Introduction
Atmospheric aerosol particles are known to modify the microphysical
properties of clouds, such as their albedo, lifetime and precipitation
patterns (Boucher et al., 2013). Due to the importance of clouds in the
weather and climate systems, these aerosol-induced changes, known as the
indirect effects of aerosol on climate, are a subject of rigorous research.
The quantification of the radiative forcing associated with the interactions
of atmospheric aerosol with clouds remains one of the biggest challenges in
the current understanding of climate change (Boucher et al., 2013). These
challenges are associated with the production of the aerosol particles that
are able to activate into cloud droplets, known as cloud condensation nuclei
(CCN) (e.g. Laaksonen et al., 2005; Andreae and Rosenfeld, 2008; Kuang et
al., 2009; Kerminen et al., 2012), their actual activation into cloud drops
(e.g. Kulmala et al., 1996; Dusek et al., 2006; McFiggans et al., 2006;
Paramonov et al., 2013; Hammer et al., 2014), the formation of clouds (e.g. Twomey, 1959; Mason and Chien, 1962; Vaillancourt et al., 2002), time
evolution of cloud microphysical and other properties (e.g. Rosenfeld et
al., 2014) and the interaction of clouds with the solar and terrestrial
radiation (e.g. Boucher and Lohmann, 1995; Ramanathan et al., 2001; Chen et
al., 2014). A better understanding is needed with respect to each of these
steps in order to improve the performance of the current global climate
models (GCMs) and to increase the accuracy of the future climate
predictions.
Several aerosol properties are of special interest when looking at the
interaction of atmospheric aerosol particles with warm clouds. The current
article focuses on the number, size and hygroscopicity of the atmospheric
aerosol particles with regard to how these parameters affect the potential
of particles to act as CCN. One such property of interest is the CCN number
concentration NCCN. Depending on the location, NCCN can vary by
several orders of magnitude, and it directly depends on the aerosol
properties and the ambient water vapour supersaturation ratio S in the
atmosphere. Köhler theory dictates that the minimum size at which
particles activate into cloud drops decreases with increasing S (Köhler,
1936); consequently NCCN increases monotonically with S for a given
aerosol population. The exact response of NCCN to an increasing S depends
on the total aerosol number concentration NCN, aerosol size distribution
and particle hygroscopicity. Besides the relevant references found
throughout the paper, discussion about NCCN concentrations in various
environments can be found in, e.g. Pandis et al. (1994), Covert et al. (1998),
Snider and Brenguier (2000), Chang et al. (2007), Andreae and
Rosenfeld (2008), Andreae (2009) and Wang et al. (2010). At any given S,
another property of interest is the critical dry diameter of CCN activation
Dc, defined as the smallest diameter at which particles activate into
cloud drops. For internally mixed polydisperse aerosol particles, this
diameter indicates that in the presence of a sufficient amount of water
vapour all particles above this size activate into cloud drops, and all
particles below this size do not. However, atmospheric aerosol is frequently
externally mixed, with particles of different sizes exhibiting different
chemical composition, and, therefore, in practice, Dc is usually
estimated as the diameter at which 50 % of the particles activate and grow
into cloud drops at any given S. Dc can be directly calculated from
size-segregated cloud condensation nuclei counter (CCNC) measurements (Rose
et al., 2008) or estimated from the size distribution data coupled with
NCCN (Hitzenberger et al., 2003; Furutani et al., 2008). The effect of
hygroscopicity on the activation of CCN into cloud drops has also been
studied extensively, and several simplified theoretical models have been
suggested to link particle composition with critical supersaturation
Sc, i.e. the minimum S required for the particles of a certain size to
activate into cloud drops (e.g. Svenningsson et al., 1992; Rissler et al.,
2005; Khvorostyanov and Curry, 2007; Wex et al., 2007). One such approach is
the hygroscopicity parameter κ, also known as “kappa”, a unitless
number describing the cloud condensation nucleus activity (Petters and
Kreidenweis, 2007). The value of κ typically varies between zero and
just above unity, with values close to zero indicating a non-hygroscopic
aerosol, i.e. with low affinity for water (e.g. freshly emitted black
carbon; e.g. Hudson et al., 1991; Weingartner et al., 1997; Wittbom et al.,
2014) and values close to unity indicating an aerosol with high
hygroscopicity, i.e. high affinity for water (e.g. sea salt particles;
e.g. Good et al., 2010). Since its introduction, the parameter κ has been
used in CCN studies quite extensively (e.g. Carrico et al., 2008; Kammermann
et al., 2010a; Levin et al., 2014).
This article summarises the measurements performed by CCNCs within the
framework of the European Integrated project on Aerosol Cloud Climate and
Air Quality interactions (EUCAARI). One of the EUCAARI project aims was to
compile a comprehensive database of in situ measured aerosol, CCN and
hygroscopic properties in order to increase the knowledge about
aerosol–cloud–climate interactions and to combine the relevant existing
measurement infrastructure (Kulmala et al., 2011). Besides CCNCs already
deployed at the existing European long-term measurement stations, several
intensive field campaigns using the CCNC were carried out as part of EUCAARI
as well. The main objective of this work is to present a comprehensive
overview and intercomparison of CCNC measurements and to provide an insight
into the cloud droplet activation and aerosol hygroscopic properties in
different environments. More specifically, the aims are to (i) get new
insight into CCN number concentrations and activated fractions around the
world and their dependence on the water vapour supersaturation ratio, (ii) provide
new information about the dependence of aerosol hygroscopicity on
particle size, and (iii) reveal seasonal and diurnal variation of CCN
activation and hygroscopic properties. While undeniably important, the
effect of size distribution on NCCN and the size-resolved activated
fraction (e.g. Dusek et al., 2006; Quinn et al., 2008; Morales Betancourt
and Nenes, 2014) is not investigated herein, and an overview of the existing
EUCAARI aerosol size distribution data can be found in Asmi et al. (2011)
and Beddows et al. (2014).
Methodology
Instrumentation
A CCNC is a type of instrument frequently used for studying the cloud
droplet activation potential of aerosol particles. In its simplest set-up, a
CCNC consists of a saturator unit and an optical particle counter (OPC)
frequently running in parallel with a condensation particle counter (CPC).
For all measurements presented herein, the CCNC used was a commercially
available instrument produced by Droplet Measurement Technologies, Inc. (DMT-CCNC), the basic principles of operation of which are described below.
