) background aerosol and
episodes under the influence of LRT plumes, as well as intermediate states.
In general, the wet season of 2014 showed average hydrological conditions
without significant precipitation anomalies within the ROIATTO,
which is in stark contrast to 2015/16 with its pronounced El Niño
influence and an associated negative precipitation anomaly (see
C. Pöhlker et al., 2018; Saturno et al., 2017a). Furthermore, the
pollution tracers – NCN,10, MBCe, and
cCO – showed comparatively low values with concentrations around
NCN,10 = 330 ± 130 cm-3,
MBCe = 0.03 ± 0.05 µg m-3,
and cCO = 110 ± 10 ppb (given as mean ± 1 SD for
the entire time period in Fig. 3) in agreement with previous studies (e.g.,
Andreae et al., 2012, 2015; Artaxo et al., 2013). In terms of atmospheric
circulation, the first half of the 2-month time frame was dominated by
backward-trajectory arrivals from the northeast (blue and green BT clusters;
see Figs. 3a and S1), whereas during the second half the dominant wind
direction shifted towards easterly directions (orange and red BT clusters;
see Figs. 3a and S1) (compare Andreae et al., 2015; Moran-Zuloaga et al.,
2017). This gradual swing of the dominant wind direction from NH to SH
relates to the northwards movement of the ITCZ.
In Fig. 3, the orange background shading emphasizes several episodes with
detectable LRT influence, according to Moran-Zuloaga et al. (2017). A
comparatively strong LRT plume at ATTO – labeled as LRT case study in Fig. 3
– is the subject of a detailed CCN analysis in Sect. 3.5. The interested
reader will find further in-depth analysis of this particular LRT event in
Moran-Zuloaga et al., 2017 (where it is discussed as event
“2014_7”). Andreae et al. (2012) argued that the
“atmospheric state and processes in the Amazon Basin cannot be understood
without the consideration of pollutant inputs by long-range transport”.
This is evidently true for the major LRT plumes in Fig. 3 with
MBCe reaching up to 0.3 µg m-3 as well as associated
increases in Nacc. However, a closer analysis of the extended and
relatively clean period from 14 April until 31 May 2014 also reveals that during
∼ 85 % of the time detectable amounts of background
pollution were present (i.e., MBCe exceeding MBCe*; see Sect. 2.7).
Although (highly) diluted, the advected aerosols can impact the CCN
population, as discussed in Sect. 3.4. Only when pollution levels actually
drop below detection limits and the conditions satisfy our rather strict
PRBC∩CO filter do empirically pristine aerosol conditions prevail,
which are emphasized by a blue shading in Fig. 3. A statistical overview of
the relative fraction of PR episodes for the years 2012 to 2016 is shown in
Fig. 1. It shows that PR conditions typically occur from March to May, with
their highest abundance around late April and early May, when weekly
frequencies of occurrence reach up to ∼ 20 %, according to
the strict PRBC∩CO filter, or even higher. Note that the PR episodes
in 2014 mostly occurred in April and May (compare Figs. 3 and S4), which
is in good agreement with the multi-year observations.
Overview of conditions and corresponding time frames for detailed
aerosol and CCN analysis. Note that time frames of different length were
averaged for certain aerosol and CCN data products (i.e., size
distributions, composition, CCN key parameters) depending on data
availability.
Conditions
Time frames (UTC)
Specific remarks
Empirically
All available
Defined in Sect. 2.7; filter time series
– For NCN(D), NCCN(S,D), κ(S,Da), CCN efficiency spectra (Fig. 6a and b): all available PRBC∩CO
pristine rain
PR periods
available as separate data file
episodes within CCN measurement period (Mar 2014–Feb 2015) were averaged.
forest (PR)
PR1
24 Apr 06:00–29 April 10:00 2014
– Episodes PR1, PR2, and PR3 highlighted in Fig. 3 and shown in detail in Fig. 5.
aerosol
PR2
4 May 23:00–8 May 10:00 2014
– For ACSM results (Table 3): all PRBC∩CO episodes within time frame 1 Aug 2014 to 30 Sep 2016
PR3
16 May 06:00–17 May 16:00 2014
were averaged.
Long-range
LRT
9 Apr 12:00–13 Apr 12:00 2014
– LRT time frame averaged for NCN(D), NCCN(S,D), κ(S,Da), CCN efficiency spectra (Fig. 6c and d).
transport (LRT)
– LRT episode highlighted in Fig. 3 and shown in detail in Fig. 7.
aerosol
– For ACSM results (Table 3): all LRT episodes according to Moran-Zuloaga et al. (2017)
(in wet season)
All available
See Moran-Zuloaga et al. (2017)
within time frame 1 Aug 2014 to 30 Sep 2016 were averaged.
LRT episodes
Biomass burning
BB
18 Aug 00:00–22 Aug 00:00 2014
– BB time frame averaged for NCN(D), NCCN(S,D), κ(S,Da), CCN efficiency spectrum (Fig. 6e and f),
(BB) aerosol
and ACSM results (Table 3).
– BB episode highlighted in Fig. 4 and shown in detail in Fig. 8.
Mixed pollution
MPOL-BB
Entire period: 22 Sep 00:40–
– Subcategories MPOL-BB and MPOL-LRT during MPOL episode averaged independently for NCN(D),
(MPOL) aerosol
MPOL-LRT
1 Oct 03:30 2014
NCCN(S,D), κ(S,Da), CCN efficiency spectra in Fig. 6g, h and i, and ACSM averages (Table 3).
(in dry season)
(for details refer to Fig. 9)
– MPOL highlighted in Fig. 4 and shown in detail in Fig. 9.
The following picture emerges for the CCN parameters: the time series of the
κ(S,Da) size distributions in Fig. 3d clearly illustrates the
different κ(S,Da) of Aitken and accumulation modes as discussed
in our Part 1 study (Pöhlker et al., 2016). Overall, κ(S,Da) shows a clear variability for low S, representing the accumulation
mode, and rather stable values for higher S, representing the Aitken mode
(see Fig. 3h). The occurrence of the LRT plumes stands out clearly by
causing a significant enhancement of κ(S,Da), with κAcc reaching 0.4 and κAit reaching 0.25
(see Fig. 3d and h). Moreover, the LRT events are also associated with increased
NCCN(S) and NCCN(S)/NCN values (Fig. 3g and i). Note that the
occurrence of the LRT events precisely coincides with the minima in
PBT and, thus, “windows” in aerosol scavenging (see Moran-Zuloaga et
al., 2017).
For the extended and comparatively clean period from 14 April until end of
May, the SMPS contour plot (Fig. 3c) reveals equally strong Aiken and
accumulation modes as well as a “patchy” appearance, due to frequent
rainfall causing (local) aerosol scavenging. As outlined in Part 1, the
aerosol abundance in the particle size range > 40 nm
predominantly defined the measured CCN population (Pöhlker et al.,
2016). Accordingly, the NCCN(S) and NCCN(S)/NCN time series
directly track the SMPS-derived patchy pattern. The low S levels (i.e.,
NCCN(0.11 %)), which do not activate Aitken mode particles, closely
followed the accumulation mode concentration, Nacc, time series, whereas
the higher S levels (i.e., NCCN(1.10 %)), which also activated
particles in the Aitken mode, closely tracked NCN,10 (= NAit+ Nacc). Two of the subsequent case studies will focus in more detail on
the specific CCN properties of the PR (Sect. 3.4) and LRT (Sect. 3.5) conditions
(see also Table 1).
Aerosol and CCN time series for representative dry-season
conditions
Overview plot illustrating selected meteorological, trace gas,
aerosol, and CCN time series for representative dry-season conditions in the
central Amazon. The shown time period from 1 August to 31 September covers the most
polluted time frame throughout the entire CCN measurement period. Individual
panels represent (a) daily frequency of occurrence of 15 different BT
clusters, fBT,cluster, with color code corresponding to Fig. S1;
(b) precipitation rate (PATTO) measured locally at ATTO and cumulative
precipitation from BT analysis (PBT) as a measure for aerosol wet
deposition; (c) SMPS-derived time series of number size distributions
spanning nucleation, Aitken, and accumulation modes; (d) CCNC-derived time
series of κ(S,Da) size distributions; (e) concentrations of
biomass burning tracers, CO mole fraction (cCO) and BCe mass
concentration (MBCe); (f) total number concentrations of the entire
aerosol population (NCN,10), Aitken mode particles (NAit), and
accumulation mode particles (Nacc); (g) CCN concentrations,
NCCN(S), for selected supersaturations S; (h) hygroscopicity parameter,
κ(S,Da), for selected S; (i) CCN efficiencies,
NCCN(S)/NCN,10, for selected S; (j) maximum activated fraction,
MAF(S), for selected S; and (k) ACSM-derived sulfate mass concentration
(Msulfate) and organic-to-sulfate ratio (OA / SO42-). All CCN data
are provided at the original time resolution of about 4.5 h. Vertical lines
highlight BB case study on biomass burning conditions for detailed analysis in
Sect. 3.6 and MPOL case study on mixed pollution conditions for detailed
analysis in Sect. 3.7.
During the dry season (August to November), the central Amazon is under
continuous influence of pronounced anthropogenic pollution. The predominant
type is biomass burning smoke from deforestation fires, which led to the coining of the
term “biomass burning season” (Freud et al., 2008). In addition, various
urban and industrial emission sources in eastern and southern Brazil as well
as southern Africa may also contribute by hard-to-define quantities (e.g.,
Andreae et al., 1994; Saturno et al., 2017a). The minimum in precipitation
rates and, thus, in aerosol scavenging fosters the distribution of pollution
aerosols over large areas by extending their atmospheric lifetime (see Fig. 2).
Figure 4 represents the dry-season counterpart of Fig. 3 and shows the
corresponding time series for a characteristic dry-season period from 1
August until 30 September 2014. The BT clusters in Fig. 4a show that
easterly and southeasterly BTs prevailed, since the ITCZ was located north
of the ATTO site. The BT clusters which are most characteristic for the dry
season approached the Amazon River delta from the South Atlantic and
then followed the river in a westerly direction towards ATTO (red and orange
clusters; see Fig. S1). These “dry-season river BTs” are the subject of a
more detailed discussion in the case study in Sect. 3.7. For certain
episodes, the wind swings further to southeasterly directions and brings air
masses from inland directions (black, brown, and grey BT clusters; see Fig. S1). These “dry-season inland BTs” are further discussed in the case study
in Sect. 3.6.
In contrast to the wet season, the accumulation mode clearly dominates the
size distribution, which explains the increased CCN efficiencies,
particularly at low S (Fig. 4c and i). The frequent “pulses” in the
accumulation mode concentration can be attributed to (aged) biomass burning
plumes, which impact the ATTO site episodically, typically for few days (see
Freitas et al., 2005). The MBCe, NCN,10, and cCO levels are
typical for dry-season conditions in the northeast Amazon Basin with
MBCe = 0.55 ± 0.35 µg m-3,
NCN,10 = 1520 ± 780 cm-3, and cCO = 140 ± 50 ppb (given as
mean ± 1 SD for the time period in Fig. 4) (e.g., Rissler et al.,
2004; Andreae et al., 2012, 2015; Artaxo et al., 2013; Saturno et al.,
2017a).
The most obvious event in Fig. 4 is the advection of a strong BB
plume from 17 to 23 August 2014, the highest pollution levels
that were observed during the entire CCN measurement period. This event can
be recognized by means of strong pulses in NAcc, MBCe, and
cCO as well as a dip in κ(S,Da). The ACSM-derived
organic-to-sulfate ratio confirms that the biomass burning pulse comprised a
predominantly organic aerosol. In general, the measured κ(S,Da) levels respond inversely to the organic-to-sulfate ratio,
confirming previous studies which stated that organic matter and sulfate
constituents mostly define the aerosol's hygroscopicity in the Amazon
(Roberts et al., 2002; Gunthe et al., 2009). The major biomass burning plume
in August 2014 is the subject of the detailed case study BB in Sect. 3.6.
