Introduction
Cloud droplets are formed by activation of a subset of aerosol particles
called cloud condensation nuclei (CCN), which affect the radiative
properties of clouds through modifying the cloud droplet number
concentration (CDNC), cloud droplet size, cloud lifetime and precipitation
processes (e.g. Rosenfeld et al., 2014). To date, radiative forcing
through aerosol–cloud interactions (ACI) constitutes the least understood
anthropogenic influence on climate (IPCC, 2013): the
uncertainty in aerosol-induced radiative forcing of ±0.70 W m-2
(from a mean of -0.55 W m-2) is twice the uncertainty for
CO2 (±0.35, mean +1.68 W m-2). This uncertainty
propagates through the calculation of climate sensitivity, a
variable that expresses the global temperature increase for given
emission scenarios (Andreae et al., 2005; Seinfeld et al., 2016). It
remains a significant challenge to reduce these uncertainties and to thereby
increase our confidence in predictions of global and regional climate change
(IPCC, 2013; Lee et al., 2013; Seinfeld et al., 2016).
The number concentration of CCN is not the only factor determining the CDNC;
the dynamics and structure of the cloud is profoundly important as well.
Reutter et al. (2009) found that cloud droplet
formation can be limited by the presence of CCN (CCN-limited regime),
by the updraft velocity (updraft-limited regime) or both (transition
regime). Globally, however, the CCN-limited regime prevails
(Rosenfeld et al., 2014). Among the main factors driving the
uncertainty in simulating CCN abundance are the aerosol particle number size
distributions, size-dependent removal processes, the contribution of
boundary layer new particle formation events to particle number
concentration and their size, the particle number size distribution of
emitted primary particles, the particle activation diameter, the formation
of biogenic and anthropogenic secondary organic aerosol (SOA), and the
processing of SO2 in clouds into particulate sulfate (e.g. Croft et
al., 2009; Lee et al., 2013; Wilcox et al., 2015). Information on aerosol
hygroscopicity is also needed to constrain uncertainty
(Rosenfeld et al., 2014). These factors affect the ability of
aerosol particles to form CCN on a large scale and in long-term periods as
well as on the regional scale and in short-term periods.
To improve model performance, data from measurements of particle number size
distribution, CCN number concentrations, aerosol particle chemical
composition and hygroscopicity are needed (Carslaw et al., 2013; Ghan and
Schwartz, 2007; Rosenfeld et al., 2014; Seinfeld et al., 2016). Satellite
observations, covering large scales and longtime horizons, can provide
proxies of these variables. However, the resolution is often too coarse to
study detailed ACIs (Rosenfeld et al., 2014, 2016;
Shinozuka et al., 2015) and other shortcomings
exist. For example, a common proxy is aerosol optical depth (AOD). It has
been found that the correlation of AOD with CCN number concentrations, a key
assumption in this approach, is strongly dependent on ambient relative
humidity (RH) and aerosol types. Furthermore, these correlations become less
reliable when sea salt and mineral dust constitute an important fraction of
the particle number, a situation which can be relevant over the ocean or
deserts (Liu and Li, 2014). This makes in situ measurements
indispensable and therefore numerous studies of CCN activity have been
carried out in a variety of environments, ranging from remote marine over
continental background to urban locations, and in the laboratory (e.g.
Andreae, 2009a; Asmi et al., 2012; Bougiatioti et al., 2009; Crosbie et al.,
2015; Cubison et al., 2008; Ervens et al., 2010; Jurányi et al., 2010;
Paramonov et al., 2015; Rose et al., 2011; Whitehead et al., 2014; Wong et
al., 2011). Most of these observations focus on relatively short time
periods and some attempt to capture specific circumstances such as biomass
burning events (e.g. Bougiatioti et al., 2016)
or focus on the hygroscopicity of specific aerosol particle components such
as black carbon (e.g. Schwarz et al., 2015) or organic carbon (e.g. Frosch et al., 2011).
While such studies provide detailed insights into CCN activation processes
and contribute to our comprehensive understanding of ACI,
they cannot address questions of regional and temporal CCN
variability. However, those aspects are crucial for model evaluation. Also,
knowledge of the size distribution, composition and hygroscopicity of aerosol
components, and atmospheric aerosols in different environments as well as
appropriate representation in model simulations, is important to quantify
aerosol radiation interactions as a function of relative humidity.
They are best addressed through long-term observations at regionally
representative locations. Among the scarce examples of such studies are
observations at the high alpine site Jungfraujoch (Jurányi
et al., 2011), in the Amazon rain forest (Pöhlker et al., 2016) or
several other European stations (Mace Head, Ireland, coastal background;
Hyytiälä, Finland, boreal forest and Vavihill, Sweden, rural
background) before or during the European Integrated project on Aerosol
Cloud Climate and Air Quality Interactions (EUCAARI) experiment (Fors et
al., 2011; Paramonov et al., 2015; Sihto et al., 2011). Further examples of
long-term studies include a study at an urban background site in Vienna,
Austria (Burkart et al., 2011), at a regionally
representative site in the Yangtze River Delta (Che et al., 2016) or at an urban site in
Shanghai (Leng et al., 2013). In addition to revealing the
seasonal and regional variability in CCN number concentrations and
associated variables, such long-term studies can address the question of
which specific aerosol particle characteristics need to be monitored to
provide data sets with which models can be effectively evaluated. Such
studies are particularly valuable given general constraints that will not
allow operating very comprehensive aerosol characterisation equipment over
long periods of time at many locations. One specific question is whether CCN
number concentrations need to be measured directly, e.g. with cloud
condensation nuclei counters (CCNC) or whether they can be inferred by
knowing the critical diameter at which particles activate as cloud droplets.
A simple parameterization was developed from the κ–Köhler theory
(Petters and Kreidenweis, 2007), which links aerosol
particle hygroscopicity with the critical diameter at a given
supersaturation and hence leaves the particle number size distribution as
determining variable for CCN number concentrations. The hygroscopicity
parameter, κ, can be calculated from the aerosol particle chemical
composition. So theoretically, it would not be necessary to operate a CCNC
if particle number size distribution and chemical composition measurements
were available. This, however, leads to the question of which degree of
detail is needed for the chemical composition and mixing state of the
aerosol particles to derive their hygroscopicity. However, there is no
unanimous conclusion in the literature. Some studies find that the
variability in aerosol size distribution is more important than the
variability in chemical composition (e.g. Dusek et al., 2006; Ervens et
al., 2007) and a review (Andreae and Rosenfeld, 2008) suggests
that a global hygroscopicity parameter of κ = 0. ± 0.1 and
κ = 0.7 ± 0.2 can be useful as a first approximation for
continental and marine aerosol, respectively. Conversely, other studies
stress the importance of not only knowing the bulk composition of particles
but also their size-resolved chemical composition and state of mixing or
even the more detailed composition of organic carbon. This is because
organic aerosol usually constitutes an important fraction of the CCN
relevant aerosol mass around the globe (Zhang et al., 2007) and more
oxygenated aerosol tends to be more hygroscopic (Cubison et al., 2008;
Duplissy et al., 2008; Frosch et al., 2011; Jimenez et al., 2009; Massoli et
al., 2010; Wong et al., 2011). In addition, several studies have
investigated the effect of organic surfactants that can decrease the surface
tension (e.g. Charlson et al., 2001; Facchini et al., 2000). It is
expected that the effect of surface tension suppression by surfactants is
smaller than predicted by the classical Köhler theory due to
surface-bulk partitioning effects unless liquid–liquid phase separation
occurs (Sorjamaa et al., 2004). A recent study, however, shows
that a combination of liquid–liquid phase separation, surfactants and
specific particle size distributions could increase the CCN number
concentration by a factor of 10 compared to climate model predictions
(Ovadnevaite et al., 2017). More generally, the importance of
a detailed knowledge of the particle chemical composition for CCN activity
depends on the distance from the source as more aged particles tend to
assume similar particle number size distributions and hygroscopic
characteristics (e.g. Andreae, 2009b; Ervens et al., 2010).