Upon entering the measurement set-up, the aerosol flow is split into two
sample flows, with the first flow leading to a CPC to determine the total
particle number concentration, hereafter referred to as NCN. The second
flow feeds the aerosol into the saturator unit of the CCNC, inside of which
the conditions of supersaturation Seff with respect to water vapour down
the centre of the column are established. Aerosol, flowing under laminar
flow conditions, is subjected to these supersaturation conditions, during
which particles with a critical supersaturation Sc smaller than
Seff will grow by the condensation of water vapour and remain in stable
equilibrium, i.e. activate as CCN. The residence time inside the saturator
column (∼10 s) allows for the activated particles to grow to
sizes larger than 1 µm in diameter; these particles are then counted by
the OPC providing the number concentration of activated aerosol particles, a
quantity hereafter referred to as NCCN. The described set-up is
characteristic of polydisperse measurements; an inclusion of a drier, a
neutraliser and a differential mobility analyzer (DMA; Knutson and Whitby,
1975) prior to the splitting of the flow into two parallel lines allows for
the selection of a particular particle size, i.e. quasi-monodisperse
measurements. Such measurements can be performed either by varying the
particle size at a constant Seff (D-scan) or by varying Seff at a
constant particle size (S-scan). Such a set-up, while more complex, provides
activation spectra and allows for a direct calculation of the critical dry
diameter of droplet activation Dc (in case of the D-scan) or the critical
supersaturation Sc (in case of the S-scan). Typically, a CCNC operates at
several different levels of Seff, most commonly ranging between 0.1 and
1.0 %; the deviations from the nominal assigned Seff values can be
monitored and corrected by applying a standardised calibration procedure, as
described in Sect. 2.3. A more detailed description of the general
operating procedures of the CCNC can be found in Roberts and Nenes (2005);
exact details of the measurement set-up at each of the locations described in
the next section can be found in the respective published literature
referenced throughout the text.
A world map showing the locations of CCNC measurements performed
during EUCAARI and presented in this study.
Names, locations and descriptions of all measurement sites presented
in the analysis.
Name of station or campaign
Location
Geographic coordinates
Elevation
Site description
(m a.m.s.l.)
Hyytiälä
southern Finland
61∘51′ N, 24∘17′ E
181
rural background
Vavihill
southern Sweden
56∘01′ N, 13∘09′ E
172
rural background
Jungfraujoch/CLACE-6
Swiss Alps
46∘33′ N, 07∘59′ E
3580
free troposphere
Mace Head
west coast of Ireland
53∘19′ N, 09∘54′ W
0
coastal background
Pallas
northern Finland
67∘58′ N, 24∘07′ E
560
remote background
Finokalia
northern Crete
35∘20′ N, 25∘40′ E
250
remote coastal
Cabauw
central Netherlands
51∘58′ N, 04∘56′ E
-1
rural background
K-puszta
central Hungary
46∘58′ N, 19∘33′ E
125
rural
Chilbolton
southern United Kingdom
51∘09′ N, 01∘26′ W
78
continental background
COPS
south-west Germany
48∘36′ N, 08∘12′ E
1156
continental background
RHaMBLe
tropical North Atlantic
∼21∘ N, 20∘ W
0
remote marine
PRIDE-PRD2006
southeastern China
23∘33′ N, 113∘04′ E
28
rural background
AMAZE-08
northern Brazil
02∘36′ S, 60∘13′ W
108
remote background
CAREBeijing-2006
northern China
39∘31′ N, 116∘18′ E
30
suburban
Measurement sites
Data from a total of 14 EUCAARI locations have been provided for this
analysis, including both long-term measurement stations and short-term
campaigns (Fig. 1). As seen in the figure, data sets came from a wide
variety of locations representing various environments, including marine and
continental, urban and background, at altitudes ranging from the ground
level to the free troposphere. The location and description of each
measurement site are given in Table 1. All measurements presented herein were
performed within the EUCAARI framework.
Hyytiälä Forestry Field Station in southern Finland is the location
of the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR II),
operated by the University of Helsinki. Located on a flat terrain and
surrounded by the boreal coniferous forest, mainly Scots pine, the station
is well representative of the boreal environment (Hari and Kulmala, 2005).
It is a rural background site, with the nearest city of Tampere (population 220 000) located 50 km
to the south-west. Air masses at the site can be of both
Arctic and European origin; however, aerosol particle number concentrations
at this site are typically low (Sogacheva et al., 2005).
Vavihill in southern Sweden is a continental background site surrounded by
grasslands and deciduous forest and operated by Lund University. The site is
located 60–70 km NNE of the Malmö and Copenhagen urban area
(population ∼2 000 000); however, it is considered to not be affected by
the local anthropogenic sources (Tunved et al., 2003). Due to its location,
the site is often used for monitoring the transport of pollution from
continental Europe into the Nordic region (Tunved et al., 2003).
The Jungfraujoch is a high Alpine station in the Bernese Alps in
Switzerland, where the aerosol measurements are performed by the Paul
Scherrer Institute (PSI). Being located high in the mountains (3580 m a.s.l.),
the station is far from local sources of pollution and is, in fact,
in the free troposphere most of the time; hence, it is considered a
continental background site and aerosol concentrations are very low (Collaud
Coen et al., 2011). However, particularly during the summer months, the
Jungfraujoch site is frequently influenced by the injections of more
polluted air from the planetary boundary layer, driven by thermal convection
(Jurányi et al., 2010,
2011; Kammermann et al., 2010a). The station is frequently inside clouds allowing for direct
measurements of aerosol–cloud interactions.
Mace Head is a coastal marine site located on the west coast of Ireland and
operated by the National University of Ireland, Galway. The distance to the
nearest urban settlement of Galway City (88 km, population 65 000) renders Mace
Head a clean background site; being on the coast, the station is directly
exposed to the North Atlantic Ocean. Occasionally the station is subject to
more polluted air masses originating from continental Europe and the United
Kingdom (O'Dowd et al., 2014).
Pallas is a remote continental site in northern Finland located in the
northernmost boreal forest zone in Europe; it is run by the Finnish
Meteorological Institute (FMI). The station is situated on top of a treeless
hill and, due to the frequent presence of clouds, is suitable for in situ
measurements of aerosol–cloud interactions. The Pallas station is subject to
both clean Arctic air masses, as well as to more polluted European air
masses; regardless, absolute particle number concentrations are typically
low (Hatakka et al., 2003).
Finokalia station is a remote coastal site located on the island of Crete
and operated by the University of Crete. The station is located on top of a
hill, and most frequently air masses arrive in Finokalia over the
Mediterranean Sea (Stock et al., 2011). The station is representative of
background conditions as there are no local sources of pollution present;
the largest nearby urban centre of Heraklion (population 175 000) is 50 km to the
west.