Beside this major biomass burning plume, several shorter pulses, which were
supposedly also caused by upwind fires, were observed throughout the
dry-season period, and their frequency of occurrence increased towards the end
(i.e., after 12 September). Phenomenologically, the major and minor biomass
burning plumes were similar as they exhibit peaks in NCN, MBCe, and
cCO and simultaneous dips in κ(S,Da) and the
organic-to-sulfate ratio. The second half of September comprised conditions
with comparatively high sulfate concentrations and a sequence of short
biomass burning plumes. This period is influenced by a mixture of different
(pollution) aerosol populations from African long-range transport and
regional South American sources. A detailed description of the case study
MPOL can be found in Sect. 3.7.
Similar to the wet season, different κ(S,Da) levels for the
Aitken and accumulation modes as well as comparably low κ(S,Da) variability over time (κAit = 0.14 ± 0.03
vs. κAcc = 0.23 ± 0.04, covering the entire time
period in Fig. 4d and h) were observed. The MAF(S) values tend to reach unity,
except for MAF(0.11 %) under the influence of biomass burning smoke (i.e.,
for the smaller and major smoke plumes).
Case study PR on empirically pristine rain forest
aerosols
Selected meteorological, aerosol, and CCN time series from ATTO
measurements for the empirically pristine rain forest (PR) case study
periods PR1, PR2, and PR3 (see Fig. 3). (a) Incoming shortwave
radiation, SWin; precipitation rates from TRMM satellite mission,
PTRMM; and in situ measurements at ATTO, PATTO.
(b) Wind direction and wind speed, U, at ATTO.
(c) Equivalent potential temperature, θe, and
relative humidity, RH, at ATTO. (d) Overlay of two data layers
showing aerosol number size distribution contour plot, dN/dlogD, as well
as color-coded markers, representing time series of κ(S,Da)
size distributions. (e) CCN concentrations, NCCN(S), for
two selected S levels; total aerosol number concentration,
NCN,10; and BCe mass concentration,
MBCe. Vertical shadings represent
PRBC∩CO periods according to the definition
in Sect. 2.7.
Aerosol–cloud interactions remain the largest uncertainty in global
climate models, and a better understanding of a preindustrial atmospheric
state is essential to reduce this uncertainty (Carslaw et al., 2013;
Seinfeld et al., 2016). As outlined in Sect. 2.7, the Amazonian wet season
provides the rare chance to analyze episodes of very clean continental
conditions, which are our best approximation of a pristine rain forest
atmosphere. This case study extracts the characteristic aerosol and CCN
properties during the identified PR periods. Figure 5 zooms into three
selected episodes – called PR1, PR2, and PR3 as highlighted in Fig. 3 – and
combines meteorological, aerosol, and CCN time series for a detailed
analysis.
The meteorological parameters in Fig. 5a, b, and c illustrate typical
wet-season conditions: (i) a rather high degree of cloudiness, which can be seen
by means of the strong cloud-related dimming of the incoming shortwave
radiation, SWin; (ii) frequent local (PATTO) and regional
(PTRMM) rain events; (iii) a comparatively stable northeasterly wind
direction, which is consistent with the BT analysis in Fig. 3; (iv) a rather
constant wind speed, U, for most of the time, which got more vigorous
during rain events; (v) high-relative-humidity (RH) conditions, being
inversely related to SWin and reaching saturation during the
nights; and (vi) a characteristic time series of the equivalent potential
temperature, θe, which tracked the daily onset of solar heating
(see simultaneous increase of SWin and θe) and further
provides valuable information on vertical atmospheric mixing, particularly
in connection with rain events. Specifically, sudden drops in θe
indicate a downward transport of air masses from upper-tropospheric
layers, which occurred frequently with the onset of strong rain (for more
details see Wang et al., 2016).
The corresponding aerosol variability is shown as an aerosol number size
distribution (dN/dlogD) contour plot in Fig. 5d. Under PR conditions we found a
dominant Aitken mode and a comparatively weak accumulation mode, as reported
previously (e.g., Andreae et al., 2015; Pöhlker et al., 2016).
Moreover, the Aitken and accumulation modes reveal a patchy and
discontinuous abundance with rather sudden concentration increases and
drops. These observations can be explained by a combination of different
effects and processes: first, the strong and persistent (mostly
combustion-related) sources of accumulation mode aerosol particles, which
are responsible for the continuous and dominant accumulation mode in the dry
season, were absent.
Second, the frequent rain events acted as an efficient aerosol removal
mechanism via aerosol rain-out (i.e., particle activation into cloud/rain
droplets) and wash-out (i.e., particle collection by falling droplets below
clouds). The wash-out efficiency is slightly higher (∼ factor
1.5) for Aitken than accumulation mode particles (Wang et al., 2010; Zikova
and Zdimal, 2016). In contrast, the rain-out efficiency, corresponding to
CCN activation, is typically much higher for accumulation than Aitken mode
particles (Pöhlker et al., 2016). Accordingly, the rain pulses
strongly modulated the aerosol's abundance via sudden and efficient
deposition, which is visible in Fig. 5d as characteristic “notches” in the
aerosol contour plot. The notches represent the (removed) part of the
aerosol size fraction that was activated as CCN into cloud droplets.
Illustrative examples can be found during days with strong rain showers,
such as 27 April, 5 May, and 6 May. Note that the “depth of the notches”,
corresponding to the smallest activated particles, could in principle be
used to estimate the S levels during the corresponding events (Krüger et
al., 2014). Further note in this context that, besides depletion of the
accumulation relative to the Aitken mode, aerosol activation and cloud
processing are also known to foster the opposite effect: the growth of
Aitken mode particles into the accumulation mode via formation of aqueous-phase
reaction products (i.e., sulfate and aqueous-phase SOA) in the cloud
droplets, followed by droplet re-evaporation and deposition of the newly
formed compounds onto the particles (e.g., Ervens, 2015; Farmer et al.,
2015). During 7 May, cloud processing might have been responsible for the
formation of a rather strong accumulation mode from an existing Aitken mode
population.
As a third process, the Aitken mode population in the rain forest BL
was frequently replenished by pulse-like appearance of particles
with diameters < 50 nm. These events are supposed to be
convection-related downward transport of air masses from an UT particle pool and the subsequent growth of the injected
fine particles (Wang et al., 2016; Andreae et al., 2018). Remarkably, the
combination of all these effects results in a comparatively constant total
particle abundance, NCN, across the rain showers, due to compensating
effects of accumulation mode particle losses and simultaneous increases in
Aitken mode abundance (see details in Wang et al., 2016). The
convection-related downward transport of fine UT particles and their
subsequent growth – presumably by the condensation of low-volatility vapors
– is a characteristic feature of the Amazonian wet season. At least three
pronounced examples for this process are included in the time frame of Fig. 5 (i.e., 27/28 April, 4/5 May, and 16/17 May). After their injection into
the forest BL, the fine particles (initial diameters between 20 and 50 nm
for the events in Fig. 5) reveal a “banana-like” growth into the Aitken mode
size range (∼ 70 nm) in the course of about 12–24 h. For the
events in Fig. 5, we calculated an initial, and thus maximum, growth rate of
0.6 to 6 nm h-1, which agrees well with the 1 to 6 nm h-1 reported
by Kulmala et al. (2004) for tropical regions as well as the reported
5 nm h-1 in Zhou et al. (2002) for an Amazonian site. Note that these
“Amazonian bananas” differ from the classical new-particle formation (NPF)
events that have been reported for various continental sites (i.e., in
northern hemispheric temperate regions) (Kulmala et al., 2004), since the number
concentrations are lower by orders of magnitude and their nucleation and
initial growth do not occur in the BL but in the UT (Ekman et al., 2008;
Engstrom et al., 2008; Pöschl et al., 2010; Andreae et al., 2018).
Accordingly, the UT particle population that is frequently injected into the
BL is already aged to a certain extent and, thus, presumably reflects
chemical processes different from the atmospheric chemistry in the BL. The
physicochemical details of the UT nucleation and growth are still largely
unknown and the subject of ongoing research (e.g., Andreae et al., 2018).
The CCN properties during PR conditions are represented by time series of
κ(S,Da) size distributions (Fig. 5d) and NCCN(S) for two
selected S (Fig. 5e). The temporal pattern of the κ(S,Da) size
distributions, which provides indications of the aerosol particles' chemical
composition, reflects the pattern of the underlying dN/dlogD contour plot.
Consistent with our observations in Part 1 (Pöhlker et al., 2016),
the accumulation mode reveals higher κ(S,Da) levels than the
Aitken mode, likely due to chemical aging through cloud processing and a
related increase in hygroscopicity (Farmer et al., 2015). The lowest
κ(S,Da) levels were observed for the “Amazonian bananas” (see
Fig. 5d). Both the accumulation and Aitken mode κ(S,Da) levels
show a variability that tracks the Aitken and accumulation mode abundance.
Note that NCCN(0.5 %) and particularly NCCN(0.2 %) show
pronounced increases during periods with increased MBCe levels (e.g., 25
April, 5 May, and 17 May). This emphasizes the remarkable impact of diluted
pollution on the CCN population in an aerosol-limited regime according to
Reutter et al. (2009).
Properties (position, x0; integral number concentration,
NCN; width, σ) of Aitken and accumulation modes from single or
double lognormal fits of the total particle size distribution. Values are
given as means of the case study periods, whereas corresponding seasonally
averaged results can be found in the Part 1 study. The errors represent the
uncertainty of the fit parameters. No meaningful double lognormal fit was
obtained for the monomodal MPOL case – thus, a single lognormal fit was
conducted to describe the properties of the main peak. For the cases
PRBC∩CO and LRT with clearly resolved bimodal size
distributions, values for the position of the Hoppel minimum, DH, as
the intersection of fitted and normalized modes as well as estimated average
peak supersaturation in cloud, Scloud(DH, κ), according to
Krüger et al. (2014) and Part 1 study are listed. The error in
Scloud(DH, κ) is the experimentally derived error in S.
Analogous information for the PRBC, PRCO, and
PRBC∪CO filters can be found in Table S1.
Conditions
Mode or
NCN
κ
x0
σ
R2
DH
Scloud(DH,κ)
size range
(cm-3)
(nm)
(nm)
(%)
Empirically pristine rain
Aitken
162 ± 4
0.12 ± 0.01
69 ± 2
0.46 ± 0.01
0.99
103 ± 2
0.29 ± 0.01
forest (PR) aerosol
accumulation
86 ± 8
0.18 ± 0.01
157 ± 1
0.44 ± 0.01
(PRBC∩CO filter)a
Long-range-transport
Aitken
125 ± 12
0.18 ± 0.02
79 ± 3
0.60 ± 0.03
0.99
118 ± 2
0.15 ± 0.01
(LRT) aerosol
accumulation
313 ± 11
0.35 ± 0.04
179 ± 2
0.52 ± 0.01
Biomass burning
Aitken
140 ± 29
0.14 ± 0.01
70 ± 1
0.20b
0.99
–
–
(BB) aerosol
accumulation
3439 ± 39
0.17 ± 0.02
167 ± 1
0.58 ± 0.01
MPOL-LRT
< 100 nm
1272 ± 27
0.14 ± 0.01
135 ± 5
0.85 ± 0.01
0.99
–
–
Mixed-pollution
> 100 nm
0.22 ± 0.03
(MPOL) aerosol
MPOL-BB
< 150 nm
2764 ± 84
0.10 ± 0.01
113 ± 1
0.81 ± 0.01
0.99
–
–
> 150 nm
0.20 ± 0.04
a Double lognormal fits for the PR cases were limited to size range 50
to 350 nm since presence of particles in nucleation mode size range
(< 50 nm) interferes with fit of Aitken mode.
b Width of Aitken mode in double lognormal fit of BB case was predefined
to ensure meaningful convergence of fit.