In this study, we present long-term observations from 12 locations of
collocated particle number size distributions, CCN number concentrations
and, in some cases, aerosol particle chemical composition measurements. Eight
of these stations are part of the European Aerosols, Clouds, and Trace gases
Research InfraStructure (ACTRIS, http://www.actris.eu/), while
the other observatories are located in South Korea, Japan, the USA and Brazil.
They cover a range of environments such as coastal and rural backgrounds,
urban and high alpine conditions, as well as boreal, Arctic and rain forest
characteristics. We explore the frequency distributions and seasonal cycles
of various variables (CCN number concentration, critical diameter, κ values
and others), the persistence of CCN number concentrations in winter
and summer, and particle activation behaviour. We also perform closure
studies based on the κ–Köhler parameterization and test the
sensitivity of results to simplified assumptions regarding aerosol chemical
composition, particle number concentrations and size distributions.
Methodology
Measurement sites and instrumentation
Figure 1a shows the locations of the 12 observatories, which span a wide
range of environments. Four stations are located near the coast, covering
Arctic (BRW), Mediterranean (FIK), Atlantic (MHD) and Pacific conditions (NOT).
Two alpine stations in Europe (PUY, JFJ) represent the continental
background and partly free tropospheric air masses, while three
observatories near sea level in Europe characterise the rural background
conditions (MEL, CES, VAV). The boreal (SMR) and rain forest (ATT)
environments are represented by one station each, as well as one urban
location in Asia (SEO; compare grouping in Fig. 1b). Table 1 provides an
overview of each station's characteristics and representativeness.
This study uses data from concomitant measurements of CCN number
concentrations, particle number size distributions and, where available,
bulk aerosol particle chemical composition. Table 1 lists the instrumental and operational details. All information regarding
each station's inlet system, instrument descriptions and sampling details is
given in the related data descriptor paper (Schmale et al., 2017), except
for the rainforest station (ATTO), which is described in Pöhlker et al. (2016).
Since the focus is on long-term observations rather than
short-term intensive field campaigns, the data used were chosen to cover at
least 75 % of each season within 12 consecutive months. Seasons are
defined as December, January, February (DJF); March, April, May (MAM); June,
July, August (JJA) and September, October, November (SON) if not referred to otherwise.
Briefly, CCN number concentrations were measured with the CCNC-100 model by
Droplet Measurement Technologies (DMT; Roberts and Nenes, 2005) in all cases except at Puy de Dôme,
where a miniature version of this instrument was deployed (Sullivan et al., 2009). Most stations measured
in the polydisperse mode, where the activation of the entire aerosol
population is measured at a given supersaturation. At four stations (ATT,
MEL, PUY, NOT), CCN number concentrations were determined in the
monodisperse mode, whereby particles are selected by a differential mobility
analyzer (DMA) that scans through a range of particle diameters upstream of
the CCNC. Regardless of the operation mode, this work considers exclusively
the time series of the bulk activated aerosol, meaning that monodisperse CCN
number concentrations were integrated over the covered size ranges.
(a) Map showing all measurement sites. Station abbreviations
are given in Table 1. All stations in Europe are part of the ACTRIS network.
This map is adapted from Natural Earth III and Schmale et al. (2017).
(b) Median and interquartile ranges of the seasonal CCN number
concentrations at a supersaturation of 0.2 % are displayed for each station.
The shaded areas group the stations into the classifications indicated.
Particle number size distributions were obtained by a variety of mobility
particle size spectrometers (MPSS) as listed in
Table 1, which are either commercially available or
custom-built. All custom-built versions have been intercompared at the World
Calibration Center for Aerosol Physics (Wiedensohler et al., 2012, 2018) or audited by it.
List of measurement sites participating in this synthesis study.
Station names followed by an asterisk (*) are part of the ACTRIS network.
Abbreviations correspond to those within the Global Atmosphere Watch network/programme.
Station name
ATTO
Barrow
CESAR Tower
Finokalia
Jungfraujoch
Melpitz
Station
abbreviation
ATT
BRW
CES
FIK
JFJ
MEL
information
country
Brazil
Alaska, USA
the
northern
Switzerland
Germany
Netherlands
Crete, Greece
coordinates
02∘07′ S,
71∘19'′ N,
51∘58′ N,
35∘20′ N,
46∘33′ N,
51∘32′ N,
58∘60′ W
156∘37′ W
04∘56′ E
25∘40′ E
07∘59′ E
12∘56′ E
elevation m a.s.l.
130
11
-1
250
3580
86
site category
rainforest
Arctic
near coast,
coastal
high alpine,
continental
maritime,
rural back-
background,
background
background
coastal
ground
Mediterranean
CCN
instrument type
DMT CCN-100
DMT CCN-100
DMT CCN-100
DMT CCN-100
DMT CCN-100
DMT CCN-100
measurements
time coverage
Mar 2014–
Jul 2007–
Oct 2012–
Nov 2014–
Jan 2012–
Aug 2012–
Feb 2015
Jun 2008
Apr 2014
Sep 2015
Dec 2014
Nov 2014
operation mode
monodisperse
polydisperse
polydisperse
polydisperse
polydisperse
monodisperse
supersaturations (%)
0.11, 0.15,
0.20, 0.30,
0.10, 0.20,
0.20, 0.40,
0.10, 0.15,
0.10,
0.20, 0.24,
0.50, 0.60,
0.30, 0.50,
0.60, 0.80,
0.20, 0.25,
0.20,
0.29, 0.47,
1.00, 1.20,
1.00
1.00
0.30, 0.35,
0.30,
0.61, 0.74,
1.45
0.40, 0.50,
0.50,
0.90, 1.10
0.70, 1.00
0.70
Size distribution
instrument type
SMPS,
TROPOS-type
SMPS TSI 3034
TROPOS-type
custom-built
TROPOS-type
measurements
TSI 3080
custom-built
custom-built
SMPS
Dual SMPS
SMPS
SMPS
custom-built
time coverage
Mar 2014–
Sep 2007–
Jan 2012–
Nov 2014–
Jan 2012–
Jan 2012–
Feb 2015
Jun 2008
Dec 2014
Sep 2015
Dec 2014
Jun 2014
diameter range (nm)
> 9–445
10–810
10–516
9–849
20–600
5–800
Chemical
instrument type
Q-ACSM
Q-ACSM
Q-ACSM
ToF-ACSM
Q-ACSM
composition
time coverage
Mar 2014–
Jul 2012–
Sep 2014–
Jul 2012–
Jun 2012–
measurements
Feb 2015
May 2013
Sep 2015
Jul 2013
Jun 2014
species
ammonium,
ammonium,
ammonium,
ammonium,
ammonium,
chloride,
chloride,
chloride,
nitrate,
chloride,
nitrate,
nitrate,
nitrate,
organics,
nitrate,
organics,
organics,
organics,
sulfate
organics,
sulfate
sulfate
sulfate
sulfate
collection efficiency
1.0 (Jan–Jul);
based on
0.5
1
based on
0.5 (Aug–Dec)
Mensah et al.