The Cabauw Experimental Site for Atmospheric Research (CESAR) is located in
the central Netherlands, 44 km from the North Sea. The station is in a rural
area; however, the big cities of Utrecht and Rotterdam are nearby; the
station is subject to both continental and maritime conditions (Mensah et
al., 2012). The station is operated by the Royal Netherlands Meteorological
Institute (KNMI).
Periods of available data for all locations and campaigns.
Summary of available data for each measurement location.
NCCN is the CCN number concentration, NCN is the total number
concentration, A is the activated fraction, Dc is the critical dry
diameter and κ is the hygroscopicity parameter. The “set-up” column
indicates whether the CCNC was operating in polydisperse or monodisperse
mode. Dc_calc and κ_calc have
been calculated from polydisperse data using the differential/scanning
mobility particle sizer (DMPS/SMPS) data.
Name of station or campaign
Set-up
Parameters
Seff levels
Time resolution
Reference
Hyytiälä
poly & mono
NCN, NCCN, A, Dc, κ
0.0859, 0.1, 0.2, 0.216, 0.3, 0.4, 0.478, 0.5, 0.6, 0.74, 1.0, 1.26 %
original
Paramonov et al. (2013)
Vavihill
poly
NCCN, NCN, A, Dc_calc, κ_calc
0.1, 0.2, 0.4, 0.7, 1.0 %
original
Fors et al. (2011)
Jungfraujoch
poly
NCCN, NCN, A, Dc_calc, κ_calc
0.12, 0.24, 0.35, 0.47, 0.59, 0.71, 0.83, 0.95, 1.07, 1.18 %
original
Jurányi et al. (2010, 2011)
Mace Head
poly
NCN, NCCN, A
0.25, 0.5, 0.75 %
averaged
Ovadnevaite et al. (2011)
Pallas A
poly
NCCN, NCN, A, Dc_calc, κ_calc
0.2, 0.4, 0.6, 0.8, 1.0 %
original
Jaatinen et al. (2014)
Pallas B
poly & mono
NCN, NCCN, A, Dc, κ
0.47, 0.72, 0.97, 1.22 %
averaged (poly), original (mono)
n/a
Pallas C
poly & mono
NCN, NCCN, A, Dc, κ
0.1, 0.15, 0.2, 0.6, 1.0 %
averaged (poly), original (mono)
Brus et al. (2013)
Finokalia A
mono
NCN, NCCN, Dc
0.21, 0.38, 0.52, 0.66, 0.73 %
averaged
Bougiatioti et al. (2009)
Finokalia B
poly
NCCN, A, Dc_calc
0.21, 0.38, 0.52, 0.66, 0.73 %
averaged
Bougiatioti et al. (2009)
Cabauw
poly
NCCN
varies between 0.1 and 1.0 %
original
Bègue (2012)
K-puszta
mono
NCCN, A, κ
0.03, 0.04, 0.10, 0.17, 0.20, 0.25, 0.44, 0.62, 0.67 %
averaged
n/a
Chilbolton
mono
NCCN, A, Dc, κ
0.11, 0.30, 0.56, 0.94 %
averaged
Whitehead et al. (2014)
COPS
poly & mono
NCCN, A, Dc, κ
0.11, 0.17, 0.24, 0.28, 0.32, 0.35, 0.43, 0.50, 0.65, 0.80 %
averaged
Irwin et al. (2010), Jones et al. (2011), Whitehead et al. (2014)
RHaMBLe
poly & mono
NCCN, A, Dc, κ
0.09, 0.16, 0.29, 0.47, 0.74 %
averaged
Good et al. (2010), Whitehead et al. (2014)
PRIDE-PRD2006
mono
NCN, NCCN, A, Dc, κ
0.068, 0.27, 0.47, 0.67, 0.87, 1.27 %
original
Rose et al. (2010, 2011)
AMAZE-08
mono
NCN, NCCN, A, Dc, κ
0.095, 0.19, 0.28, 0.46, 0.82 %
original
Gunthe et al. (2009)
CAREBeijing-2006
mono
NCN, NCCN, A, Dc, κ
0.066, 0.26, 0.46, 0.66, 0.86 %
original
Gunthe et al. (2011)
CLACE-6
mono
NCN, NCCN, A, Dc, κ
0.079, 0.17, 0.27, 0.46, 0.66 %
original
Rose et al. (2013)
Average NCCN concentrations (cm-3) at all studied
locations. All NCCN concentrations were recalculated to correspond to
the Seff levels suggested by the ACTRIS network: 0.1, 0.2, 0.3, 0.5 and
1.0 %. The four long-term data sets are shown at the top of the table.
Name of station or campaign
Seff=0.1 %
Seff=0.2 %
Seff=0.3 %
Seff=0.5 %
Seff=1.0 %
Vavihill
362
745
952
1285
1795
Hyytiälä
274
407
526
824
1128
Mace Head
472
526
581
691
1007
Jungfraujoch
135
249
341
444
599
PRIDE-PRD2006
1888
4594
6956
9760
13 855
CAREBeijing-2006
2547
4751
6510
8460
10 711
Cabauw
435
1607
2208
3235
6439
Finokalia B
903
1167
1431
1793
2354
Finokalia A
946
1257
1567
1882
2109
COPS
3
210
364
710
–
RHaMBLe
300
535
717
922
1153
K-puszta
146
349
512
727
834
Chilbolton
145
210
274
384
506
CLACE-6
66
126
156
205
303
Pallas B
–
–
149
176
247
AMAZE-08
37
85
112
136
205
Pallas C
14
38
50
74
141
Pallas A
7
19
31
50
98
The University of Manchester conducted four short-term measurement campaigns
utilising a CCNC: K-puszta, Chilbolton, COPS and RHaMBLe. K-puszta is a
rural site surrounded by deciduous–coniferous forest located on the Great
Hungarian Plain in central Hungary 80 km SE of Budapest. The site has no
local anthropogenic pollution sources (Ion et al., 2005). Chilbolton is also
a rural site, located in southern United Kingdom, 100 km WSW of London. The
site is most frequently influenced by the marine air masses; a potential
local source of anthropogenic pollution is the seasonal agricultural
spraying (Campanelli et al., 2012). The Convective and
Orographically-induced Precipitation Study (COPS) campaign took place at the
top of the Hornisgrinde Mountain in the Black Forest region of south-west
Germany. While this site is primarily surrounded by the coniferous forest,
the close proximity to the Rhine Valley exposes the site to some
anthropogenic pollution. Due to its elevation, the site is occasionally in
the free troposphere (Jones et al., 2011). The Reactive Halogens in the
Marine Boundary Layer (RHaMBLe) Discovery Cruise D319 campaign was a cruise
conducted in the tropical North Atlantic between Portugal and Cabo Verde.