Overview of case study conditions for empirically pristine rain
forest (PR), long-range transport of African aerosols (LRT), biomass burning (BB), and mixed pollution of
African and Amazonian sources (MPOL), showing size dependence of
hygroscopicity parameter (κ(S,Da)), number size
distributions of total aerosol particles (NCN(D)), and number
size distributions of cloud condensation nuclei (NCCN(S,D)) at
all 10 S levels (S = 0.11–1.10 %) (left side) as well as CCN
efficiency spectra with erf fits (right side). For the size distributions
(left), values of κ(S,Da) for every S level are plotted
against their corresponding midpoint activation diameter, Da(S).
For κ(S,Da), the error bars represent 1 SD. For
Da(S), the experimentally derived error is shown. The standard
errors of the number size distributions – NCN(D) and
NCCN(S,D) – are indicated as shading of the individual lines.
For the CCN efficiency spectra (right), NCN,10 was chosen as a
reference concentration. The experimental data were fitted with single- or
double-erf fits dashed lines with
shading as uncertainty of the fits. The error bars at the markers represent
the measurement error in S and 1 SD in the
NCCN(S)/NCN,10 dimension. The shading represents the
uncertainty of the fits. An overview of the erf fits from all case study
conditions and seasonal averages can be found in Fig. 11. The parameters of
all erf fits are summarized in Table 4. Data for PR conditions in
(a) and (b) represent averages of all
PRBC∩CO episodes during the entire CCN
measurement period as defined in Sect. 2.7. For the PR CCN efficiency
spectrum, the double-erf fit is the better representation, although the
single-erf fit also works as a good approximation. Data for LRT conditions,
shown in (c) and (d), represent the LRT3 period as shown
in Fig. 3. A single-erf fit describes the experimental data accurately. Data
for BB conditions, shown in (c) and (d), represent the
time period in August 2014 as shown in Fig. 4. For the BB case, the
experimental data have been fitted with a single-erf fit with two
modifications: (i) the “default” fit with predefined variable
a1 = 1 as utilized for all other case studies tends to overestimate
NCCN(S)/NCN,10 at high S; (ii) a corresponding fit
with a free variable a1 describes the experimental data more accurately.
Data for MPOL conditions were separated into a MPOL-LRT
case (g), representing sulfate-rich African aerosols, and a
MPOL-BB case (h), representing plumes from close-by fires. MPOL
CCN efficiency spectra are combined in (i) including single-erf
fits. All fit parameters of the erf fits shown here are summarized in
Table 4. The colors of the CCN efficiency spectra were chosen according to
Wong (2011).
Figure 6a and b summarize the average aerosol and CCN key properties under
PR conditions. Figure 6a shows the characteristic average NCN(D) size
distribution with a pronounced bimodal appearance, comprising a dominant
Aitken mode (DAit ≈ 70 nm, NAit ≈ 160 cm-3)
and a comparatively weak accumulation mode (Dacc ≈ 160 nm,
Nacc ≈ 90 cm-3) (see Table 2). This bimodal shape is
typical for clean Amazonian conditions as reported previously (e.g., Gunthe
et al., 2009) and further resembles aerosol size distributions under marine
background conditions (e.g., Atwood et al., 2017). The corresponding
NCCN(S,D) size distributions for all S levels show that for S < 0.3 % mostly accumulation
mode particles were activated, whereas for
S > 0.3 % the Aitken mode particles also acted as CCN.
Furthermore, Fig. 6a shows the average κ(S,Da) size dependence
with a characteristic stepwise increase of κ(S,Da) towards
larger D. The Aitken mode κ(S,Da) levels are rather low and sharply
defined (mean ± SD: 0.12 ± 0.01), whereas the accumulation
mode κ(S,Da) levels are slightly higher (0.18 ± 0.02). The
results suggest that the Aitken mode particles, which are frequently
injected into the BL via downward transport from the UT, are mostly
comprised of organic matter. This observation agrees well with recent results
showing that “the UT particles consist predominantly of organic material,
with minor amounts of nitrate and very small fractions of sulfate” (Andreae
et al., 2018). The hygroscopicity of organic material, κorg, is
typically assumed as ∼ 0.10; however, κorg can
vary substantially (close to 0 up to 0.3) as a function of the organic
material and its oxygen-to-carbon (O : C) ratio (Jimenez et al., 2009; Thalman
et al., 2017). The κ(S,Da) levels of the accumulation mode
similarly indicate the presence of predominantly organic particles, albeit
with somewhat more inorganic constituents than in the Aitken mode. This is
consistent with the corresponding ACSM results in Table 3, underlining that
organic matter accounts for most of the mass (90 %), whereas nitrate (4 %) and sulfate (6 %) add only small contributions. Note that
MNH4, MCl, and MBCe were below the detection limit for PR conditions
and were omitted in the calculation of the mass fractions accordingly (see
Sect. 2.1). A predicted average hygroscopicity parameter, κp,
of 0.16 ± 0.01 was calculated based on the ACSM results – excluding
MNH4, MCl, and MBCe – and agrees with the measured value of
κ(0.11 %) = 0.18 ± 0.05 (Table 3).
Aerosol chemical composition from ACSM measurements at ATTO for
characteristic conditions and seasons. ACSM data were available for time
period from 1 August 2014 to 30 September 2016, and the averaged values are
shown as mean ± SE (SE was rounded up if SE < 0.01). Mass
fractions were calculated as outlined in Sect. 2.1. The shown ACSM 3σ
detection limits for 30 min averaging time were obtained from Ng et
al. (2011). The MAAP-based MBCe detection limits are
specified in Sect. 2.7. Note that MNH4, MCl, and
MBCe ranged below the instrument detection limit in
certain cases, which makes the corresponding results unreliable. The
predicted average aerosol hygroscopicity parameter, κp, was
calculated according to the Part 1 study (Pöhlker et al., 2016) and is
shown as mean ± SE. The κ(0.10 %) results are shown with
the experimentally derived error. Analogous information for the
PRBC, PRCO, and
PRBC∪CO filters can be found in
Table S2.
Conditions and seasons
Mass concentrations Mspecies (µg m-3)
OA / SO42-
κp
κ(0.10 %)
(Mass fraction (%))
OA
NO3-
NH4+
SO42-
Cl
BCe
Empirically pristine rain forest (PR)
0.64 ± 0.02
0.03 ± 0.01
0.17 ± 0.01a
0.04 ± 0.01
< (0.01 ± 0.01)a
< (0.01 ± 0.01)a
∼ 53
0.16 ± 0.01*
0.18 ± 0.05
aerosol (PRBC∩CO filter)
(90)*
(4)*
(–)*
(6)*
(–)*
(–)*
Long-range transport
1.81 ± 0.04
0.08 ± 0.01
0.30 ± 0.01
0.25 ± 0.01
0.04 ± 0.01
0.21 ± 0.01
∼ 24
0.24 ± 0.01
0.35 ± 0.04
(LRT) aerosol
(67)
(3)
(11)
(9)
(1)
(8)
Biomass burning
21.14 ± 0.50
0.55 ± 0.02
0.58 ± 0.02
0.82 ± 0.02
0.03 ± 0.01
0.89 ± 0.03
∼ 26
0.15 ± 0.01
0.18 ± 0.01
(BB) aerosol
(88)
(2)
(2)
(3)
(0)
(4)
MPOL-LRT
5.50 ± 0.06
0.22 ± 0.01
0.54 ± 0.01
1.75 ± 0.03
0.03 ± 0.01
0.37 ± 0.01
∼ 3
0.28 ± 0.01
0.26 ± 0.05
Mixed-pollution
(65)
(3)
(6)
(21)
(0)
(4)
(MPOL) aerosol
MPOL-BB
7.88 ± 0.18
0.36 ± 0.03
0.68 ± 0.03
2.03 ± 0.08
0.05 ± 0.01
0.57 ± 0.02
∼ 4
0.26 ± 0.01
0.24 ± 0.02
(68)
(3)
(6)
(18)
(0)
(5)
Wet season (Feb–May)
1.02 ± 0.01
0.05 ± 0.01
0.20 ± 0.03a
0.14 ± 0.01
0.02 ± 0.01
0.07 ± 0.01
∼ 23
0.19 ± 0.01*
0.21 ± 0.05
(78)*
(4)*
(–)*
(11)*
(1)*
(5)*
Transition periods
3.26 ± 0.04
0.11 ± 0.01
0.32 ± 0.01
0.32 ± 0.01
0.02 ± 0.01
0.20 ± 0.01
∼ 21
0.21 ± 0.01
0.24 ± 0.05
(Jun/Jul & Dec/Jan)
(77)
(3)
(7)
(8)
(1)
(5)
Dry season (Aug–Nov)
5.86 ± 0.05
0.19 ± 0.01
0.33 ± 0.01
0.64 ± 0.01
0.02 ± 0.01
0.34 ± 0.01
∼ 11
0.18 ± 0.01
0.21 ± 0.04
(79)
(3)
(5)
(9)
(0)
(5)
Detection limits of ACSM and MAAP
0.15
0.01
0.28
0.02
0.01
0.01
–
–
–
a Here, the measured Mspecies ranged below the instrument detection
limits, and the shown values are questionable. In these cases (i.e.,
PRBC∩CO and wet-season average), mass fractions and κp
were calculated by omitting the corresponding Mspecies as outlined in
Sect. 2.1. The corresponding mass fractions and κp are marked
by *.
Figure 6b displays the CCN efficiency spectrum for PR conditions, which can
be regarded as the CCN signature of the corresponding aerosol population (for
details refer to the Part 1 companion paper). The pronounced bimodal particle
size distribution with its characteristic Hoppel minimum and the stepwise
increase of κ(S,Da) (see Fig. 6a) result in a weak plateau
at about S= 0.4 %, which required to apply a double-erf fit. For
comparison, we also applied a single-erf fit. Expectedly, the double-erf fit
is the better representation of the experimental data, although the
single-erf fit also covers the data reasonably well, since the plateau is not
particularly pronounced. However, a closer look reveals differences between
the single- and double-erf fits for low and high S. For very low S
(< 0.1 %), the double-erf fit indicates that the PR aerosol
particles start acting as CCN only above about S = 0.06 %, which
can probably be explained by the absence of suitable CCN in the size range of
several hundred nanometers, which are indeed comparatively sparse under PR
conditions according to Fig. 5. However, this size range was not covered
directly by our CCN measurements, making it hard to draw conclusions. Since
the double-erf fit describes the data more accurately than the single-erf
fit, its extrapolation for S > 1.1 % is likely more
accurate and suggests that “full”
activation (∼ 90 %) is reached at S≈ 1.5 %.
The CCN efficiency spectra represent a tool with which to visualize characteristic
differences in the behavior of certain (contrasting) aerosol populations in
cloud formation. Of particular relevance is the slope of the CCN efficiency
spectra, d(NCCN(S)/NCN,10)/dS, as the sensitivity of the activated CCN
fraction of a given aerosol population within a given S range to changes in
supersaturation, ΔS. Accordingly, high
d(NCCN(S)/NCN,10)/dS slopes indicate a regime in which even a subtle
ΔS has relatively strong effects on the NCCN(S) and, thus,
Nd concentrations (assuming constant NCN,10), whereas low
d(NCCN(S)/NCN,10)/dS values indicate a regime that is characterized by
more stable NCCN(S) and Nd concentrations, even upon large ΔS.