Middlebrook et
(2012)
al. (2012)
Continued.
Station name
Mace Head
Noto Peninsula
Puy de Dôme
Seoul
Smear
Vavihill
Station
abbreviation
MHD
NOT
PUY
SEO
SMR
VAV
information
country
Ireland
Japan
France
South Korea
Finland
Sweden
coordinates
53∘20′ N,
37∘27′ N
45∘46′ N,
37∘34′ N
61∘51′ N,
56∘01′ N,
09∘54′ W
137∘22′ E
02∘57′ E
126∘58′ E
24∘17′ E
13∘09′ E
elevation m a.s.l.
5
0
1465
38
181
172
site category
coastal
coastal
mountain,
urban,
rural
rural
background
background
continental
monsoon
background,
background
background
influence
boreal forest
CCN
instrument type
DMT CCN-100
DMT CCN-100
mini-CCNC
DMT CCN-100
DMT CCN-100
DMT CCN-100
measurements
time coverage
Jul 2011–
May 2014–
Nov 2014–
Oct 2006–
May 2012–
Dec 2012–
May 2012
Feb 2015
Sep 2015
Dec 2010
Dec 2014
Nov 2014
operation mode
polydisperse
monodisperse
monodisperse
polydisperse
polydisperse
polydisperse
supersaturations (%)
0.10, 0.25,
0.10, 0.20,
0.2
0.20, 0.40,
0.10, 0.20,
0.10, 0.15,
0.35, 0.50,
0.50, 0.80
0.60, 0.80
0.30, 0.50,
0.20, 0.25,
0.75, 1.00
1.00
0.30, 0.35,
0.40, 0.50,
0.70, 1.00,
1.40
Size distribution
instrument type
custom-built
DMA: TSI
custom-built
TSI SMPS
UHEL-type
ULUND-type
measurements
SMPS
Model 3081L,
DMPS
3936L10
custom-built
custom-built
CPC: TSI Model
Dual DMPS
Dual-DMPS
3776
time coverage
Jan 2011–
May 2014–
Nov 2014–
Jan 2006–
Jan 2012–
Dec 2012–
Dec 2012
Feb 2015
Sep 2015
Dec 2010
Jun 2014
Nov 2014
diameter range (nm)
25–500
8–342
10–400
> 10–478
> 3–1000
> 3–900
Chemical
instrument type
HR-ToF-AMS
Q-ACSM
composition
time coverage
Jan 2011–
Mar 2012–
measurements
Dec 2012
Sep 2013
species
ammonium,
ammonium,
chloride,
chloride,
nitrate,
nitrate,
organics,
organics,
sulfate,
sulfate
sea salt
collection efficiency
based on
0.52
Middlebrook et
al. (2012)
Submicron aerosol particle chemical compositions were measured by two
different types of aerosol mass spectrometers. The high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS) operated at Mace Head
has been described by DeCarlo et al. (2006) in general and in particular for
Mace Head by Ovadnevaite et al. (2014). The aerosol chemical
speciation monitor (ACSM), deployed at all other stations, has been
introduced by Ng et al. (2011) and the
first official ACTRIS intercomparison is described in Crenn et al. (2015).
The intercomparison covers all quadrupole ACSMs, except the one
deployed at ATTO, which is described in Pöhlker et al. (2016). On
Jungfraujoch, a time-of-flight ACSM was operated as described by
Fröhlich et al. (2013, 2015). All aerosol mass spectrometer types are
able to provide the mass concentrations of standard chemical species that
include particulate ammonium, chloride, nitrate, organics and sulfate in
the submicron size range. Table 1 lists which
species are available at each station; missing species mean that their
concentrations were below the detection limit. At Mace Head, the sea salt
content of the submicrometer aerosol is given in addition based on a
specific method introduced by Ovadnevaite et al. (2012).
Table 1 also lists the collection efficiency (CE) of each mass spectrometer.
The CE depends on the transmission of particles into the instrument and
their chemical composition and is hence an instrument and site-specific
factor (Huffman et al., 2005; Middlebrook et al., 2012).
Additionally, at the time of data collection, equivalent black carbon (BC)
mass concentrations were available for the stations JFJ (aethalometer model AE31,
Magee Scientific), MEL and MHD (multi-angle absorption photometer,
MAAP, Thermo Scientific), which are used for the sole purpose of calculating
the hygroscopicity parameter κ (see Sect. 2.3.2). For stations
where no concomitant BC concentration time series were available, BC mass
fractions from the literature were used as approximation as described in Sect. 2.3.2.
Data treatment and quality assurance
The collection, harmonisation and quality assurance of the data sets
presented here are described in detail in the data descriptor by Schmale et
al. (2017). Data have a time resolution of 1 hour and represent standard
temperature and pressure (STP) conditions. The time resolution of CCN number
concentrations at Puy de Dôme (PUY) and ATTO are 4 and 6 hours,
respectively, because the scans over the submicron aerosol size range in
monodisperse mode took longer. Most instruments measuring particle number
size distributions had been intercompared, audited or the data had
been published previously (see Table 9 in Schmale et al., 2017). The same
was the case for the chemical composition data (same reference). For that
reason, emphasis was given to the quality check of the CCN number
concentrations that had not previously been published in most cases.
Exceptions are the data from Seoul (Kim et
al., 2014) and ATTO (Pöhlker et al., 2016), whereby the latter
station is not included in Schmale et at. (2017). Note that the aerosol
sample flow was kept at a relative humidity < 40 % at all sites
except in Seoul, meaning that particle size can be biased large. For all
polydisperse data sets where measurements at a supersaturation of 1.0 %
were available, the total CCN number concentration was compared to the total
particle number concentration in all instances when the contribution of
particles < 30 nm was at most 10 %. It is expected that at such a
high supersaturation, almost all particles > 30 nm activate. Hence
the data points are expected to group around the 1 : 1 line within the target
uncertainty of 10 % (Wiedensohler et al., 2012). Figure 4 in Schmale
et al. (2017) shows that most instruments performed reasonably well, with
the exception of the CCNCs at the Cesar (CES) and Jungfraujoch (JFJ)
stations. At CES the CCN number concentration is strongly underestimated,
and the underestimation increases with increasing supersaturation.
Discrepancies are as large as a factor of 3.3 in the geometric mean for
1.0 % supersaturation. This suggests that small particles, activating at
higher supersaturation, were not sufficiently accounted for by the CCNC. As
this was not due to insufficient droplet growth to the detection limit of
1 µm of the optical particle counter in the CCNC, the bias most likely
originated from particle losses in the sampling line to the CCNC. Since this
cannot be accounted for across the various supersaturations, the data set has
not been corrected. Therefore the CCN number concentrations reported for CES
represent a lower limit. Details for JFJ have already been discussed in
Schmale et al. (2017). More details for both stations are provided in
Sect. S1 in the Supplement (hereafter referred to as Sect. S1).