The operational area can be described as a remote marine environment with
few, if any, sources of anthropogenic pollution. Air masses can originate
from both the ocean and from the African mainland (Good et al., 2010).
The Max Planck Institute for Chemistry (MPIC) also conducted four CCNC
measurement campaigns within the EUCAARI framework: PRIDE-PRD2006, AMAZE-08,
CAREBeijing-2006 and CLACE-6, with the last one having taken place at the
previously described Jungfraujoch station. The PRIDE-PRD2006 campaign took
place in southeastern China, in a small village ∼60 km NW of
Guangzhou, in the vicinity of a densely populated urban centre. The wind
direction during the campaign rendered the site a rural receptor of the
regional pollution originating from the Guangzhou urban cluster (Rose et
al., 2010). The AMAZE-08 campaign took place at a remote site in an
Amazonian rainforest, 60 km NNW of Manaus, Brazil. During the campaign, the site
experienced air masses characteristic of clean tropical rainforest
conditions as well as air masses influenced by long-range transport of
pollution (Gunthe et al., 2009; Martin et al., 2010). The CAREBeijing-2006
campaign was conducted at a suburban site in northern China, on the grounds
of Huang Pu University in Yufa, ∼50 km south of Beijing. The
site is subject to air masses originating both in the south and in the
north; however, being located on the outskirts of a large urban centre,
particle concentrations are generally high (Garland et al., 2009).
Data
The measurement period for each location and a brief summary of available
CCNC data are presented in Fig. 2 and Table 2, respectively. Available
data range from mid-2006 to the end of 2012; the four long-term data sets all
exceed one year in duration. As originally requested by the authors from the
EUCAARI partners, some of the data were submitted in the NASA-Ames format
with daily and monthly/campaign averages. Other data sets were submitted in
the original time resolution and have been compiled accordingly for this
overview study.
For the quality assurance of the CCNC data, data providers were requested to
recalculate all values to correspond to the standard temperature and
pressure and to utilise a consistent procedure for the CCNC calibration.
Calibrations were asked to be performed as outlined in Rose et al. (2008)
using nebulised, dried, charge-equilibrated and size-selected ammonium
sulphate or sodium chloride aerosol particles. To predict Seff for
instrument calibration, water activity was asked to be parameterised
according to either the AIM-based model (Rose et al., 2008) or the
ADDEM model (Topping, 2005); both of these models can be considered
as accurate sources of water activity data, and the discussion about their
associated uncertainties can be found in the corresponding references. As
none of the participating data providers noted a deviation from the
calibration procedure, it is assumed that the data were treated accordingly.
However, deviations from the described procedure and from the target
Seff levels may be possible and can potentially affect some of the
conclusions presented in this paper. Uncertainties associated with
deviations from the mentioned calibration procedure and parameterisation are
discussed in great detail in Rose et al. (2008) and Topping (2005).
Average cumulative NCCN for all available locations shown as a
percentage of the NCCN measured at the Seff of 1.0 % (above each
bar). Colours indicate the supersaturation Seff bins.
For some of the polydisperse data sets, where available,
differential/scanning mobility particle sizer (DMPS/SMPS; Wang and Flagan,
1989; Wiedensohler et al., 2012) data were used in conjunction with the CCNC
to derive the critical dry diameter Dc. The procedure was carried out by
comparing NCCN to the DMPS/SMPS-derived number size distributions; these
were integrated from the largest size bin until the cumulative NCN
concentration was equal to NCCN. Dc was then calculated by
interpolating between the two adjacent size bins (Furutani et al., 2008).
Following the calculation of Dc, the hygroscopicity parameter κ
was determined using the effective hygroscopicity parameter (EH1) Köhler
model (Eq. 1) assuming the surface tension of pure water (Petters and
Kreidenweis, 2007; Rose et al., 2008). Due to the surface tension of actual
cloud droplets being lower than that of pure water droplets (Facchini et
al., 2000), this assumption, although commonly used, typically leads to an
overestimation of the NCCN (Kammermann et al., 2010b).
S=Dwet3-Ds3Dwet3-Ds3(1-κ)exp4σsolMwRTρwDwet,
where S is water vapour saturation ratio, Dwet is the droplet
diameter, Ds is the dry particle diameter, which, as per Rose et al. (2008),
can be substituted with Dc, κ is the hygroscopicity
parameter, σsol is the surface tension of condensing solution
(assumed to be pure water), Mw is the molar mass of water, R is the
universal gas constant, T is the absolute temperature and ρw is
the density of pure water.
For certain sites, total number concentrations of particles larger than 50 or 100 nm in diameter (N50 or N100) were calculated from the
corresponding DMPS or SMPS data.
In order to compare the results from different stations, several
interpolation/extrapolation techniques were used. All NCCN
concentrations were averaged for each site for each Seff level and then
recalculated to correspond to the target Seff levels suggested by the
Aerosols, Clouds and Trace gases Research InfraStructure (ACTRIS) Network:
0.1, 0.2, 0.3, 0.5 and 1.0 %. Recalculation to the nearest target
supersaturation was accomplished by a simple linear
interpolation/extrapolation of NCCN as a function of Seff using the
two adjacent/nearest Seff points. For the Jungfraujoch data, Dc at
Seff of 0.12 and 0.95 % was recalculated to the corresponding
Dc at the target Seff of 0.1 and 1.0 %, respectively, assuming
a size-independent κ.
Results and discussion
CCN concentrations
Table 3 presents CCN number concentrations NCCN at all 18 measurements
locations and campaigns for five Seff levels mentioned in the previous
section. First and foremost, since CCN are simply a fraction of the total
aerosol population with their concentration depending on Seff,
NCCN values at Seff of 1.0 % follow a similar pattern known from
total particle number concentrations. The lowest NCCN values, thus,
originate in remote and clean locations, such as Pallas, the Amazonian
rainforest (AMAZE-08), Jungfraujoch and Chilbolton. The highest NCCN
values are found in more polluted locations – CAREBeijing-2006 and
PRIDE-PRD2006, both in China. At lower Seff levels, other effects, such
as those of size distribution and hygroscopicity, become more pronounced.