Gaussian error function (erf) fit parameters describing CCN
efficiency spectra, NCCN(S)/NCN,10 vs. S, as model
input data representing (i) PRBC∩CO, LRT,
BB, and MPOL conditions according to present work, (ii) seasonal averages
from the Part 1 companion paper (Pöhlker et al., 2016), and (iii) CCN
efficiency spectra adapted from Roberts et al. (2001) and Andreae et
al. (2004). A reference concentration ofNCN,10 has been used in
all cases. NCN is shown as mean ± SE. The errors in
Smode and wmode represent the uncertainty of the fit
parameters (see Sect. 2.2). All CCN efficiency spectra are plotted in Fig. 11
for comparison. Analogous information for the PRBC,
PRCO, and PRBC∪CO filters can be
found in Table S3.
Conditions and seasons
NCN
erf fit
Mode
amode
Smode
wmode
R2
Comment
(cm-3)
(%)
Empirically pristine rain forest (PR)
260 ± 3
Single
1
1
0.43 ± 0.01
1.61 ± 0.07
0.99
aerosol (PRBC∩CO filter)
Double
1
1
0.11 ± 0.01
0.46 ± 0.30
0.99
2
0.21 ± 0.09
0.61 ± 0.08
1.13 ± 0.20
Long-range transport (LRT) aerosol
439 ± 3
Single
1
1
0.09 ± 0.01
1.92 ± 0.15
0.97
Fit parameters for CCN efficiency spectra representing
Biomass burning (BB) aerosol
3584 ± 28
Single
1
1
0.15 ± 0.01
1.15 ± 0.13
0.96
conditions defined in present work
1
0.93 ± 0.01
0.14 ± 0.01
0.89 ± 0.06
0.99
Mixed pollution
MPOL-LRT
1277 ± 6
Single
1
1
0.16 ± 0.01
1.70 ± 0.08
0.99
(MPOL) aerosol
MPOL-BB
2777 ± 38
Single
1
1
0.28 ± 0.01
1.60 ± 0.04
0.99
Wet season
323 ± 2
Single
1
1
0.35 ± 0.01
1.80 ± 0.06
0.99
LRT season
426 ± 3
Single
1
1
0.22 ± 0.01
2.39 ± 0.10
0.98
Fit parameters for seasonal CCN efficiency spectra
Transitions periods
943 ± 4
Single
1
1
0.28 ± 0.01
1.70 ± 0.05
0.99
from Part 1 study (Pöhlker et al., 2016)
Dry season
1528 ± 5
Single
1
1
0.18 ± 0.01
1.57 ± 0.11
0.98
“Cloud-processed smoke” conditions
2000–8000
Single
1
1
0.47 ± 0.02
1.64 ± 0.15
0.97
Fit parameters for CCN efficiency spectra for biomass
“Fresh smoke” conditions
2000–8000
Single
1
1
1.00 ± 0.05
1.56 ± 0.12
0.98
burning smoke conditions from Andreae et al. (2004)
Wet-season period (Mar/Apr 1998)
390 ± 250
Single
1
1
0.62 ± 0.02
1.46 ± 0.07
0.99
Fit parameters for CCN efficiency spectra representing
wet-season period from Roberts et al. (2001)
Selected meteorological, aerosol, and CCN time series from ATTO
measurements for the long-range-transport (LRT) study period (see Fig. 3). (a) Incoming shortwave radiation,
SWin; precipitation rates from TRMM
satellite mission, PTRMM; and in situ measurements at ATTO, PATTO. (b) Wind
direction and wind speed, U, at ATTO. (c) Equivalent potential temperature,
θe, and relative humidity, RH, at ATTO. (d) Overlay of two data
layers showing aerosol number size distribution contour plot, dN/dlogD, as
well as color-coded markers, representing time series of κ(S,Da) size distributions. (e) CCN
concentrations, NCCN(S), for
two selected S levels; total aerosol number concentration, NCN,10; and
BCe mass concentration, MBCe.
The CCN efficiency spectra can be linked to a concept introduced by Reutter
et al. (2009), which classifies the sensitivity of Nd concentrations in
response to changes in NCN and wb. This results in three distinct
regimes: (i) an aerosol-limited regime, which is characterized by high Smax
(> 0.5 %), a high wb / NCN ratio, high activated
fractions Nd / NCN, and a linear relationship between NCN and
Nd; (ii) an updraft-limited regime, which is characterized by rather low Smax
(< 0.2 %), a low wb / NCN ratio, low activated fractions
Nd / NCN, and a linear relationship between wb and Nd; and
(iii) a transitional regime with intermediate states and nonlinear dependencies of
Nd on NCN and wb. Note that the concept of Reutter et al. has
been developed for the conditions of pyro-convective clouds and, thus,
represents an extreme case, which has to be kept in mind in the subsequent
comparison with the CCN efficiency spectra. Pöschl et al. (2010) applied
the concept of Reutter et al. (2009) to wet-season rain forest conditions.
Based on Reutter et al. and Pöschl et al., PR conditions with the
associated low aerosol concentrations (NCN,10 ≈ 260 cm-3,
Table 4) can be characterized as aerosol-limited, due to the overall low
abundance of potential CCN. However, the CCN efficiency spectrum in Fig. 6b
suggests that the PR aerosol population also has a certain sensitivity to
wb, since changes in updraft velocity Δwb – and the
associated ΔS (even for S > 1 %) – strongly modulate
the activated fraction, NCCN(S)/NCN,10. In this sense, the PR CCN
efficiency spectrum has a very characteristic shape and differs clearly from
the other conditions, as it keeps increasing over a wide S range with a
somewhat larger slope for S < 0.3 % and a slightly decreasing
slope for S > 0.3 %. Accordingly, the PR case appears to be aerosol- and
updraft-sensitive, which makes the corresponding aerosol–cloud interactions
highly dynamic. For more quantitative insights, modeling runs according to
Reutter et al. based on the characteristic PR bimodal NCN(D) size
distribution in Fig. 6a as well as adjusted NCN and wb ranges are
required.
Case study LRT on Saharan dust, African smoke, and Atlantic marine
aerosols
The African continent is of significant importance for the Amazonian
atmospheric composition as it represents a major source of desert dust and
pollution aerosols (e.g., Swap et al., 1992; Andreae et al., 1994; Yu et
al., 2015; Rizzolo et al., 2017). A systematic overview of the properties
and relevance of LRT plume arrivals in the ATTO region during the Amazonian wet
season can be found in Moran-Zuloaga et al. (2017). Furthermore, a general
characterization of the CCN population's response to LRT conditions during the
wet season can be found in our Part 1 paper (Pöhlker et al., 2016).
Based on these previous studies, the present paper analyzes the
characteristic impact of LRT plumes on the wet-season CCN population in detail.
The characteristic impact of LRT plumes on the aerosol and CCN data can
already be seen in Fig. 3. For a detailed analysis, the strongest LRT episode
in Fig. 3 has been chosen and is represented in Fig. 7 by selected
meteorological, aerosol, and CCN time series. Note that the advected dust
plumes typically cause clear increases in both the coarse mode, which plays
a secondary role here, and the accumulation mode, which explains their
relevance for the CCN variability (Moran-Zuloaga et al., 2017). The LRT
influence is visible in Fig. 7 by means of increases in MBCe (due to
the LRT plume's smoke component), in the accumulation mode abundance in the
dN/dlogD contour plot, in the concentrations NCN and NCCN(S),
and in κ(S,Da).
The plume's influence on the ATTO site lasted for about 4 days, with its
onset on 9 April ∼ 12:00 and its end on 13 April
∼ 12:00, which is in good agreement with the corresponding
remote-sensing data in Moran-Zuloaga et al. (2017). During these 4 days,
meteorology determined the variability of the LRT aerosol in the rain forest BL
via air mass advection (i.e., wind speed and direction), rain-related
scavenging (i.e., PATTO and PTRMM), and convective mixing
(represented by SWin as a proxy). Throughout the LRT period, the dominant
wind direction was mostly NE to E, which is characteristic for wet-season
conditions (compare Fig. 3). Note that the BTs arriving from the NE are
particularly prone to bringing dust-laden air into the ATTO region
(Moran-Zuloaga et al., 2017). Furthermore, only few (major) rain events and
related aerosol scavenging occurred during this time, which is a further
prerequisite for efficient LRT transport. The arrival of the LRT plume during
afternoon hours of 9 April occurred via convective downward mixing of the
aerosol into the forest BL (see simultaneous increases in SWin,
θe, and the accumulation mode in dN/dlogD), and its influence
lasted until 13 April, when the concentration time series decreased
gradually. Note that the advected LRT plumes are transported into the ATTO
region as compact and stratified aerosol layers up to altitudes of 2–3 km
(Moran-Zuloaga et al., 2017). Aerosol concentrations were highest during the
afternoon hours on 9, 10, and 11 April, when convection (see SWin) is
most efficient in mixing the aerosols downward into the BL. Furthermore, a
sudden rain-related air mass change, lasting for about 12 h (see wind
direction and θe changes) at the end of 9 April/beginning of
10 April, was associated with a simultaneous drop in LRT aerosol
concentrations. The CCN population responded noticeably to the injection of
the LRT event, which can be seen in NCCN(S),
NCCN(S)/NCN,10, and κ(S,Da) in Fig. 3. Figure 7 displays
the NCCN(0.2 %) and NCCN(0.5 %) time series, which show a 2-
to 3-fold increase upon LRT aerosol intrusion. Moreover, the κ(S,Da) data can be used as an
indirect measure for the aerosol chemical
composition, reflecting the average ratio of organic to inorganic
constituents in the particles. In Fig. 7d, the occurrence of elevated
κ(S,Da) levels coincides with the variability in dN/dlogD. For this
particular event, κAcc increased from ∼ 0.3 to
∼ 0.4, whereas κAit increased from
∼ 0.15 to ∼ 0.25.
Figure 6c summarizes the characteristic NCN(D) and NCCN(S,D) size
distributions as well as the size dependence of κ(S,Da) during
LRT influence. In general, the comparison of Fig. 6a and c contrasts the
characteristic LRT vs. PR conditions and emphasizes that the intrusion of African
LRT plumes into the wet-season BL has significant influence on the aerosol and
CCN populations. Specifically, the accumulation mode under LRT conditions
(Dacc ≈ 180 nm, Nacc ≈ 300 cm-3) is about 5×
enhanced in comparison to the PR episodes. The Aitken mode
(DAit ≈ 80 nm, NAit ≈ 120 cm-3) shows
comparable absolute strength to that under PR conditions (see Table 2) but appears
in the LRT NCN(D) distribution only as a shoulder. The NCCN(S,D) size
distributions show that supersaturations S ≤ 0.3 % activate
particles mostly in the accumulation mode range, whereas S > 0.3 % starts activating the Aitken mode population.
The κ(S,Da) size distribution shows κAcc reaching rather high
levels up to 0.40 (κAcc = 0.35 ± 0.04) and κAit with values up to 0.2
(κAit = 0.18 ± 0.02).
In comparison to the PR conditions, κAcc was clearly enhanced due
to the presence of aged, internally mixed, and salt-rich particles (see
discussion in subsequent section). In contrast, the κAit levels
were only slightly higher than during the PR periods, probably due to a
broader accumulation mode base, which reached into the Aitken mode range.
Note that the relative increase in κ(S,Da) for the contrasting
LRT and PR conditions is large enough to exert an influence on the CCN
population, which is an exceptional case throughout the Amazonian seasons,
since the κ(S,Da) levels for most of the year vary within a
rather narrow range (0.1 to 0.2) (see Part 1 paper).