At the observatories in Melpitz (MEL), NOT and PUY, CCN were not measured at
a supersaturation of 1.0 % but in monodisperse mode. Therefore, the
integrated particle number concentration above the critical diameter at a
measured supersaturation (diameter at which particles activate) was plotted
against the integrated CCN number above the same diameter. The CCN number
size distribution data at both stations compare well with the particle
number size distributions (see Fig. 5 in Schmale et al., 2017).
All data (except for ATTO) are available from: http://actris.nilu.no/Content/products.
The ATTO data have been published by Pöhlker et al. (2016).
Data analyses
Frequency distributions, seasonal cycles and persistence
The CCN number concentration frequency distributions were calculated in
200 bins with a logarithmic (log10) spacing of 0.023, starting with
1 particle (cm-3). Frequency distributions of the particle number size
distributions' geometric mean diameter (Dg) were calculated for the
available particle diameter range at each station, and also starting at a
lower cut-off of 20 nm for comparability. The frequency distributions of Dg
as well as the critical diameters (Dcrit) are based on 105 bins
with a logarithmic (log10) spacing of 1/64. The value of Dcrit was
derived from integrating the particle number size distributions from their
maximum diameters to that diameter at which the integrated particle number
equaled the measured CCN number concentration (see also Sect. 2.3.2, Eq. 5).
All frequency distributions are normalised to the number of data points at each station.
Seasonal cycles are represented by the monthly medians calculated from the
hourly values of the respective variable (4 and 6 hourly data for CCN
at PUY and ATT, respectively). If a particular month is covered several
times in a time series, the median of all data acquired in that month is
derived. Additionally, the interquartile range has been calculated in the same way.
The CCN number concentrations at the regionally representative stations
discussed here are influenced by a variety of factors that include the
microphysical and chemical characteristics of the particles, atmospheric
transport, dry and wet particle deposition, synoptic patterns, and seasonal
source strengths. For example, the boreal forest produces more SOA in the growing season (summer) than in
winter. Determining the persistence of CCN number concentrations, i.e. the
duration over which their concentration remains similar, can help to
identify regionally relevant factors that significantly influence the
abundance of CCN. At each station, the persistence was calculated by
auto-correlating the time series for the winter (DJF) and summer (JJA)
months. Data gaps of less than 1 day were filled by the average of the
preceding four data points. Large data gaps, exceeding 1 day, were not
filled. Instead shorter periods of the season were auto-correlated
separately and then averaged. This was the case for JFJ and BRW in winter,
and MHD, FIK and BRW in summer. The auto-correlation function “acf” in the
program R (version 3.3.1) was applied to the time series of CCN at a
supersaturation of 0.2 % with 1 hour time resolution, except for ATT
and PUY where the highest time resolutions were 6 and 4 hours,
respectively. The significance level of the auto-correlation was determined
by calculating the large lag standard error, Ecorr, of the
auto-correlation coefficient, accounting for the interdependency between
auto-correlation coefficients, following Eq. (1):
Ecorrrk=1N1+2∑i=1Kri2,
with N being the number of data points, rk the correlation coefficient at
lag k and K < k with K being the last lag of a specific calculation
step. The higher the number of observations, the larger Ecorr
becomes, and with this the likelihood of identifying a potentially randomly high
correlation at a large lag as significant. The persistence of a property is
determined by the time coordinate at which the auto-correlation curve
crosses the large lag standard error curve.
Hygroscopicity parameter kappa (κ) and CCN closure
The hygroscopicity parameter, κ, quantifies the Raoult effect, i.e.
the relationship between the particle's hygroscopic equilibrium growth
factor (GF) and corresponding water activity. When assuming a surface tension
and using the Köhler equation, which combines the Raoult and Kelvin
effects to the related GF and RH at equilibrium, the κ value
unambiguously relates the dry particle size with the critical
supersaturation (Petters and Kreidenweis, 2007): the
higher the value of κ, the higher the hygroscopicity of a particle
(Zieger et al., 2017). The κ of a mixed
particle can be derived in good approximation from the particle chemical
composition following a simple mixing rule as given in Eq. (2) when the
κ value of each component i is known (Petters and Kreidenweis, 2007):
κ=∑iεiκi,
with εi being the volume fraction of component i. The volume
fraction of each component was derived from its measured mass concentrations
and density (1.4 g cm-3 was assumed for organic aerosol) in this work.
Comparison of average hygroscopicity parameters (κmean)
provided in Table 1 in Petters and Kreidenweis (2007) with the κ values
derived in this work based on a water activity of 0.9975 at the point of CCN
activation as input to the E-AIM model II and IV (http://www.aim.env.uea.ac.uk/aim/model2/model2a.php).
The water activity was derived from the following assumptions: κ = 0.3,
supersaturation SS = 0.5 %, temperature T = 5 ∘C, and surface tension σ = 74.95 mN m-1.
The ideal κ values refer to a water activity of 1. Note that the growth-factor-derived values
in Petters and Kreidenweis (2007) are based on a water
activity of about 0.9. For NaCl the value reported in Petters and Kreidenweis (2007)
is too low and should be around 1.5 instead (Zieger et al., 2017).
The κ values of pure substances typically depend on water activity.
Petters and Kreidenweis (2007) provide κ values
for a variety of chemical components including inorganic salts and acids.
These, however, only partly refer to conditions at the point of particle
activation. We therefore calculated the pure component κ values for
a reference water activity of aw = 0.9975 following
Petters and Kreidenweis (2007):
1aw=1+κVsVw.
Vs is the volume of the dry particulate matter and Vw the volume of
water. The reference aw was chosen to reflect the water activity in the
solution droplet at the point of CCN activation for a supersaturation of 0.5 %,
temperature of 5 ∘C, corresponding pure water surface
tension of 74.95 mN m-1 and κ of 0.3. These properties and
conditions are typical for cloud formation in ambient clouds and they imply
a critical dry particle diameter of 63 nm. Note that the temperature has
only a minimal effect on the κ of a pure component, while it affects
CCN activation through the temperature dependence of surface tension and the
Kelvin effect. This reference water activity was used as input to the E-AIM
model II and IV (http://www.aim.env.uea.ac.uk/aim/aim.php), by which the
particulate water content was calculated for the pure salts and inorganic
acids in aqueous solution. The E-AIM II model is an equilibrium
thermodynamic model including the following ions: H+, NH4+,
SO42-, NO3- and H2O. It is valid from 328 K to about
200 K. Model IV includes Na+ and Cl- and is valid from 180 to 330 K.
Based on this, the GFs and ,from that, the κ values were calculated
for sulfuric acid, ammonium sulfate, ammonium bisulfate, ammonium nitrate
and sodium chloride, accounting for the solution density which is provided
by the AIM model. Note, we did not account for the water content of the
chemical species in dry conditions, e.g. RH = 10 %. The chemical
species were derived from ions quantified by the mass spectrometric
measurements following the procedure suggested by Gysel et al. (2007). The results (shown in
Fig. 2) are generally similar to and slightly lower than the ideal κ
(aw = 1), but can be larger or smaller than the values provided in
Petters and Kreidenweis (2007). Note that the value for
NaCl in Petters and Kreidenweis (2007) is too low, instead of 1.12 it should be
around 1.5 (Zieger et al., 2017).