When examining NCCN at Seff of 0.1 %, the highest values are still
found in China; similar to NCCN at Seff of 1.0 %, the lowest
values are found in Pallas, the Amazonian rainforest (AMAZE-08),
Jungfraujoch and also in south-west Germany (COPS).
Average activated fraction A as a function of supersaturation
Seff for all available data sets. Symbols represent arithmetic mean
values of A calculated from all available data for each station for each
Seff level. Lines represent the linear fits in the form A=a×ln(Seff)+b. Also shown is the overall fit based on most of the data
points (*Finokalia, COPS, Jungfraujoch and Pallas A, B and C data sets
excluded). The shading of the overall fit represents the prediction bounds
of the fit with a confidence level of 95 %. Slope, intercept and
correlation coefficient values of the linear fits can be found in Table 4.
In order to examine the CCN activation spectra in more detail, Fig. 3
presents cumulative NCCN concentrations shown as a percentage of the
NCCN measured at the highest Seff of 1.0 %. One group of locations
that can be pointed out in the figure is representative of the marine
environment: Finokalia, Mace Head and the RHaMBLe campaign. At these marine
locations the presence of large and hygroscopic sea salt particles is
expected, and a large fraction of particles already activates at the lowest
Seff, i.e. of the total NCCN measured at the highest Seff, about
a third activates already at the lowest Seff. In the case of Mace Head,
the observed behaviour is due to the presence of sea salt particles and a
peculiar organic composition of the marine aerosol (Ovadnevaite et al.,
2011). Additionally, both Finokalia and Mace Head have a large fraction of
the long-range transported and aged aerosol (Bougiatioti et al., 2009;
Ovadnevaite et al., 2011), which has been shown to increase particle
hygroscopicity (Perry et al., 2004; Furutani et al., 2008). Chilbolton,
being a continental background site representative of the regional aerosol
properties, also belongs to this group; however, the NCCN concentrations
at this location may be underestimated due to the aerosol not being dried
prior to entering the CCNC (Whitehead et al., 2014).
Another group of locations with a different CCN activation pattern is
represented by Pallas and Cabauw – at these locations very few particles
activate at the lowest Seff, and the NCCN increases drastically when
Seff changes from 0.5 to 1.0 %. This may indicate that the aerosol
is dominated by the Aitken mode particles and, to a lesser extent, that the
aerosol may be of low hygroscopicity. A high concentration of Aitken mode
particles in the autumn and low aerosol hygroscopicity in Pallas have been
previously reported by Tunved et al. (2003) and Komppula et al. (2006),
respectively. The two measurement locations discussed here are interesting
with regard to the ratio of presumed cloud droplet number concentration
(CDNC) to the total aerosol particle number concentration. It has been
reported that, although under the clean and convective conditions ambient
Sc may reach as high as 1.0 %, in the polluted boundary layer
Sc usually remains below 0.3 % (Ditas et al., 2012; Hammer et al.,
2014; Hudson and Noble, 2014). If one assumes this value, a comparatively
small fraction of aerosol in northern Finland and central Netherlands would
potentially activate into cloud droplets if exposed to this Sc. This has
direct implications for the cloud formation and, thus, local climate at
these locations.
Activated fraction
Another variable describing CCN activation properties of an aerosol
population that was examined for the majority of locations is the activated
fraction A calculated as a ratio of NCCN to NCN (Fig. 4). Each
activation curve in Fig. 4 is based on the arithmetic mean values of A
calculated from all available data for each station for each Seff level.
Included in the figure is the overall fit shown with prediction bounds
(95 % confidence level) based on most of the activation curves, except the
outlying ones of Finokalia, COPS, Jungfraujoch and Pallas A, B and C. As can
be seen in the figure from the similar shape and placement of the activation
curves and in Table 4 from the similar slope and intercept values, for
many locations there is no discernible difference in how A responds to
changing Seff on an annual basis; this is further signified by the
prediction bounds of the overall fit. Therefore, the average total number
concentration NCN alone is sufficient in order to roughly estimate the
annual mean NCCN at any given Seff, for example, using the overall
fit parameters presented in Table 4. The appropriateness of the overall fit
for estimating NCCN based on NCN alone was investigated for the
whole Hyytiälä data set, by comparing the NCCN measured by the
CCNC with the NCCN calculated using the NCN and the overall fit
presented in Table 4. Such a comparison revealed that for Hyytiälä
the overall fit leads to an annual median overestimation of NCCN of 49,
41, 33, 17 and 2 % for the Seff levels of 0.1, 0.2, 0.3, 0.5 and
1.0 %, respectively.
Parameters of the linear fit A=a×ln(Seff)+b, for all
locations depicted in Fig. 4. a is the slope, b is the intercept and r is the
correlation coefficient of the simple linear regression. The overall linear
fit is based on most of the activation curves depicted in Fig. 4, except
Finokalia, COPS, Jungfraujoch and Pallas A, B and C.
Name of station or campaign
a
b
r
Hyytiälä
0.21
0.62
0.99
Vavihill
0.21
0.64
1.00
Jungfraujoch
0.17
0.48
1.00
Mace Head
0.23
0.79
0.98
Finokalia
0.29
0.86
0.99
Pallas A
0.08
0.19
0.99
Pallas B
0.15
0.49
0.98
Pallas C
0.13
0.35
0.98
COPS
0.31
0.92
0.97
RHaMBLe
0.21
0.70
1.00
Pride-PRD2006
0.26
0.74
0.99
AMAZE-08
0.23
0.70
0.99
CARE-Beijing2006
0.22
0.74
1.00
CLACE-6
0.22
0.69
1.00
Overall
0.22
0.69
0.96
For Seff levels below 0.3 %, the variability of the overall fit, as
shown by the prediction bounds, leads to the uncertainty of the predicted
NCCN of up to an average of ∼45 %. This uncertainty
decreases exponentially for Seff levels above 0.3 %. A global
modelling study conducted by Moore et al. (2013) reported that CDNC over the
continental regions is fairly insensitive to NCCN, where a 4–71 %
uncertainty in NCCN leads to a 1–23 % uncertainty in CDNC. Since the
overwhelming majority of measurements analysed in this paper were conducted
on land, and the overall fit results in an uncertainty of the predicted
annual mean NCCN of up to ∼45 %, for many sites the use
of the overall fit would yield a deviation of the predicted average CDNC of
approximately less than 10 %. CDNC, however, is more sensitive to
NCCN in cleaner regions with low total particle number concentrations,
such as the Alaskan Arctic and remote oceans (Moore et al., 2013). In such
areas the use of the overall fit may not be appropriate.