The African LRT plumes that frequently impact the Amazon Basin during the wet
season comprise a complex mixture of different aerosols components,
including (i) a fraction of Saharan dust (mostly > 1 µm),
(ii) biomass burning aerosols from fires in West Africa (mostly in the
accumulation mode), and (iii) marine aerosols from the plume's transatlantic
passage (in coarse and accumulation modes) (Andreae et al., 1986; Talbot et
al., 1990; Swap et al., 1992; Weinzierl et al., 2017). Note that the African
fires in West Africa are to a large extent agriculture-related and, thus,
man-made (e.g., Barbosa et al., 1999; Capes et al., 2008). Fresh soot and
uncoated dust particles typically have low hygroscopicities, whereas sea
salt particles (i.e., NaCl and sulfates) represent comparatively efficient
CCN. The LRT plumes make a rather long atmospheric journey – about 10 days
according to Gläser et al. (2015) – which is associated with strong
atmospheric processing and typically results in complex internally mixed
particles (see Andreae et al., 1986). The resulting mixtures of dust and
salt as well as coated soot particles, as commonly found in the dust plumes,
readily act as CCN at realistic S levels (< 1 %) (Andreae and
Rosenfeld, 2008).
Table 3 summarizes the ACSM results on aerosol chemical composition during LRT
influence. Note that the ACSM only measures the
non-refractory fraction of the particle mass and, thus, does not detect a
large fractions of the dust and salt constituents. During LRT conditions and
with respect to the non-refractory part, organic mass dominated the particle
composition (67 %), with minor nitrate contributions (3 %) and larger
fractions of ammonium (11 %) and sulfate (9 %). Furthermore, a slight
sea-salt-related increase in detected chloride mass (1 %) relative to the
PR state was observed. Based on the ACSM results, a predicted κp
of 0.24 ± 0.01 was obtained, which agrees reasonably well with the
measured hygroscopicity level, κ(0.11 %) = 0.35 ± 0.04.
Accordingly, the observed elevated fractions of sulfate and ammonium were
responsible for part of the increase in κ(S,Da). The remaining
difference between κp and κ(0.11 %) can likely be
explained by further refractory inorganics that were not covered by the
ACSM. As an illustration of the complex mixing state of the African LRT aerosol
population, Figs. S10 and S11 provide selected SEM-EDX data from a
characteristic LRT aerosol sample. Note that these results merely serve as a
qualitative example here, whereas a quantitative microspectroscopic analysis
of LRT samples is the subject of ongoing work. The observed particles' morphology
underlines the influence of atmospheric processing and shows a high degree
of internal mixing. Chemically, the EDX maps show dust particles, sulfate
salts, and a rather large abundance of NaCl particles.
Selected meteorological, aerosol, and CCN time series from ATTO
measurements for the biomass burning case study period, BB (see Fig. 4).
(a) Incoming shortwave radiation, SWin; precipitation rates from TRMM
satellite mission, PTRMM; and in situ measurements at the ATTO site,
PATTO. (b) Wind direction and wind speed, U, at ATTO. (c) Equivalent
potential temperature, θe, and relative humidity, RH, at
ATTO. (d) Overlay of two data layers showing aerosol number size distribution
contour plot, dN/dlogD, as well as color-coded markers, representing time
series of κ(S,Da) size distributions. (e) CCN concentrations,
NCCN(S), for two selected S levels; total aerosol number concentration,
NCN,10; and BCe mass concentration, MBCe. (f) ACSM-derived
sulfate mass concentrations and organic-to-sulfate mass ratio.
Figure 6d displays the characteristic CCN efficiency spectrum for the LRT
conditions, which – expectedly – shows a rather steep increase at low S due
to two effects pointing in the same direction: an enhanced accumulation mode
and the presence of rather “good” CCN with comparatively high κ(S,Da) levels. This large initial slope corresponds to a very small
value for the characteristic variable S1 = 0.09 %, which shows
that, at comparatively low S (lowest S1 among all analyzed
conditions), 50 % of the aerosol population were already activated as CCN (Table 4). At S
levels around 0.3 %, ∼ 80 % of the aerosols acted
as CCN; “full” activation (90 %) was reached at just
S ≈ 0.5 %. At higher S (i.e., > 0.9 %) the efficiency spectrum
approaches unity. This is in stark contrast to the PR CCN efficiency
spectrum in Fig. 6b, where 50 % of the particles were activated at
significantly higher S around 0.5 %, whereas full activation (90 %)
was reached at high S
around 1.5 %. Thus, the LRT episodes show a pronounced sensitivity to
ΔS only in the low-S regime, in contrast to the
PR case with a
sensitivity to ΔS spanning across a wide S range. With the
characteristic shape of the CCN efficiency spectrum (i.e., rather strong
accumulation mode) and the rather low aerosol concentrations
(NCN,10 ≈ 440 cm-3, Table 4), the LRT
conditions are located at the border between aerosol-limited and transitional
regimes (compare Reutter et al., 2009; Pöschl et al., 2010).
Case study BB on aged biomass burning smoke
During the dry season, the ATTO site is frequently influenced by smoke from
biomass burning (BB) activities in different regions of the Amazon forest
(Freitas et al., 2005; Andreae et al., 2012, 2015; C. Pöhlker et al.,
2018). As a characteristic example, the case study BB focuses on the strong
biomass burning plume that was observed at the ATTO site in the middle of
August 2014. During this event, pollution aerosol and trace gas
concentrations reached their annual maxima, with NCN,10 peaking
at 5000 cm-3, cCO peaking at 350 ppb, and
MBCe reaching up to 2.5 µg m-3, as
shown in Fig. 4. Selected meteorological, aerosol, and CCN time series of the
event are shown in Fig. 8. This case study provides the opportunity to
analyze the CCN properties of aged smoke from a rather defined large-scale
BB plume in the Amazon region.
Figure 8 shows that the influence of the BB smoke plume on the ATTO site
lasted for about 6 days (18 to 22 August) with a gradual onset and decay. In
terms of meteorology, this period was characterized by mostly cloud-free
conditions (see SWin) without precipitation, comparatively low RH
levels, and some variability in wind direction. Note that, during the
presence of this major smoke plume, atmospheric dimming in the ATTO
region could be recognized (i.e., compare SWin maxima for the seven
consecutive days). The signature of the smoke aerosol particles can very
clearly be seen in the dN/dlogD contour plot, NCN, as well as MBCe. In
terms of particle size, the pronounced increase mostly occurred in the
accumulation mode, which appears to be comparatively broad for this specific
event. The CCN concentrations – e.g., NCCN(0.2 %) and
NCCN(0.5 %) – track the relative increase in total aerosol abundance
and show a 4- to 5-fold increase as well. At the same time, the presence of
the pyrogenic aerosols correspond to a clear drop in aerosol hygroscopicity
in both the Aitken (ΔκAit ≈ -0.05) and
accumulation modes (ΔκAcc ≈ -0.1), relative to
the conditions before and after the major BB plume (see overlay of κ(S,Da) size distributions and the
dN/dlogD contour plot in Fig. 8d). This
drop in κ(S,Da) is associated with a high organic-to-sulfate,
OA / SO42-, ratio (Fig. 8f), reflecting the dominant role of organic
constituents in biomass burning particles as documented in previous studies
(Fuzzi et al., 2007; Artaxo et al., 2013; Lathem et al., 2013).
Geographically, the location of the fires and, thus, the origin of the strong
biomass burning plume could be found by means of a combination of BTs and
satellite data products as shown in Fig. S12 (Draxler and Hess, 1998; Acker
and Leptoukh, 2007; Justice et al., 2011). The strong pollution at the ATTO
site resulted from the presence of intense fires in the southern Amazon and a
temporary “swing” of the BT track over the fire locations. During the dry
season, the BTs mostly belong to the E and ESE clusters (see Figs. S1 and 4).
For the period before and after the biomass burning plume, the BT track
follows a “coastal path” and enters the continent in the region of the
Amazon River delta (about 0∘ N 50∘ W). Subsequently, the
air masses “follow” the Amazon River in westerly directions to the ATTO
site. During the peak period of the BB event the BTs deviate from the
coastal path and follow an “inland path” across the southeast of Brazil
(see Figs. S12a and 4). At about 7∘ S and 55∘ W, the BTs
intersect a region with strong fire activities, which are clearly visible in
satellite products such as NO2 total column measurements. These
fires are localized along the Cuiabá–Santarém highway (BR-163). This
highway corridor is known as a region of intense logging and burning of
primary forest and its conversion to cattle pasture (Nepstad et al., 2002;
Fearnside, 2007; C. Pöhlker et al., 2018). The transport time of the
smoke from the fires to the ATTO site is about 2–3 days (given by the BTs),
which provides a reference for the residence time and aging of the aerosol
particles in the atmosphere. The satellite image in Fig. S12b shows the smoke
plume that originated in the BR-163 region and traveled northwestwards. It
clearly impacts the area around Manaus and the ATTO site precisely during the
“event days” in August.
For a further characterization of the BB case study, we calculated a ratio of
excess NCN,10 to excess cCO (ΔNCN,10 / ΔcCO) for the event of 17.9 ± 0.7 cm-3 ppb-1 (see Fig. S13a), which agrees well with the typical range for a variety of vegetation
fires (30 ± 15 cm-3 ppb-1), in contrast to much higher
ΔNCN,10 / ΔcCO levels for urban
(100–300 cm-3 ppb-1) and power plant emissions (up to
900 cm-3 ppb-1) (Janhäll et al., 2010; Kuhn et al., 2010;
Andreae et al., 2012). Furthermore, the ratios of excess NCCN(S) to
excess cCO, ΔNCCN(S)/ΔcCO, for the individual S
levels range between 6.7 ± 0.5 cm-3 ppb-1 for the lowest
S = 0.11 % and values around 18.0 ± 1.3 cm-3 ppb-1 for
higher S (see Table S4 and Fig. S13b). The ΔNCCN(S)/ΔcCO ratios converge against ΔNCN / ΔcCO
even for comparably small S (i.e., > 0.24 %), which can be
explained by the fact that small S already activate a substantial fraction of
the pronounced (mostly pyrogenic) accumulation mode (see discussion below).
Kuhn et al. (2010) reported ΔNCCN(0.6 %) / ΔcCO
ratios around 26 cm-3 ppb-1 for biomass burning plumes, which is
consistent with our observations (i.e., ΔNCCN(0.6 %) / ΔcCO = 17.9 ± 1.3 cm-3 ppb-1; see Table S4). The obtained ΔNCCN(S) / ΔcCO ratios were utilized in a dedicated CCN
parameterization scheme in our Part 1 study (Pöhlker et al., 2016).
Figure 6e summarizes the corresponding NCN and NCCN(S) size
distributions as well as the size dependence of κ(S,Da) for the
BB case study. It shows a very strong accumulation mode (Dacc ≈ 170 nm, Nacc ≈ 3400 cm-3), “swamping”
the Aitken mode
(DAit ≈ 70 nm, NAit ≈ 140 cm-3) almost
completely, giving the entire distribution a monomodal appearance. For
S < 0.5 % almost the entire aerosol population is activated as
CCN. The averaged κ(S,Da) levels are rather low for both the
Aitken and accumulation modes (κAit = 0.14 ± 0.01 and
κacc = 0.17 ± 0.02), in which κacc is
among the lowest values found in the accumulation mode size range throughout
the entire study. The low κ(S,Da) levels can be explained by the
fact that pyrogenic aerosols predominantly contain organic constituents and
rather low levels of inorganic species. The ACSM results during the BB period
emphasize the predominant mass fraction of organics (87 %) as well as the
minor contribution by nitrate (2 %), ammonium (3 %), and sulfate
(4 %) (Table 3). The predicted κp of 0.15 ± 0.01
agrees reasonably well with the measurement result, κ(0.11 %) = 0.18 ± 0.01.