In our study, we assume that chloride is present in the form of NaCl and
apply the κ value as shown in Fig. 2. For MHD, the contribution of
submicron sea salt has been calculated by the data originators after
Ovadnevaite et al. (2011a) to which we assign the same κ value.
Given that the AMS and ACSM do not fully detect sea salt components which
are present in the submicron aerosol (Salter et
al., 2015), this contribution to sea salt mass contributions is likely to
be underestimated at all other stations close to the sea and where chemical
composition data are available (e.g. CES, FIK), except at MHD.
For particulate organics, we use a κ of 0.1, following observations
in a variety of environments (e.g. Dusek et al., 2010; Gunthe et al.,
2009, 2011; Jurányi et al., 2009; Rose et al., 2010, 2011). It should be noted, however, that κorg has been
observed to be higher in other studies, especially when the organic aerosol
becomes more oxygenated ,that is, when chemical aging has taken place
(e.g. Chang et al., 2010; Massoli et al., 2010). At an O : C ratio of 0.2,
i.e. non-oxygenated organic matter, κorg tends to be < 0.10,
while it increases towards 0.25 or higher at a ratio near 1.0
(e.g. Wong et al., 2011). At some forest sites,
significant organic particle mass is produced in situ and the atmospheric
processing during transport might have only a small influence. A previous
study in the Amazon rain forest revealed that the κ value of the bulk
aerosol is only slightly larger than 0.1, when the organic aerosol mass
fraction is close to 1. At the boreal forest site (SMR), however, the
κ value seems to fall in between 0.1 and 0.2 for high organic mass
fractions (Paramonov et al., 2013). It is
conceivable that the in situ contribution to organic aerosol mass from biogenic
emissions are smaller than in the rain forest, and hence that forest
emissions upwind are transported and chemical processes over hours to
days play a larger role in determining κorg. At sites like CES,
which are classified as background sites but located relatively close to
urban agglomerations (20 and 30 km from Rotterdam and Utrecht,
respectively), the observed organic matter might have been sufficiently processed
to become more hygroscopic than what is normally observed in
the urban environment (e.g. Ervens
et al., 2010). For BC we use κ = 0 (e.g.
Hitzenberger et al., 2003; Rose et al., 2011; Tritscher et al., 2011).
With these κ values for individual components, we calculate the bulk
aerosol hygroscopicity with Eq. (2) in five variations:
deriving all chemical components, including salts and acids, using the
ammonium, nitrate, sulfate and chloride ions, and organics from the aerosol
chemical composition data, and no BC (referred to as “κIA+OA-BC”);
only with ion-balanced (IB) inorganic components, which excludes acids and
bisulfates, but with organics, and no BC (“κIB+OA-BC”);
similar to 1 but including BC (“κIA+OA+BC”);
similar to 2 but including BC (“κIB+OA+BC”);
κ = 0.3.
For alternatives 2 and 4, the measured number of sulfate and nitrate ions
was neutralised with a calculated amount of ammonium. We chose to calculate
ammonium because the quantification of ammonium with the aerosol mass
spectrometer is subject to higher uncertainty than for sulfate and nitrate.
Chloride was assumed to be present as sodium chloride. All particulate
sulfate and nitrate were assumed to be inorganic, because most composition
data were obtained from unit mass resolution ACSM measurements, which do not
allow apportioning these ions to organic species. The contribution of
particulate sulfate to ammonium sulfate, ammonium bisulfate and sulfuric
acid were obtained after Eq. (2) in Gysel et al. (2007) when using prediction
alternative 1 and 3. For the stations MEL, MHD and JFJ, BC time series were
available. For stations where no BC time series were available at the point
of data collection, seasonal or yearly average values were taken from the
literature. For ATTO, BC concentrations were obtained from Fig. 30 in
Andreae et al. (2015), for CES from Schlag et al. (2016), for SMR from Hyvärinen
et al. (2011) and for FIK from Bougiatioti et
al. (2014). Results for all κ values are provided in
Table 2. It must be noted that when using bulk
aerosol particle chemical composition data from AMS or ACSM measurements,
the larger particles (all instruments used inlet lenses with an upper
cut-off of 1 µm) will dominate the aerosol mass. Hence, the
composition information is representative of the size range around the peak
of the mass size distribution and might not reflect the composition of the
majority of particles when small particles dominate the number
concentration. This can be the case when new particle formation happens,
e.g. at SMR or MEL (Manninen et al., 2010). In the presence of mostly
accumulation mode particles, however, good agreement between hygroscopic
GF measurements and its derivation from bulk aerosol composition
data has been found for SMR, e.g. Raatikainen et
al. (2010). At JFJ earlier studies deriving κ from hygroscopic tandem
DMAs and the CCNC resulted in κ = 0.20 and 0.24 (Jurányi et
al., 2011 and Kammermann et al., 2010a, respectively), showing that the method
of deriving the particle hygroscopicity can play a role at some locations.
The size of the particles is an even more important determining factor for a
particle's ability to act as CCN than the κ value. Hence, for all
stations where particle number size distribution and chemical composition
data are available, we can predict the number of CCN particles at a given supersaturation (SS).
using the κ–Köhler equation (Eq. 6, Petters
and Kreidenweis, 2007). This equation describes the equilibrium saturation
ratio S (ratio of the partial vapour pressure of water and the saturation
vapour pressure of water) over an aqueous solution droplet:
S=1+κD03Ddrop3-D03-1exp4σsolϑwRTDdrop,
with D0 being the dry particle diameter, Ddrop the droplet diameter,
σsol the surface tension of the solution (we use a surface
tension of water of 72.86 mN m-1 corresponding to 20 ∘C,
which is close to the sample air temperature in the CCNC),
ϑw the partial molar volume of water in the solution (which was assumed to be
the molar volume of pure water), R the universal gas constant and T the
temperature. The first term on the right hand side of the equation is a
semi-empirical formulation of the Raoult term, i.e. for the water activity
aw expressed with dry size, droplet size and κ value.
More details are given elsewhere (e.g. Jurányi et al., 2010; Petters
and Kreidenweis, 2007). The maximum of Eq. (4), with Ddrop being the
independent variable, describes the critical supersaturation for a particle
with given dry size and κ value. Similarly, the critical dry
diameter (Dcrit) for a certain supersaturation and κ value
describes the dry size for which the corresponding critical supersaturation
equals this supersaturation. The critical dry diameter was numerically
derived from Eq. (4) (rather than using simplified and approximate analytical solutions).
Median values (based on all data) for the bulk particle composition-derived
hygroscopicity parameter kappa (κ) at each station with particle chemical
composition measurements. The subscripts to κ indicate which species
were or were not included: “IA + OA - BC” for inorganic aerosol and
organic aerosol mass but no black carbon; “IB + OA - BC” for ion-balanced
inorganic aerosol and organic aerosol mass but no black carbon; “IA + OA + BC”
for inorganic aerosol, organic aerosol mass and black carbon; and “IB + OA + BC”
for ion-balanced inorganic aerosol, organic aerosol mass and black carbon.