Four locations stand out in Fig. 4, which were not included in the overall
fit. A is visibly higher in Finokalia and during the COPS campaign than in
other locations, with approximately 60 % of the total aerosol population
at both locations activating into cloud drops at the Seff of
∼0.4 %. Reasons for the observed behaviour in Finokalia
were discussed in the preceding Sect. 3.1. During the COPS campaign the
size distributions varied greatly, and, as will be shown later, Aitken mode
aerosol was more hygroscopic than accumulation mode aerosol, possibly
explaining the behaviour of the COPS activation curve seen in Fig. 4 at
least for higher Seff levels (Irwin et al., 2010; Jones et al., 2011).
Another location with seemingly different activation curves is Pallas, where
the activation spectrum changes throughout the year, and even at fairly high
Seff level of 1.0 %, less than half of the total aerosol population
activated into cloud drops. The long-term Jungfraujoch data set also
exhibited comparatively low A values, lower than those presented by
Jurányi et al. (2011) and those during the CLACE-6 campaign at the same
location (Fig. 4). While the A values in the long-term Jungfraujoch data set
were calculated with respect to CPC measurements of total particle number
concentration, A values for the CLACE-6 campaign and those reported by
Jurányi et al. (2011) were calculated with respect to integrated SMPS
size distribution measurements with a higher size cut-off. While the aerosol
hygroscopicity at these locations will be discussed later, the effect of the
size distribution on the activation curves is evident.
The similarity in how A responds to Seff at the majority of studied
locations is an interesting result. In other words, at any given Seff
the annual mean fraction of aerosol that will activate into cloud drops is
pretty much the same in many locations, a fact that was pointed out
previously by Andreae (2009). This phenomenon can easily be illustrated
using the example of the activation curve during the RHaMBLe cruise in the
tropical North Atlantic. As will be discussed later, while the NCCN here
is comparable to several other locations, the hygroscopicity of the aerosol
is much higher, with the hygroscopicity parameter κ being just below
unity across all studied sizes. Yet, the fact that the aerosol is so
hygroscopic seems to affect the activation efficiency of the aerosol in a
similar manner as, for example, during the PRIDE-PRD2006 campaign in
southeastern China. During this campaign absolute NCCN was an order of
magnitude higher than during the RHaMBLe cruise (Table 2), and the
hygroscopicity was much lower (Rose et al., 2010). This order of magnitude
difference in NCCN, a large difference in κ and at least some
presumed difference in the shape of size distribution between the RHaMBLe
cruise and the PRIDE-PRD2006 campaign seem to result in no apparent
difference in the fraction of the aerosol that activates into cloud drops at
any given Seff. For most of the continental locations the overall fit
presented in Table 4 can provide a reasonable estimation of annual mean
NCCN based on the NCN for any given Seff. It should be kept in
mind, however, that the activation curves in Fig. 4 for the long-term
data sets do not reflect the potential short-term or seasonal variability,
which, as can be seen in the example of the three Pallas campaigns, can be
rather high. This and the fact that the short-term campaigns have been
conducted during different seasons mean that the overall fit represents the
annual mean activation behaviour and does not capture the variability on the
shorter timescales.
Average effective activated fractions A100
(NCCN/N100) and A50 (NCCN/N50) as a function of
supersaturation Seff for the four long-term measurement locations.
One important uncertainty associated with the comparison of the activation
curves in Fig. 4 is the precise size range from which NCN is
determined. In order for the activation curves to be directly comparable,
the lower size limit of NCN must be the same for all locations. In this
study, data of the lower limit of NCN for each location
(NCN,Dmin) were unavailable and, hence, this parameter was likely to
vary, complicating the comparison of activation curves in Fig. 4. To
circumvent the problem, to conduct a more accurate comparison and to reveal
more information about the effect of size distribution on CCN variability,
N100 and N50 concentrations were used instead of NCN to
calculate the effective activated fractions corresponding to a certain lower
cut-off diameter A100 and A50, respectively. These were calculated
for the four long-term measurement locations only (where the data were
available), and the results of the comparison are depicted in Fig. 5. When
N100 is used instead of NCN, the differences among locations
described above almost disappear except for the lowest values of S. In
general, the activation curve of A100 for Mace Head is similar to those
for Hyytiälä, Vavihill and Jungfraujoch for Seff above 0.4 %.
In other words, when one considers the fraction of only accumulation mode
particles that activates into cloud drops at any given Seff, the
difference in how Seff affects A at all examined locations diminishes. In
Hyytiälä, Vavihill and Jungfraujoch, particles with a dry diameter of
100 nm activate at the Seff of slightly higher than 0.2 % assuming an
internally mixed aerosol. Around this Seff Mace Head does exhibit a
slightly higher A100 compared to other locations, possibly due to the
increased CCN activity of the organically enriched Aitken mode aerosol
(Ovadnevaite et al., 2011).
When A50 is examined in detail, the difference between Mace Head and
other locations seen in Fig. 4 remains, with Mace Head exhibiting a higher
activated fraction compared to the three other locations. In
Hyytiälä, Vavihill and Jungfraujoch, particles with a dry diameter of
50 nm activate at a Seff of ∼0.7 %, while in Mace Head
these same particles activate at a Seff of ∼0.55 %.
Differences observed in Figs. 4 and 5 lead to the conclusion that
A50 and A100 have a more stable dependence on S; i.e. the variability
in the fraction of nucleation/Aitken mode particles among different
locations is large. Consequently, when comparing data sets of activated
fractions A from several locations with different expected concentrations of
nucleation/Aitken mode particles and instrumental set-ups, a recommendation
is made for the consideration of using N100 and/or N50
concentrations instead of NCN when calculating A coupled with A values
derived from total number concentrations. Besides more systematic comparison
of activation curves and, therefore, more accurate results, such an approach
can provide additional information about the effect of size distribution and
its variability, and hygroscopicity on CCN activation. The use of
A100 and A50 also diminishes the effect of the spatial variability
of the fraction of nucleation/Aitken mode particles, those less relevant for
CCN activation at typical ambient Seff levels.
Mean hygroscopicity parameter κ as a function of critical
dry diameter Dc for selected locations. Figure split in four panels for
more detail. Shown with one standard deviation.
Median and percentile κ values for Aitken (<100 nm)
and accumulation (>100 nm) mode particles for
Hyytiälä, Vavihill, Jungfraujoch and Pallas A and C.