Figure 6f displays the characteristic CCN efficiency spectrum for the BB
period, which levels out clearly below unity. The dashed line shows the erf
fit forced to go from 0 to 1 (i.e., with the pre-defined variable
a1 = 1), which does not match the data points for S > 0.5 %. This fit has been included here, since the activated fraction
must reach 100 % eventually. For comparison, the solid line is an erf fit
of the data where the plateau is an open fit parameter. This erf fit matches
the data points very well, and the fit parameters as presented in Table 4
reveal the plateau at 93 %. Physically, this indicates the presence of an
externally mixed aerosol population with 7 % of the particles being
hydrophobic (e.g., a certain fraction of rather fresh soot) and not acting
as CCN in the measured S range. The slope of the efficiency spectrum shows a
steep increase for S < 0.4 %. According to these findings, the
ΔS-sensitive range is rather small with S < 0.4 %.
According to Reutter et al. (2009), the BB case study conditions with the
large number of available CCN (e.g., NCCN (0.47 %) > 4000 cm-3) fall into the updraft-limited regime.
Selected meteorological, aerosol, and CCN time series from
ground-based ATTO site measurements for the mixed pollution case study
period, MPOL (see Fig. 4). (a) Incoming shortwave radiation,
SWin; precipitation rates from TRMM satellite mission, PTRMM; and in situ
measurements at the ATTO site, PATTO. (b) Wind direction and wind speed,
U, at ATTO. (c) Equivalent potential temperature, θe, and
relative humidity, RH, at ATTO. (d) Overlay of two data layers showing
aerosol number size distribution contour plot, dN/dlogD, as well as
color-coded markers, representing time series of κ(S,Da) size
distributions. (e) CCN concentrations, NCCN(S), for two selected S
levels; total aerosol number concentration, NCN,10; and BCe mass
concentration, MBCe. (f) ACSM-derived sulfate mass concentrations and
organic-to-sulfate mass ratio. The vertical shading highlights episodes
under the influence of local/regional fires (MPOL-BB) vs. periods that are
dominated by long-range transport of African aerosols (MPOL-LRT).
It can be expected that the large aerosol load, its optical properties, and
its ability to serve as CCN must already influence cloud properties and the
stability of the thermodynamic profile of the atmosphere at both local and
regional scales (Cecchini et al., 2017b). As mentioned above, the incoming
solar radiation was dimmed compared to the days before and after this BB event.
Furthermore, the cloud fraction decreased, which might result from
stabilizing the atmosphere due to increased absorption of solar radiation in
and above the boundary layer, the “semidirect effect” (e.g., Koren et al.,
2004; Rosenfeld et al., 2008; Rosário et al., 2013). A detailed
investigation of the direct radiative forcing and the modification of cloud
properties by aerosol particles in the Amazon rain forest are clearly beyond
the scope of this study. Nevertheless, the results shown here may serve as
input for dedicated regional climate simulations.
Case study MPOL on the complex aerosol mixtures during the dry
season
The previous BB case study presented the aerosol and CCN properties of a
strong and well-defined regional biomass burning plume. However, the
dry-season aerosol mixture at ATTO can be rather complex due to the influence
of a variety of sources, such as biomass burning, fossil fuel combustion, and
industrial emissions from local/regional sources as well as from African
long-range transport, in addition to the natural aerosol background (i.e.,
PBAP, SOA, marine aerosols) (e.g., Freud et al., 2008; Artaxo et al., 2013;
Saturno et al., 2017a; C. Pöhlker et al., 2018). Accordingly, the
dry-season aerosol mixture is highly variable and no single episode reflects
the conditions comprehensively. Therefore, we defined a mixed pollution
(MPOL) case study, which is evidently influenced by local/regional biomass
burning in South America and the long-range transport of African aerosols.
The MPOL case serves as an example of the highly variable aerosol
conditions during the Amazonian dry season. The 7-day MPOL period in
September 2014 shows an exceptionally low OA / SO42- mass
ratio (Table 3). Saturno et al. (2017b) recently showed that the high sulfate
concentrations can be explained by the long-range transport of volcanogenic
aerosols from Africa (i.e., from the Nyiragongo and Nyamuragira volcanoes in
eastern Congo) into the Amazon (compare also Fioletov et al., 2016). While
the volcanogenic aerosol shows characteristic properties that allow this
LRT component to be distinguished from the local/regional BB aerosol,
African BB aerosols can be transported similarly; however, an experimental
discrimination of African vs. Amazonian BB aerosol is difficult (see
Saturno et al., 2017a). Upon arrival of the African plume in the Amazonian
atmosphere, the LRT aerosol has mixed with local/regional fires emissions.
Meteorologically, the time frame of the MPOL period was mostly cloud-free (see
daily profile in SWin) and without precipitation (see PTRMM and
PATTO), as well as characterized by rather stable wind conditions (Fig. 9). With respect to the aerosol and CCN parameters, the relevant time
series – such as NCN, MBCe, and NCCN(S) – show a high variability.
Specifically, the dN/dlogD contour plot shows an alternating pattern: on one
hand, and marked by a grey shading, rather extended periods with comparably
low aerosol concentrations (around 1000 cm-3) and high κ(S,Da) values (up to 0.35) can be observed. The κ(S,Da)
levels reaching ∼ 0.35 are among the highest in the entire dry season of 2014 (compare Fig. 4). On the other hand, and marked by a light purple
shading, these conditions are interrupted by several pulses with strongly
enhanced aerosol concentrations (up to 5000 cm-3) as well as
substantially lower κ(S,Da) values (around 0.1). The changes in
κ(S,Da) occur with simultaneous changes in the aerosol chemical
composition: slight increases of the OA / SO42- ratio indicate that
the pulses comprise organic-rich aerosol due to the influence of
local/regional BB plumes, whereas the lower OA / SO42- aerosol is
comparatively rich in sulfate due to the volcanogenic origin.
Average probability distribution of particle hygroscopicity,
dH/dlogκ, on the left side and the same quantity weighted by the
particle number size distribution, (dH ⋅ dN)/(dlogκ ⋅ dlogD), on the right side, plotted over the effective
hygroscopicity parameter, κ, and dry-particle diameter, D, for (a and b) the entire measurement period as well as
(c and d) PR, (e and f) LRT, (g and h)
BB, (i and j) MPOL-LRT, and (k and l) MPOL-BB conditions. The particle size distributions
used for the weighting are shown in Fig. 6.
A combination of BTs and satellite data during the MPOL period shows that
all BTs follow an easterly direction along the Amazon River (Fig. S14), which
is the most frequent scenario during the dry season (see C. Pöhlker et
al., 2018). The same study also shows that during this period the shores of
the Amazon River can be regarded as the core region of the ATTO site
footprint, where BT densities are highest. Accordingly, all aerosol and trace
gas sources in these areas are of primary importance for the ATTO region. For
instance, this is true for a deforestation hot spot in the area around the
cities Oriximina and Òbidos as well as a variety of anthropogenic sources
(i.e., industry, power plants, cities, shipping) (compare Fig. S14 and
C. Pöhlker et al., 2018). During the MPOL time frame, several (smaller)
deforestation fires were observed within the fetch of the corresponding BTs
(Fig. S14). These fires, which mostly burnt for less than a day according to
satellite observation, are likely responsible for the
low-κ(S,Da) and high-OA / SO42- pulses
within the MPOL period. Thus, the pulses can be considered as the advection
of nearby biomass burning plumes into the ATTO region. According to the BT
data, the smoke experienced a transport and aging time of 12–18 h. Thus,
the smoke plumes during MPOL are fresher than during the BB case study,
which experienced atmospheric aging for 2–3 days. This difference in aging
might be related to differences in the corresponding κ(S,Da)
values, with fresh smoke during MPOL showing a lower hygroscopicity
(κ<100nm,MPOL ≈ 0.10) and the aged
smoke showing higher values (κAit,BB ≈ 0.14,
κacc,BB ≈ 0.17). Note that biomass burning smoke
cannot explain the sulfate-rich aerosol during MPOL since BB-related
sulfate contents are typically below 6–7 % (e.g., compare BB case in
Table 3 as well as Fuzzi et al., 2007; Gunthe et al., 2009; Saturno et al.,
2017b).
Figure 6g and h summarize the NCN(D), NCCN(S,D), and κ(S,Da) size distributions during MPOL,
separately for the biomass burning
pulses (MPOL-BB with OA / SO42-≈ 4) and LRT influence (MPOL-LRT with
lower OA / SO42-≈ 3). In both cases, the size
distributions show a broad monomodal distribution, which did not allow a
stable double lognormal mode fitting of Aitken and accumulation modes (see
Table 4). The mode is centered at ∼ 135 nm for MPOL-LRT, whereas the
relatively fresh biomass burning smoke during MPOL-BB shows a modal diameter of
∼ 113 nm, in agreement with ∼ 100 nm for fresh
biomass burning smoke reported by Andreae et al. (2004). The corresponding
NCCN(S,D) size distributions show similar shapes, albeit with
substantial absolute differences in CCN concentrations. Furthermore, clear
differences are also observed for the average κ(S,Da) size
distributions: during MPOL-LRT, κ(S,Da) shows a pronounced size
dependence. The average κ for sizes < 100 nm is rather size
independent (κ<100nm= 0.14 ± 0.01). Instead, the
average κ>100nm increases with D up to 0.26 (on average:
κ>100nm= 0.22 ± 0.03). According to the shape of the
κ(S,Da) size distribution in Fig. 6g, it can be assumed that the
aerosol hygroscopicity was even higher for D larger than the covered size
range here (i.e., > 170 nm). During the dry season, the highest
κ(S,Da) levels for accumulation mode particles were observed
during the peak abundance of sulfate at the ATTO site. The hygroscopicity
during the MPOL-BB pulses shows generally lower values and weaker size
dependence for particles < 150 nm. The average
κ<150nm = 0.10 ± 0.01 was even smaller than during BB and PR conditions and
represents one of the lowest values measured during the entire observation
period. For larger particles (e.g., > 150 nm), κ(S,Da) strongly increased towards values comparable to those during
MPOL-LRT, showing that both conditions are superimposed (on average: κ>150nm= 0.20 ± 0.04).
Figure 6i shows the CCN efficiency spectra for the two MPOL states. The slope of
the MPOL-LRT spectrum is significantly steeper than the slope of the MPOL-BB spectrum. The
S1 level of 50 % activation for the sulfate-rich aerosol population
(0.16 %) is clearly lower than during the short biomass burning pulses
(0.28 %). This rather large difference can be explained by the smaller
modal diameter of the MPOL-BB relative to the MPOL-LRT case. Furthermore, κ is
significantly decreased for the biomass burning pulses, which are dominated
by organic constituents. However, note that the absolute CCN concentration
is – while being less hygroscopic – significantly higher for MPOL-BB periods.
In general, the MPOL case study emphasizes the following aspects: (i) the
dry-season aerosols that arrive at the ATTO site can be rather complex mixtures
of superimposed emissions from different sources with contrasting chemical
properties. (ii) The properties of the aerosol and CCN population can change
rather suddenly and substantially (see NCN and NCCN(S) changes by
a factor of 3–4 within a few hours). Similarly, the aerosol hygroscopicity can
vary quite strongly from the lowest value observed in the entire study
(∼ 0.1) to values around 0.35 and higher, which are among to the
largest values observed here during the dry season. These quickly and
substantially changing aerosol regimes will presumably also impact the cloud
conditions during the dry season. (iii) Finally, the Amazonian aerosol and CCN
population can be substantially perturbed by emissions from sources (e.g.,
volcanoes in eastern Congo) that are remarkably far away from the ATTO
site (∼ 10 000 km) (Saturno et al., 2017b). This emphasizes
very clearly that intercontinental influences have to be considered
carefully in the analysis of the Amazonian atmospheric composition.