Station
κIA+OA-BC
κIB+OA-BC
κIA+OA+BC
κIB+OA+BC
ATT
0.26
0.21
0.25
0.20
CES
0.52
0.50
0.50
0.48
FIK
0.48
0.47
0.46
0.45
JFJ
0.41
0.31
0.39
0.29
MEL
0.43
0.42
0.42
0.42
MHD
0.63
0.63
0.61
0.61
SMR
0.30
0.29
0.27
0.25
Having determined Dcrit at a given SS and assuming equal
composition of all particles with similar size, we can calculate the number
of activated particles by integrating the particle number size distribution
from its maximum diameter (Dmax) down to Dcrit following Eq. (5):
NCCN(SS)=-∫DmaxDcrit(SS)dN(D)dlogDdlogD.
NCCN(SS) can then be compared to the number of CCN at the same SS
measured by the CCNC (i.e. a closure study).
At stations with simultaneous particle number size distribution and
polydisperse CCN measurements, κ can alternatively be derived by
first estimating Dcrit with Eq. (5). This approach is only approximate for
externally mixed aerosols. However, assuming a sharp activation cut-off,
which is a priori incorrect in such cases, results in largely compensating
errors (Kammermann et al., 2010a), thus still providing valid results.
(a) Normalised frequency distributions of CCN number concentration
at SS = 0.2 % and total particle number in light grey, (b) geometric
mean diameter Dg, and (c) critical diameter Dcrit
at SS = 0.2 %. The grey lines in (b) are based on size distributions
starting at 20 nm. The critical diameter is derived from the total CCN concentration
(SS = 0.2 %) and the integrated particle number concentration starting
from the largest diameter (see Sect. 2.2.2 for details). Note that seasons are
not represented by an equal number of data points at each station which can
lead to small biases in the frequency distributions. In (a) and (c)
all axes start at 0.00.
Results and discussion
Frequency distributions, seasonal cycles and persistence
Figure 1b provides an overview of CCN number concentration at SS = 0.2 %
(CCN0.2) per season at each station. Coloured bars indicate the
median while the black bars are a surrogate for seasonal variability
spanning the interquartile range. The observatories are grouped by their
station classification (see coloured shadings). It becomes apparent that
there can be a large variability in CCN0.2 number concentrations within
one station category. Within the coastal background station category, the
median values can be < 100 cm-3 at BRW and higher than 1500 cm-3
at NOT in spring. In the rural background category, in spring the
largest difference is found between MEL with about 1600 cm-3 and VAV
with about 400 cm-3. Reasons are discussed in detail further below.
Seasonal cycles (median and interquartile range) of (a) CCN0.2
number concentration, (b) Dg and (c) Dcrit
at SS = 0.2 %. Note that only particles sizes > 20 nm were taken
into account. The black vertical bars are placed at the same x-axis value in
each panel for each station for better comparability. For SEO, data at SS = 0.2 %
was limited. In order to display the full seasonal cycle, values for SS = 0.4 %
are also shown. Note that the number of overlapping data points at VAV for CCN
number concentration and particle number size distribution in October is < 200,
i.e. < 10 days. No monthly median was derived. Also note, if the interquartile
range seems to be missing, variations are so small that they do not appear
beyond the thick median line.
Figure 3 shows normalised frequency distributions of
CCN0.2, the Dg of the entire particle number size distributions
(limited to sizes > 20 nm) and Dcrit at SS = 0.2 % as
derived from Eq. (5) based on the CCN and particle number size distribution
measurements only. The highest frequency of low CCN0.2 number
concentrations (< 200 cm-3) can be found at the Arctic site
BRW, which is characteristic of the Arctic maritime environment
(Barrie, 1986). Similarly low number concentrations are observed
at the mountain sites PUY and JFJ with almost no contribution of
> 1000 cm-3. This is expected as they represent
continental background conditions as well as the free troposphere, mostly
during winter and night-time, but also occasionally during summer (e.g.
Herrmann et al., 2015; Venzac et al., 2009). Higher concentrations can be
due to boundary layer air mass injections, especially during summer. Note
that a potential influence from touristic activities was removed from the data
sets (e.g. Fröhlich et al., 2015; Venzac et al., 2009). Low number
concentrations are also found at the coastal site MHD (with the highest
occurrence of CCN0.2 densities of 200 cm-3), which for certain
periods reflects the clean marine conditions over the Atlantic Ocean
(Ovadnevaite et al., 2014). The coastal environments of FIK in the
Mediterranean and NOT in the Pacific Ocean exhibit generally higher
concentrations (between 200 and 2000 cm-3) due to particular pollution
influences which ,for example, include long-range transport of NE European
pollution and biomass burning plumes (Bougiatioti
et al., 2016) and long-range transport of East Asian pollution plumes
(Iwamoto et al., 2016), respectively. In terms of CCN number
concentrations, the NOT site is in fact similar to the European rural
background sites MEL and CES, which experience higher concentrations than
the higher latitude continental background site in VAV and the substantially
cleaner boreal forest environment (SMR, both < 1000 cm-3). The
highest concentrations are seen in the urban environment of Seoul (SEO,
1000–5000 cm-3). While CCN0.2 concentrations are generally
mono-modally distributed at all sites, the tropical rain forest observatory (ATT)
and the Arctic station (BRW) exhibit bimodal distributions spanning a
wide range of possible CCN number concentrations between 20–2000 and 20–200 cm-3,
respectively. As seen more clearly in the
seasonal cycle (see Fig. 4), for ATTO this is due to the conditions of the
rainy and dry seasons, as well as forest fires and other long-range
transported air pollution influences (Pöhlker et al., 2016; Whitehead
et al., 2016). At BRW the Arctic haze period leads to higher CCN number
concentrations than observed in the remainder of the year.
Using Dg as a proxy for aerosol size distributions, Fig. 3b shows that
similar particle number size distributions do not always imply similar
frequencies of CCN number concentrations. For example, the two mountain
stations (JFJ, PUY) do not show similar frequency distributions of
CCN0.2 while they do for Dg, because the particle number
concentration at PUY is higher and therefore more particles activate. BRW
and MHD, while similar in their CCN0.2 frequency distribution, exhibit
significantly different particle geometric mean diameters: mostly
> 100 nm at BRW and mostly < 100 nm at MHD. The Nordic
country stations (SMR, VAV) present similar particle number size
distributions. This is true for the particle number size distributions with
and without particles < 20 nm considered. The difference in results
of Dg when excluding particles < 20 nm is due to frequent new
particle formation events at these locations (Manninen et al., 2010). The
largest particles are observed in the most remote places, the Arctic (BRW)
and the rain forest station (ATT) with high frequencies of Dg > 100 nm.
The critical diameters at SS = 0.2 %, being an indication for the
particle hygroscopicity, as shown in Fig. 3c, provide yet another
perspective on the diverse aerosol populations. Differences in aerosol
sources might not necessarily be visible in the size distributions, whereas
they can show up in terms of hygroscopicity. At a constant SS, a smaller
Dcrit is expected for more hygroscopic particles such as sea salt.