<100 nm
>100 nm
Station
median
25th percentile
75th percentile
median
25th percentile
75th percentile
Hyytiälä
0.18
0.13
0.27
0.29
0.22
0.45
Vavihill
0.20
0.15
0.28
0.27
0.22
0.33
Jungfraujoch
0.18
0.12
0.28
0.22
0.16
0.31
Pallas A
0.09
0.07
0.13
0.13
0.09
0.20
Pallas C
0.18
0.15
0.27
0.25
0.19
0.37
CCN and their hygroscopicity
Critical dry diameter Dc and hygroscopicity parameter κ were
provided for the majority of the presented locations, and the variation of
κ with dry size is seen in Fig. 6 (the figure is split into four
panels for better visual representation). The variation of κ with
dry size is not the same everywhere, and three groups can be pointed out.
In the first group of locations κ clearly increases with size; this
is the case for Hyytiälä, Vavihill, Jungfraujoch (Fig. 6, upper
left panel), Pallas (Fig. 6, upper right panel), and for the four
campaigns conducted by the MPIC (Fig. 6, lower right panel). At these
locations accumulation mode particles have a higher hygroscopicity than the
Aitken mode particles, likely due to cloud processing. The results of the
Mann–Whitney U test (Mann and Whitney, 1947) for two populations that are not
normally distributed (below and above 100 nm of dry size; Paramonov et al.,
2013) reveal that in Hyytiälä, Vavihill, Jungfraujoch and Pallas A
and C the difference in κ is statistically significant at the 5 %
significance level; i.e. the median κ of Aitken and accumulation
mode particles are significantly different (Table 5). Published data for the
PRIDE-PRD2006, CAREBeijing-2006, CLACE-6 and AMAZE-08 campaigns have
previously reported such a trend (Rose et al., 2010, Gunthe et al., 2011,
Rose et al., 2013 and Gunthe et al., 2009, respectively). Data for Chilbolton
(Fig. 6, lower left panel) also reveal an increase in κ with size,
although absolute κ values at this site may be underestimated due to
the aerosol sample not being dried before entering the CCNC (Whitehead et
al., 2014). Such behaviour of κ leads to two implications. First, as
already discussed in Su et al. (2010) and Paramonov et al. (2013), the
hygroscopicity of the whole aerosol population can, and in some cases
should, be presented as a function of size; this can be done by way of
either separate κ values for the Aitken and accumulation mode
aerosol or hygroscopicity distribution functions. Values of κ
derived from the CCNC are frequently discussed in conjunction with the
chemistry information obtained, e.g. from the aerosol mass
spectrometer
(AMS) measurements. The second implication here is that if, due to
instrumental limitations, such measurements are representative only of the
accumulation mode particles, κ values derived from such measurements
should be extended to the Aitken mode particles with caution. The effect of
extending the accumulation mode κ down to the Aitken mode was
examined using detailed data from Hyytiälä as an example.
NCCN was calculated using the median annual size distribution and
Dc calculated with size-dependent and the assumed size-independent
κ values. It was found that if κ of the accumulation mode is
assumed to be the same for the Aitken mode, the NCCN, on average, is
overestimated by 16 and 13.5 % for the Seff of 0.6 and
1.0 %, respectively.
The second group of locations, or in this case only one location, exhibits a
decrease of κ with particle dry size, and such a trend exists only
for the COPS campaign (Fig. 6, lower left panel). Apparently, at the
mountainous site in the Black Forest region of south-west Germany the
chemical composition of the accumulation mode aerosol makes it less
hygroscopic compared with the Aitken mode at supersaturated conditions
(Irwin et al., 2010). However, the same study reported that the measurements
by the hygroscopicity tandem DMA (HTDMA) in a sub-saturated regime revealed
an increase of κ with particle dry size.
The third group of locations, represented by the K-puszta station and
RHaMBLe measurement campaign, is characterised by the absence of any
dependence of κ on the particle dry size. Though quite different in
magnitude (Fig. 6, lower left panel), κ values and, therefore,
aerosol chemical composition seem to have no particular size dependence
across the whole measured size range. Also of interest is the high aerosol
hygroscopicity across the whole investigated aerosol size range (Aitken
mode) during the RHaMBLe cruise – all κ values are just below unity
(Good et al., 2010). The marine nature of the aerosol and clean background
conditions of the remote tropical North Atlantic are likely responsible for
high aerosol hygroscopicity.
Monthly median Dc at the Seff of 0.1 % (upper) and
1.0 % (lower) for three long-term measurement locations. Error bars are
25th and 75th percentiles.
Three of the four long-term data sets, excluding Mace Head, included
Dc and κ data, making it possible to examine aerosol
hygroscopicity both on the annual basis and diurnal basis separated by
seasons. Figure 7 presents the annual variation of Dc for lowest and
highest Seff levels in Hyytiälä, Vavihill and Jungfraujoch. As
can be seen in the y axis of the upper panel, particles measured at the
Seff of 0.1 % are in the accumulation mode, i.e. Dc is larger than
100 nm in diameter. Of the three stations presented, Dc has an annual
pattern only in Hyytiälä, with a minimum Dc and an increased
hygroscopicity in the winter and a maximum Dc and a decreased
hygroscopicity in the summer, as previously reported by Paramonov et al. (2013). The likely reason for a decrease in the accumulation mode particle
hygroscopicity in Hyytiälä in the summer is the increase in the
emissions of the volatile organic compounds (VOCs), leading to an increase
in secondary organic aerosol (SOA) formation and, thus, a higher organic
fraction. The higher hygroscopicity in the winter can also be explained by a
higher sulphate fraction, stronger aerosol oxidation and potentially other
ageing processes that are known to increase particle hygroscopicity
(Furutani et al., 2008). No annual pattern is present in the aerosol
hygroscopicity of accumulation mode aerosol in Vavihill and Jungfraujoch.
The lower panel in Fig. 7 depicts the annual variation of aerosol
hygroscopicity for the Aitken mode aerosol, revealing no pattern for any of
the three locations. The absence of a pattern coupled with the absence of an
apparent difference among sites indicates that the aerosol hygroscopicity of
Aitken, ∼50 nm aerosol is fairly similar and constant
throughout the year at all three locations.
Hourly median critical dry diameters Dc at the Seff of
1.0 % for three long-term measurement locations separated by seasons.
Shaded areas represent the 25th and 75th percentiles, with colours
corresponding to the median data series.