Aerosol particle hygroscopicity distributions and aerosol mixing
state
In this section, we investigate the aerosol particle mixing state for the
different case study conditions with the help of the aerosol particle
hygroscopicity distribution – or κ distribution – concept
introduced by Su et al. (2010). This approach visualizes the spread of
κ values among particles of a given size. Specifically, in an ideal
internal particle mixture, all particles have the same chemical composition
and therefore the same hygroscopicity, resulting in a narrow and defined
κ distribution. In an external mixture, the particles at a given
size can have widely different chemical compositions and hygroscopicities,
resulting in a broad κ distribution.
Figure 10 summarizes two versions of κ distributions for the
contrasting case study conditions: (i) the “classical” κ
distributions according to Su et al. (2010), which emphasize the spread of
κ levels for all particle diameters across the measured size range,
and (ii) κ distributions weighted with the corresponding average
particle size distributions from Fig. 6, which provide a quantitative
overview of particle abundance as a function of hygroscopicity and size
(NCN κ distribution). The comparison of the κ and
NCN κ distributions for the contrasting case study conditions
emphasizes similarities and differences between the corresponding aerosol
populations, which allows drawing conclusions on the aerosol mixing state
and microphysical properties.
The κ distributions for most conditions reflect a bimodal character
of the corresponding aerosol distributions with distinctly different
properties in the Aitken and accumulation modes as outlined in Fig. 6. Specifically, all distributions show an increasing
spread of κ levels towards larger particle diameters, which suggests
a higher degree of external particle mixing and therefore a higher diversity
of particle properties (i.e., hygroscopicity) in the accumulation than in
the Aitken mode. As an example, the BB and MPOL-BB cases show a κ spread in
the accumulation mode range that reaches from values well below 0.1 up to
levels of about 1. Remarkably, the PR κ distribution differs from all
other cases since it shows overall the smallest spread of κ over the
entire size range. This suggests the Aitken and accumulation mode particles
under PR conditions are two distinct and chemically rather homogeneous aerosol
populations with a comparatively high degree of internal mixing. As an
example, the PR Aitken mode particle population covers a defined κ range
between ∼ 0.1 and ∼ 0.15. In contrast, the
LRT, BB, and MPOL aerosol populations appear to be more externally mixed.
Overview and comparison of normalized CCN efficiency spectra and
number-concentration-based CCN spectra for characteristic conditions and
seasons. (a and b) PR, LRT, BB, MPOL-LRT, and MPOL-BB conditions as defined in this paper.
(c and d) Seasonally averaged spectra from the Part 1 companion paper (Pöhlker et al., 2016), CCN efficiency spectrum
from Roberts et al. (2001), and CCN spectra from Roberts et al. (2003) and Gunthe et al. (2009). (e and f)
Conditions representing different aging states of biomass
burning aerosols based on BB and MPOL-BB conditions from present work and data from a
previous study by Andreae et al. (2004). For clarity the erf fit with the
pre-defined variable a1 = 1 was used for the BB case (compare Fig. 6f).
CCN spectra in (b) were obtained from multiplication of CCN efficiency
spectra in (a) with corresponding average aerosol number concentrations in
Table 4. Markers represent measured average CCN concentrations for case
study periods. Error bars at markers represent 1 SD. Good agreement of
CCN spectra and markers underlines reliability of CCN efficiency spectra in
representation of CCN population. Blue vertical shading represents estimated
peak supersaturations at cloud base, Scloud(DH,κ), in the
ATTO region according to Pöhlker et al. (2016) and the present study. The
colors were chosen according to Wong (2011).
In addition to the diversity of the hygroscopicity as visible in the
κ distributions, the NCN κ distributions further
emphasize the quantitative abundance of particles in the hygroscopicity-size
space. Accordingly, the NCN κ distributions can be regarded as
signature-like representations of the aerosol microphysical state under
certain conditions. Note that the NCN κ distributions in Fig. 10
differ substantially from each other. The PR case shows a unique signature.
The LRT and MPOL-LRT cases show certain similarities due to the fact that both are
characterized by similar aerosol size distributions and comparatively high
κ levels. The comparison of the relatively fresh MPOL-BB smoke and the
relatively aged BB smoke emphasizes the aging-related increases in particle
size and hygroscopicity by means of a characteristic shift of the dominant
mode in the NCN κ distributions. In both cases the spread of
κ is rather large, indicating a comparatively strong external mixing
in the smoke plumes. In essence, the κ distributions and NCN
κ distributions are valuable overview representations, which
combine characteristic aerosol properties in terms of particle size,
particle concentration, κ diversity, and the aerosol mixing state in a
fingerprint-like manner. So far, only very few studies (e.g., Mahish et al.,
2018) have used the κ distribution or related concepts to
characterize ambient aerosol properties. In light of the results in Fig. 10,
we suggest that this concept should be used more broadly as it provides
valuable insights into the particle mixing state beyond the more established
characterizations of aerosol and CCN properties.
Overview of CCN efficiency spectra and corresponding CCN spectra
CCN efficiency spectra serve as normalized CCN signatures. Their shape is
influenced by (i) the shape of the NCN size distribution (i.e., relative
strength of Aitken vs. accumulation modes), (ii) the aerosol composition
through the κ(S,Da) values and its size dependence, and
(iii) the mixing state of the aerosol as represented in the κ and
NCN κ distributions. CCN spectra provide quantitative
information on the actual CCN concentrations as a function of S. From the CCN
efficiency spectra, CCN spectra are readily obtained by multiplication with
the corresponding average NCN concentrations (Table 4). This last
section combines the CCN efficiency spectra (i.e., the erf fits) and CCN
spectra of all analyzed case study conditions from this work with the
seasonally averaged spectra from the Part 1 study and literature
data. Figure 11 shows an overview of all spectra. The direct comparison
emphasizes characteristic similarities and differences as a basis for a
concluding discussion on aerosol–cloud interactions. Note in this context
that Farmer et al. (2015) presented “cumulative numbers of CCN, normalized
to CN”, which is conceptually related to the CCN efficiency spectra
reported here. Accordingly, a comparison of the spectra reported by Farmer
et al. (2015) with Fig. 11 may help to put the characteristics of the
Amazonian CCN population into a broader context.
The peak supersaturation and its variability are an essential parameter to
understand aerosol–cloud interactions. However, only sparse quantitative
information on the actually relevant S range is available. The spectra in
Fig. 11 are plotted for a broad S range from 0.001 to 20 %. According to
Reutter et al. (2009), this range can be generally subdivided into very low
(S < 0.1 %), low (S < 0.2 %), transitional
(0.2 % < S < 0.5 %), and high (S > 0.5 %) supersaturation regimes. Furthermore, a
recent study by Fan et al. (2018) suggests that under certain conditions even very high
supersaturations (S ≫ 1 %) can be reached in
deep convective clouds. One approach to actually quantify ATTO-relevant
average peak S at cloud base, Scloud(DH,κ), uses the position
of the Hoppel minimum, DH, as outlined in Hoppel et al. (1986) and
Krüger et al. (2014). According to results in our Part 1 companion paper
and the present study (see Table 2), ATTO-relevant
Scloud(DH,κ) values range from ∼ 0.15 to
∼ 0.34 %. This Scloud(DH,κ) range is shown
as a “S landmark” in Fig. 11 for orientation.
The array of condition-specific CCN efficiency spectra in Fig. 11a shows the
diversity of spectral shapes. The PR-specific spectrum represents the lower
limiting case with the “slowest” increase in activated fraction upon
increasing S (e.g., 50 % activation reached at S ≈ 0.5 %),
whereas the BB and LRT spectra represent the upper limiting cases, reaching high
activated fractions at relatively low S (50 % activation at S ≈ 0.15 % for the BB case and at
S ≈ 0.1 % for the LRT case).
The slopes of the spectra, d(NCCN(S)/NCN,10)/dS, indicate how
sensitively the activated fractions react towards changes in
supersaturation, ΔS. Overall, the highest sensitivities towards
ΔS occur between ∼ 0.1 and ∼ 1.0 %. However, the cases have to be investigated individually to obtain
condition-specific “high-sensitivity regimes”. For low S (< 0.1 %) – corresponding to
the activation of accumulation and coarse-mode particles – the aerosol populations that act as CCN most efficiently
are those with strong accumulation modes and high κ levels, such as
the sea-salt-rich LRT and sulfate-rich MPOL-LRT populations. In contrast, the PR aerosol
acts as CCN least efficiently as it is characterized by a small fraction of
accumulation mode particles with rather low κ. In the transitional
S regime with 0.2 % < S < 0.5 % – corresponding to
the activation of particles between the Aitken and accumulation modes (i.e.,
the Hoppel minimum size range) – the individual spectra show their highest
divergence and thus the largest differences in CCN efficiency. Note that
this range is collocated with the experimentally deduced and ATTO-relevant
Scloud(DH,κ) levels. For high S (> 1 %) –
corresponding to the activation of Aitken mode and even smaller particles –
full activation is reached and sensitivities towards ΔS decrease.
For comparison, the season-specific CCN efficiency spectra from our Part 1
study are shown in Fig. 11c. Generally, the shapes of the condition-specific
spectra and the season-specific spectra agree well (e.g., relationship
between wet and dust seasons and between PR and LRT conditions). However, the
characteristic spectral features are more pronounced for the conditions than
for the seasons, due to the fact that the case studies on certain conditions
incorporate more defined aerosol plumes and/or populations. As an example,
the LRT season spectrum includes several LRT events, as well as clean
periods in between, whereas the LRT case study spectrum includes only the core
period of one defined LRT episode (see Table 1).
Since the size and composition of aerosol particles change dynamically due to
atmospheric aging and processing, the CCN efficiency spectra also change accordingly. Figure 11e emphasizes the dynamic
character of the shape of the CCN efficiency spectra by means of different
aging states of biomass burning plumes. Here we combined four
biomass-burning-related CCN efficiency spectra: (i) the BB case study
spectrum from this work, which represents smoke after ∼ 2–3 days of
atmospheric aging; (ii) the MPOL-BB case study spectrum from this work,
which represents smoke after ∼ 1 day of aging; (iii) “cloud-processed
smoke” from biomass burning regions in southeast Brazil after hours to days
of atmospheric aging according to Andreae et al. (2004); and (iv) “fresh
smoke in the mixed layer”, also from southeastern Brazil, which was sampled
in the fire plumes and thus was aged for minutes to hours only (also from
Andreae et al., 2004). The spectral shapes in Fig. 11e clearly differ: the
lowest activated fractions were observed for the fresh smoke (50 %
activation at S≈ 1.0 %; 90 % at S≈ 4.0 %),
followed by the cloud-processed smoke (50 % activation at S≈ 0.5 %; 90 % at S≈ 2.0 %), then the MPOL-BB spectrum
(50 % activation at S≈ 0.3 %; 90 % at S≈ 1.2 %), and finally the BB spectrum (50 % activation at S≈ 0.2 %; 90 % at S≈ 0.4 %) with the highest
activated fractions as the upper limiting case. Atmospheric aging tends to
increase the particle size through coagulation and condensational growth, and
to enhance the particle hygroscopicity through oxidation, aqueous-phase
chemistry, and reaction product deposition. Therefore, aging tends to
increase the strength of the accumulation at the expense of the Aitken mode
and increases κ. Accordingly, it evolves the smoke's CCN efficiency
spectra from the “fresh-smoke” conditions – as the initial state –
towards the BB case study conditions and presumably even further. This
underlines the well-known trend that atmospheric aging increases the
suitability of a given particle population to act as CCN (Andreae and
Rosenfeld, 2008). Important to note in the context of this study is the
following: the CCN efficiency spectra represent signatures of a given aerosol
population in a given state of atmospheric aging. Accordingly, the
atmospheric aerosol aging and the dynamic evolution of the CCN efficiency
spectra's shape have to be kept in mind during discussion and utilization of
the spectra in follow-up studies.