This is reflected by the Dcrit distributions at MHD and BRW. The
distributions are bimodal with high Dcrit occurrences of greater and
smaller than 100 nm, suggesting that the smaller mode is associated with sea salt
and the other CCN active marine aerosols in the case of MHD
(Ovadnevaite et al., 2011b) and the generally highly
hygroscopic Arctic background aerosol in BRW (Lathem et al., 2013). The second, less
hygroscopic mode can be associated with a variety of other aerosol sources
such as particles transported from inland sources which include peat
combustion, traffic and industrial emission sources (Ovadnevaite et al.,
2011b; Taylor et al., 2016) for MHD, or industrial or biomass burning
pollution plumes in the Arctic (Lathem et
al., 2013). In the Mediterranean environment the distribution is not
bimodal, although it exhibits a small plateau for slightly more hygroscopic
particles around 100 nm, while the majority of particles are on average less
hygroscopic (high Dcrit occurrence at 180 nm) than in the other coastal
areas. This might be due to European pollution outflow and biomass burning
plumes (Bougiatioti et al., 2016). At NOT – despite
the influence of two distinct sources, marine aerosol and long-range
transported anthropogenic pollution (Iwamoto et al., 2016) –
only a mono-modal distribution of Dcrit is found (peak at 90 nm). This
is likely due to the dominant wind direction from the west. Particles from
different sources are hence continuously mixed and low-volatility gaseous
components condense on all types of particles, which results in a mono-modal
size distribution. This is different from MHD and BRW where different wind
directions advect aerosol from different sources. At most other locations,
the distributions of Dcrit are relatively narrow and centred around or
are slightly larger than 100 nm for SS = 0.2 %, except for JFJ. Here, a
second mode around 150 nm is also found, most likely originating from
boundary layer air mass injections in summer, as the seasonal cycle
of Dcrit suggests in Fig. 4c. Investigation of diurnal cycles clearly shows
that aerosol hygroscopicity decreases with boundary layer air mass
injections due to changes in aerosol chemical composition (Jurányi et
al., 2011; Kammermann et al., 2010a). Note that the second mode is likely
over-weighted in Fig. 3c because there are more summers than winter seasons
in the data set. In Fig. 4 monthly data were averaged and are hence equally weighted.
The seasonal cycles of CCN0.2 number concentration, Dg, and
Dcrit show characteristic differences between the locations (Fig. 4). As
mentioned above, boundary layer air masses are uplifted in summer at JFJ,
which is evident from the enhanced CCN number concentration, a median of
240 cm-3 compared to about 20 cm-3 in winter (compare
also with Jurányi et al., 2011) and the total particle number
concentration (see Sect. S3 for all stations). At the same time, particles
are larger (Dg about 75 nm in summer versus 50 nm in winter; Fig. 4b),
but less hygroscopic (Dcrit > 100 nm versus < 100 nm;
Fig. 4c). A similar seasonal cycle exists at PUY, although less pronounced,
likely due to its lower elevation. Both forest environments also show
seasonal cycles. In the boreal forest (SMR), CCN0.2 number
concentrations in spring and autumn are lower (200 cm-3) than in summer
(430 cm-3) and also in winter even though the total particle number
concentration is lower in winter than in the transition seasons (see Sect. S3).
The low CCN0.2 number concentrations in spring and autumn coincide
with smaller particle sizes. In spring and autumn, new particle formation
events contribute substantially to the particle number concentration
(Dal Maso et al., 2005). Those newly formed particles stay smaller than
during summer because there are less VOC oxidation products available that
would condense on the particles. However, these particles still have a
rather high organic mass fraction, which makes them less hygroscopic. Thus,
the CCN0.2 and particle number concentrations are smaller in spring and
autumn compared to the summer (Paramonov et al., 2013; Petäjä et
al., 2005). Note that while we refer to CCN at a supersaturation of
0.2 %, small particles could contribute to the CCN number concentration at
higher supersaturations in which case the lower concentrations in spring and
autumn might not be as apparent. During summer, particles are larger on
average with a Dg of 70 nm, but have a similar hygroscopicity
(Dcrit around 110 nm) to the spring and autumn particles (Dcrit around
100 nm) owing to the larger fraction of organic aerosol components (compare
Fig. 7). Nevertheless, more CCN0.2 can be observed due to an increase
in the overall particle number concentration likely owing to high pressure
periods in which air masses from the south arrive carrying aged
anthropogenic and biogenic particles. In the rain forest (ATT),
concentrations are low during the rainy season (< 500 cm-3)
early in the year when natural aerosol sources dominate (China et al.,
2016; Pöhlker et al., 2012; Wang et al., 2016) and higher during the dry
season (> 500 cm-3) as a result of in-basin transport of
emissions from deforestation fires (Pöhlker et al., 2016). In the
rainy season, the biogenic (natural) particles are also smaller (Dg of
90 nm versus 130 nm in the dry season) and seem to be more hygroscopic, with
a Dcrit of about 100 nm. Seoul (SEO) is subject to monsoon influence in
summer (June through September). However, in the urban environment the
impact of the rainy season is not clearly visible, neither in the
CCN0.2 number concentration nor in the average particle size. This is
likely due to the continuous emission of particles from urban sources. The
variations of Dcrit, < 100 nm in winter and > 100 nm
in summer, seem to suggest that aerosol particles are more hygroscopic in
winter than in summer, potentially due to changes in emission sources. At
BRW, the influence of Arctic haze (Barrie, 1986) is evident from roughly a factor of 5
higher CCN0.2 number concentrations in late
winter and spring with concentrations around 100 cm-3. Also at FIK, the
seasonal cycle is characterized by pollution events occurring in summer
(CCN0.2 > 500 cm-3), which are associated with
long-range transport of biomass burning aerosol containing larger size
particles and the absence of precipitation (Bougiatioti
et al., 2016). The coastal sites at the Atlantic (MHD) and Pacific (NOT)
show relatively large variability in all measured parameters without
exhibiting a distinct seasonal cycle. At MHD particles tend to be smaller in
summer (Dg around 70 nm). In summer, sea salt contributes less to the
MHD aerosol particle population, which results in a smaller Dg. More sea
spray in winter, because of higher wind speeds and wave breaking, explains
the smaller Dcrit (70 nm versus 80 nm in summer) in that season
(Yoon et al., 2007). At NOT, CCN0.2 number
concentrations seem to be lower in winter (< 1000 cm-3)
compared to other seasons (> 1000 cm-3). This might be
related to convection, cloud and precipitation formation, and hence wet
particle removal, induced by the interplay of the cold winter monsoon and
the warm currents at the ocean surface. The rural and continental background
stations in Europe all show relatively flat seasonal cycles.
While the seasonal cycles inform how aerosol particle properties change over
longer timescales, i.e. months, auto-correlation of the hourly CCN0.2
number concentration time series can provide insights into the variability
over shorter (synoptic) timescales, i.e. days. Figure 5 shows the
persistence of CCN0.2 number concentrations for winter (DJF) and summer (JJA).