The diurnal patterns of aerosol hygroscopicity were analysed for
Hyytiälä, Vavihill and Jungfraujoch on a seasonal basis. It was
discovered that for the accumulation mode particles, those measured at the
Seff of 0.1 %, no diurnal pattern was observed at any of the three
locations in any of the seasons, indicating that throughout the day
photochemistry does not have any apparent effect on the hygroscopicity of
the accumulation mode particles. Diurnal patterns of aerosol hygroscopicity
for Aitken mode particles can be seen in Fig. 8. In the winter no
particular pattern is visible at any of the locations; it can, however, be
seen that while the aerosol hygroscopicity is similar between
Hyytiälä and Vavihill, the Aitken mode aerosol at the Jungfraujoch
is less hygroscopic. In the spring both Hyytiälä and Vavihill
exhibit a clear diurnal pattern, which extends also into the summer. A peak
in aerosol hygroscopicity is observed around midday when Dc reaches its
minimum. Several previous studies have reported such behaviour in
Hyytiälä and have attributed it to the vegetation activity,
photochemistry and the ageing of organics during sunlight hours (Sihto et
al., 2011; Cerully et al., 2011; Paramonov et al., 2013). While no diurnal
pattern of aerosol hygroscopicity is visible for Jungfraujoch for winter and
spring, a very clear pattern does exist in the summer and autumn. In these
seasons Aitken mode particles exhibit an obvious decrease in hygroscopicity
in the afternoon shown by the peak in Dc during these hours. This
phenomenon has also been previously reported and attributed to the daytime
intrusions of air from the planetary boundary layer (PBL) injecting less
hygroscopic particles into the free troposphere (Kammermann et al., 2010a).
The discussion above demonstrates that diurnal patterns of hygroscopicity
are not the same everywhere and vary by seasons; however, the environments
of Hyytiälä and Vavihill are similar enough to result in similar
diurnal patterns.
Conclusion
CCNC measurement data from 14 locations, including four long-term
measurement sites, have been analysed, compared and discussed with respect
to the deduced CCN activation and hygroscopic properties. As already known,
the pattern of how NCCN and A respond to the increasing S is indicative of
the total NCN concentrations, the size distribution of the pre-existing
aerosol population and its hygroscopicity. Certain marine locations
exhibited high A values and rapidly increasing NCCN even at low S values,
as was the case during the COPS campaign in south-west Germany. At these
locations aerosol populations are likely accumulation mode-dominated and/or
of relatively high hygroscopicity. Pallas, a remote background location in
northern Finland, exhibited a pattern of low A values and slowly increasing
NCCN at low S values, revealing the likelihood of Aitken mode-dominated
aerosol and/or fairly low hygroscopicity at this site. Jungfraujoch, a high
Alpine site in the free troposphere, also exhibited comparatively low A
values, as the particle number is often dominated by the Aitken mode
particles. For the rest of the studied locations (the majority), the pattern
of increasing A with increasing S was similar, i.e. at most locations the same
fraction of aerosol activated into cloud drops at any given S. For example,
20 % of the total aerosol population at most locations will activate into
cloud drops at the S of 0.1 %. A simple linear fit for estimating annual
mean NCCN at most continental locations is presented. When comparing
activated fractions A at several locations, a recommendation is made to use
N100 and/or N50 when calculating A values together with A values
derived from total number concentrations. Using this technique, a more
accurate comparison should be performed for sites where the exact size range
of NCN is not known and where the concentrations of nucleation/Aitken
mode particles are expected to be high, additionally revealing more
information about the effect of size distribution and hygroscopicity on CCN
activation.
The hygroscopicity of aerosol particles as a function of size is not the
same at all locations; while κ decreased with increasing size at a
continental site in south-west Germany and was fluctuating without any
particular size dependence across the observed size range in the remote
tropical North Atlantic and rural central Hungary, all other locations
exhibited an increase of κ with size. In fact, at the rural
background sites of southern Finland and southern Sweden, at a free
troposphere site in the Swiss Alps and at a remote background site in
northern Finland the difference in hygroscopicity between Aitken and
accumulation mode aerosol was statistically significant at the 5 %
significance level. Therefore, assuming a size-independent κ can
lead to a substantial overestimation of NCCN at higher levels of
Seff (those above 0.6 %). The hygroscopicity of the whole aerosol
population can be presented separately for Aitken and accumulation mode
particles; additionally, hygroscopicity distribution functions can be used
to analyse size-resolved CCNC data and efficiently describe the size
dependence of κ (Lance, 2007; Su et al., 2010; Jurányi et al.,
2013). It is known, however, that in most cases the size distribution and
its variation have a larger effect on the NCCN than the particle
hygroscopicity and its variation with size.
Among Hyytiälä, Vavihill and Jungfraujoch, no annual pattern of
aerosol hygroscopicity was found for the Aitken mode aerosol. The
accumulation mode aerosol exhibited a discernible annual pattern only in
Hyytiälä, where a peak in hygroscopicity was found in February and a
minimum in July. Such a pattern is likely attributed to the higher sulphate
fraction and stronger aerosol oxidation in the winter and active SOA
formation and higher organic fraction in the summer. Among the same three
sites, no diurnal trend of aerosol hygroscopicity was found for accumulation
mode aerosol. The hygroscopicity of the Aitken mode aerosol in
Hyytiälä and Vavihill follows a clear diurnal pattern in the spring
and summer – an increase in aerosol hygroscopicity was observed in the
afternoon, likely due to the photochemistry and ageing of the organics. At
the Jungfraujoch, Aitken mode aerosol showed a decrease in aerosol
hygroscopicity in the afternoon during the summer and autumn; this
phenomenon is caused by the injections from the planetary boundary layer
containing somewhat less hygroscopic aerosol.
In general, the comparison of CCNC measurements is complicated by the
variation of instrumental set-ups, settings, measurement times and intervals,
performed calibrations, calculations and available parameters among sites.
Supplementary data, such as aerosol size distribution and chemical
composition, can enhance the uniformity of the analysis and expand the
representativeness of the aforementioned results. However, as the first
overview of its kind, the summary of CCNC measurements discussed here
presents a unique insight into the CCN activation and hygroscopic properties
in Europe and a few non-European sites. While, as shown here, CCNC
measurements can provide useful information about the CCN and their
activation into cloud droplets, the missing link in the aerosol–cloud
interactions is the connection of CCN to the ambient CDNC. If filled, this
gap can greatly improve our understanding of the processes and feedbacks
within the aerosol–cloud–climate triangle and enhance the performance and
accuracy of the global climate models.