From all CCN efficiency spectra the corresponding CCN spectra were
calculated as displayed in Fig. 11b, d, and f. Moreover, measured CCN
spectra from previous, mostly short-term, campaigns in the Amazon Basin are
also shown and agree well with our results (Roberts et al., 2001, 2003;
Andreae et al., 2004; Gunthe et al., 2009). Beyond the literature data shown
in Fig. 11, good agreement was further observed with Andreae (2009) and
Martins et al. (2009). Overall, Fig. 11 may serve as a basis for dedicated
cloud microphysical studies on the characteristic differences between the
aerosol/CCN populations and their impacts on cloud properties. Particularly,
the results obtained here (i.e., NCN concentrations and CCN efficiency
spectra) can be used to investigate the sensitivity of clouds under PR
conditions, as an approximation for pristine atmospheric conditions in
tropical continental regions. The large differences between PR background
conditions and perturbed atmospheric states related to anthropogenic (aged)
BB smoke in terms of total aerosol concentration as well as the shape of the
CCN efficiency spectra are evident. Similarly, the differences between the
PR and LRT case studies suggest that the frequent LRT events in the wet season are
related to simultaneous changes in the cloud microphysical state, although
the changes in total aerosol concentration are comparatively small.
Summary and conclusions
In a recent synthesis paper on aerosol–cloud interactions and their highly
uncertain representation in global climate models, Seinfeld et al. (2016)
proposed that long-term and focused observations in “geographic areas that
are critical in climate response” are necessary to obtain a detailed
process understanding. Further, studies in “those regions of the
present-day atmosphere that approximate preindustrial conditions” will help
to “replicate preindustrial aerosol–cloud relationships”. In these regions
– such as the Amazon Basin – clouds are particularly sensitive to small
changes in CCN concentrations (Carslaw et al., 2013). Accordingly,
observations are needed here to obtain crucial knowledge on the man-made
perturbation of preindustrial aerosol–cloud interactions. This work aims to
help tackle this major scientific challenge by presenting detailed
long-term aerosol and CCN data for characteristic atmospheric states at the
Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin, which is a
unique, climate-relevant location with atmospheric conditions oscillation
between pristine and anthropogenically strongly perturbed states.
The basis for this work is size-resolved measurements of atmospheric
aerosol and cloud condensation nuclei (CCN) concentrations and
hygroscopicity at ATTO over a full seasonal cycle (March 2014–February 2015). The
results of these observations are presented in two papers: the recently
published Part 1 paper provides an in-depth analyses of the multi-month
patterns in the Amazonian CCN population as well as seasonal averages of the
key CCN parameters (Pöhlker et al., 2016). Further, Part 1 compares
and discusses different CCN parametrization schemes and their suitability to
represent the Amazonian CCN cycling in modeling studies. The present Part 2
study completes this picture by analyzing the CCN variability at the
original time resolution (4.5 h), which is sufficient to resolve its
short-term variability in relation to air mass changes as well as aerosol
emission and transformation processes.
A focal point of both studies is the concept of CCN efficiency spectra, which
represent a tool to visualize the different behaviors of contrasting aerosol
populations in cloud formations and, thus, can be regarded as “CCN
signatures”. Analytically, the CCN efficiency spectra can be described
precisely and in a physically correct way by Gaussian single- or double-error-function (erf) fits. In contrast to other
common analytical functions, the erf approach describes the measurement
results very accurately and allows CCN properties to be extrapolated to
experimentally hardly accessible supersaturations in the low- and high-S
regimes.
Here, we zoom into the long-term CCN data in two steps: first, we discuss
the aerosol and CCN variability for two 2-month periods that represent
contrasting regimes in the aerosol, cloud microphysical, and precipitation
seasonality in the central Amazon. The selected periods provide insights
into the characteristic atmospheric cycling during the clean wet season and
the polluted dry season. Second, we focus on the following four selected
case study periods, which represent particularly relevant atmospheric
states:
Empirically pristine rain forest (PR)
conditions are one of the scientifically most relevant atmospheric states at
ATTO. Here, we defined PR conditions by means of black carbon and CO
concentrations as pollution tracers. At ATTO, the PR frequency of occurrence
peaks between the second half of April and the first half of May. Under PR
conditions, aerosol concentrations are very low (∼ 290 cm-3), and the aerosol population has a characteristic bimodal shape
with a dominant Aitken and comparatively weak accumulation mode
(DAit ≈ 70 nm; NAit ≈ 160 cm-3 vs.
Dacc ≈ 160 nm; Nacc ≈ 90 cm-3). The aerosol
particles are composed of mostly organic matter with minor amounts of
inorganic constituents (OA: 0.64, NO3-:
0.03, SO42-: 0.04, Cl-:
< 0.01, BCe: < 0.01 µg m-3). The observed low
κ(S,Da) levels agree with the
particles' composition and show a size dependence (κAit = 0.12 ± 0.01 vs. κacc = 0.18 ± 0.01). The CCN efficiency spectrum shows a characteristic shape since it
converges towards full activation rather slowly (50 % activation at
∼ 0.5 %, 90 % activation at ∼ 1.5 %).
Thus, the CCN population is sensitive towards ΔS in both the low- and
high-S regime. Accordingly, the CCN population can be regarded as both
aerosol-sensitive due to the low total aerosol concentrations and
updraft-sensitive according to the CCN efficiency spectrum with its sensitivity to
ΔS across a wide S range.
During long-range-transport (LRT) episodes within the wet season, major amounts
of aged North African dust, West African biomass burning smoke, and Atlantic
marine aerosols are advected on an event basis into the basin (mostly
February–April). Total aerosol concentrations (∼ 440 cm-3) are
slightly enhanced relative to the PR state, and the aerosol population has a
bimodal shape with a minor Aitken and a stronger accumulation mode
(DAit ≈ 80 nm, NAit ≈ 120 cm-3 vs.
Dacc ≈ 180 nm, Nacc ≈ 300 cm-3). Besides the
non-refractory fraction of organics and inorganics (OA: 1.81 µg m-3; NO3-: 0.08; NH4+:
0.30; SO42-: 0.25; Cl-:
0.04; BCe: 0.21 µg m-3), a larger
refractory fraction of mineral dust and sea salts can be found in internally
mixed particles. The observed κ(S,Da) levels are increased
compared to the PR state in agreement with the chemical composition (κAit = 0.18 ± 0.02 vs.
κacc = 0.35 ± 0.04). The CCN efficiency spectrum shows a steep increase – and thus high
sensitivity to ΔS – at low S and quickly converges against full
activation towards high S (50 % activation at ∼ 0.09 %,
90 % activation at ∼ 0.53 %). Thus, the CCN regime under
LRT influence is at the border of the aerosol-limited and transitional regimes.
Biomass burning (BB) is the predominant anthropogenic influence during the
Amazonian dry season, which alters the atmospheric composition
substantially. During the BB event analyzed here, we found strongly enhanced
aerosol concentrations (∼ 3600 cm-3) and a size
distribution dominated by the accumulation mode (DAit ≈ 70 nm,
NAit ≈ 140 cm-3 vs. Dacc ≈ 170 nm,
Nacc ≈ 3400 cm-3). The aged smoke particles comprise
mostly organic matter (OA: 21.1; NO3-: 0.55; NH4+:
0.58; SO42-: 0.82; Cl-: 0.03; BCe: 0.89 µg m-3). The observed κ(S,Da) levels
were comparatively low and weakly size-dependent (κAit = 0.14 ± 0.01 vs. κacc = 0.17 ± 0.02).
The corresponding CCN efficiency spectrum shows a rather steep
increase at low S and converges against a threshold (∼ 93 %)
below full activation at high S (50 % activation at S ≈ 0.15 %). With respect to the large particle concentrations and the large
sensitivity of the CCN efficiency spectrum to ΔS in the low-S regime,
the BB case falls into the updraft-limited regime.
During the dry season, the central Amazon often experiences mixed aerosol
populations from regional, continental, and even transcontinental sources.
Here, we analyzed one characteristic example of such mixed-pollution
(MPOL) scenarios, in which a mixture of African volcanogenic emissions and
nearby Amazonian fires impacted the ATTO site: under conditions with a
predominant influence of the African volcanogenic emissions, we found a
broad monomodal size distribution (Dmode ≈ 130 nm,
Nmode ≈ 1300 cm-3), strongly enhanced sulfate levels (OA:
5.50; SO42-: 1.75 µg m-3), and
correspondingly elevated hygroscopicities (κ<100nm = 0.14 ± 0.01 vs.
κ>100nm = 0.22 ± 0.03). Under conditions with predominant
influence by the nearby fires, we found high concentrations in a monomodal
distribution (Dmode ≈ 110 nm, Nmode ≈ 2800 cm-3), an enhancement of organic matter on top of the sulfate
background (OA: 7.88; SO42-: 2.03 µg m-3), and low hygroscopicities
(κ<150nm = 0.10 ± 0.01 vs. κ>150nm = 0.20 ± 0.04). Accordingly, the
interplay of the aged volcanogenic plume and the fresh smoke resulted in
large variations of the total aerosol concentration, aerosol composition,
and CCN properties. We suppose that the highly variable CCN population
results in associated (microphysical) variations in cloud properties.
Hygroscopicity distributions were analyzed for all conditions, providing
detailed and characteristic insights into the mixing state of the different
types of aerosols. We found that the spread of κ increases with size
for all conditions. The κ distributions suggest that the PR aerosol
population is rather internally mixed, whereas the BB, LRT, and MPOL aerosols show
more external mixing states. In essence, the κ distributions and
NCN κ distributions are valuable overview representations,
combining characteristic aerosol properties in terms of particle size,
particle concentration, κ levels, and aerosol mixing state in a
fingerprint-like manner. This representation helps to further elucidate
aerosol–cloud interactions, such as the shapes of the CCN efficiency
spectra: as general tendencies, more externally mixed aerosols, resulting in
broader κ distributions, also broaden the CCN efficiency spectra –
analogous to broad NCN distributions. Internally mixed aerosols with
more defined κ distributions tend towards steeper segments in the
CCN efficiency spectra. Furthermore, externally mixed aerosols with
distinctly different κ levels tend to introduce further
steps/plateaus into the CCN efficiency spectra, in addition to plateaus
caused by multimodal NCN distributions. However, the CCN efficiency
spectra for the conditions reported here are primarily shaped by the
particle size distributions and average κ levels, whereas the
diversity of κ seems to play a minor role. To clarify exactly how
the signatures and patterns in κ and NCN κ distributions
are related to the signatures and shapes of the CCN efficiency spectra,
dedicated future studies at contrasting locations and modeling support are
required.
Finally, the CCN efficiency spectra and CCN spectra for all analyzed cases
are discussed in an overview, which emphasizes the following observations:
(i) the combination of CCN efficiency spectra for all analyzed conditions
show a large variability. (ii) Within this range, the estimated peak
supersaturations at cloud base, Scloud(DH,κ), are collocated
with the intermediate S range, in which the CCN efficiency spectra show
their highest variability, thus underlining the impact of changing
aerosol populations on cloud properties. (iii) The sensitivity of the CCN
populations to changes in S is conditions-specific with highest
susceptibilities towards ΔS between S = 0.1 and S = 1 %.
(iv) The combination of CCN efficiency spectra for different, particularly
contrasting, aerosol populations provides a basis for follow-up studies on
the aerosol-related differences in cloud properties in the Amazon region and
beyond. (v) Finally, the atmospheric aging of aerosol particles in the
atmosphere is reflected in a corresponding evolution of the shape of the CCN
efficiency spectra and has to be kept in mind upon their utilization.