The persistence is essentially a metric for how long the pattern of
CCN number concentrations “remains similar” (see Sect. 2.3.1). This does not
exclude periodic variations on shorter timescales,
such as diurnal cycle or simply an unvaried number concentration cycle, but
the observed persistence as long as the amplitude of the periodic variations
and the averages over the cycles remain similar. At MEL, CES and SMR, for
example, the winter persistence is larger than 5 days, which is most
likely related to the relatively stable weather patterns in winter when
atmospheric blocking situations occur, which are anti-cyclonic,
quasi-stationary high-pressure systems persisting for several days up to
weeks that disturb the otherwise predominant westerly flow (Sillmann
and Croci-Maspoli, 2009). Conversely, in summer, persistence is only 2 days
for MEL and CES likely reflecting a combination of the much more
variable weather conditions and genuine changes in aerosol particle
characteristics due to short- and medium-range transport, as well as
intermittent new particle formation events (Manninen et al., 2010). Also,
the amplitude of the boundary layer height between night and day is quite
large introducing differences in particle concentrations due to dilution
effects. At the mountain stations, the persistence is longer in summer. It
is driven by the regularity of the boundary layer injections and the
resulting high particle number concentrations (Herrmann et al.,
2015). It has to be noted that, in this case, the high persistence is an
indication of a regular pattern rather than a constant CCN0.2 number
concentration. In the rain forest, the rainy season is characterized by a
longer persistence (7.5 days) than the dry season (2 days) potentially owing
to the regular rain events, i.e. similar to the boundary layer injections
at the mountain stations. FIK shows higher persistence during summer (5 days)
than winter (< 3 days), while the opposite is the case for all
other coastal stations. At FIK weather patterns are stable in summer and air
masses originate from the N-NE sector for more than 80 % of the time
(Kouvarakis et al., 2000). For VAV the longer persistence in
summer (4.5 versus 2 days) as represented in this data set might reflect a
peculiarity of the particular observation period. Generally, similar to SMR,
CES and MEL, more stable conditions in winter are expected. The long
persistence in winter at BRW (5.7 days) reflects the stable Arctic
atmosphere which gets perturbed during spring and summer, when the Arctic
haze conditions fade. Note, since there was not enough data coverage for BRW
in the summer months, springtime (M, A) is shown. Persistence is low in SEO
(1.2 days) and there is virtually no difference between seasons, likely due
to the station's vicinity to emission sources that drive variability rather
than synoptic patterns.
Persistence of CCN number concentrations at SS = 0.2 % in days
for winter (DJF) and summer (JJA). Note that for BRW there were not sufficient data
during summer, so spring values are shown, and since ATTO is located in the
tropics, wet and dry seasons are different as indicated.
Activation
To compare the activation behaviour of particles at all sites, we calculated
the activation ratio (AR) for each measured SS based on the particle number
size distribution > 20 nm. Further, to explore how the AR changes
with SS, we form the ratio of AR at each SS (ARx) to AR at SS = 0.5 %
(AR0.5). If CCN number concentrations were not measured at SS = 0.5 %
the value was linearly interpolated. Results are shown in Fig. 6.
Figure 6a shows all non-coastal sites, and Fig. 6b the coastal sites. The
dashed black line represents a logarithmic fit through all curves following Eq. (6):
ARxAR0.5=A⋅ln(SS)+b,
with A = 0.46 ± 0.02 and b = 1.31 ± 0.02. A steep slope means
that the aerosol particle population activation is sensitive to small
changes in the SS, while a flat slope indicates that a further increase in
SS would not have a large impact on the AR. The curves in Fig. 6a suggest
that particles at all non-coastal sites, except for the rain forest
location, have comparable activation properties with changing SS. This
reflects the results shown in Fig. 3. These sites
have similar ranges for the critical and geometric mean diameters. When
fitting the average of the non-coastal curves, A would be 0.54 ± 0.01
and b = 1.41 ± 0.01. Particles observed in the rain forest follow the
general non-coastal curve up to SS = 0.5 %. Thereafter, the curve
flattens, meaning that the aerosol particle population is rather insensitive
to higher SS and that most particles activate at SS ≤ 0.5 %. The
frequency distribution of Dg at ATTO (Fig. 3b) suggests that most
particles are larger than 100 nm which will already activate at
supersaturations lower than SS = 0.5 %. Regarding the lower activation
ratio at higher SS, Pöhlker et al. (2016) link it to the influence of
nearby biomass burning emissions and hence smaller less hygroscopic
particles. Also, previous studies (e.g. Gunthe et al., 2009) confirmed
this finding by showing that particles with an electrical mobility
diameter < 90 nm are less hygroscopic than larger particles, owing to the
difference in composition. The mass fraction of inorganic constituents is
higher in larger particles.
Ratio of activation ratios for all measured SS (%) over the activation
ratio at 0.5 % SS for each station. At SS = 0.5 % (x-axis) the ratio
is 1. Activation ratios are based on particle size distributions starting at
20 nm. (a) Shows non-coastal sites, while (b) groups all
coastal sites. The black dotted line is the average fit through all curves
from (a) and (b), whereby y = A ⋅ ln(SS %) + b
with A = 0.46 ± 0.02 and b = 1.31 ± 0.02.
The curves for the coastal sites exhibit more spread at both low and high
SS (compare also Fig. 3). In the Arctic (BRW), for example, the curve
suggests that most particles activate already at SS ≤ 0.3 %, which
is in line with the measured large particles sizes and the observation that
Arctic background aerosol particles are generally highly hygroscopic
(Lathem et al., 2013). A similar
observation is true for the Mediterranean environment. The observed
activation behaviour at MHD follows the average from all curves (dashed line)
while particles at NOT are still sensitive to higher SS, similar to the
“land-based” observations. This is most likely due to the influence from
long-range transported anthropogenic air pollution at the site.
Overall it seems that at the coastal sites, the mixing between anthropogenic
and natural (marine) sources leads to a complex behaviour of particle
activation. Conversely, at continental sites the natural (biogenic) sources
lead to size-distributions and hygroscopic characteristics that are
comparable to the anthropogenic ones. For instance, new particle formation events supply
ultrafine particles in place of combustion particles. As a consequence, very
different places like JFJ, SMR, CES, MEL and SEO show similar geometric mean
diameters and hence similar particle activation curves. For further details
regarding the seasonal cycles of AR we refer the reader to Sect. S3.
(a) Monthly median chemical composition as measured by each
station's mass spectrometer (see Table 1 for details on the type of spectrometer).
The horizontal dashed line is placed at 1 µg m-3 for easy
comparison of mass concentrations between stations. (b) Median (black
line) and interquartile range of composition-derived κ values per month.
The dashed black line is located at κ = 0.3. Note, we do not show
monthly BC concentrations where available here, because the displayed
κ values are based only on the mass spectrometric data.
Binned averages and standard deviations of inorganic and organic
particle mass concentrations versus CCN0.2. The mass concentrations are
averaged over bins of 50 particles cm-3. Green and grey lines are linear
fits through the points with all parameters given in each panel. The table
provides the linear regression data: R stands for correlation coefficient,
s for slope and i for intercept.
Aerosol chemical composition and the composition-derived hygroscopicity parameter κ
At seven stations, the aerosol particle chemical composition was measured by
means of different types of aerosol mass spectrometers (see
Table 1 for details). Figure 7 shows the seasonal
cycle of inorganic and organic median mass concentrations on the left, and
the evolution of κ on the right throughout the year as median value
and interquartile range. At most stations, nitrate plays a minor role except
for the rural background stations CES and MEL, where it especially
contributes during the colder months with up to 40 %. These two stations
are closest to the central European high-NOx region
(Beirle et al., 2004). The mass fraction of organics is
mostly below 50 % at the two sites, and the hygroscopicity of the
particles appears to be driven by the inorganic components, predominantly by
ammonium nitrate. The larger the fractional contribution of nitrate (fNO3),
the higher κ becomes: at CES κ ≥ 0.83 × fNO3 + 0.11 and
at MEL κ ≥ 0.82 × fNO3 + 0.12 (not shown). Note that especially for the
European sites, it might be possible that a considerable fraction of nitrate
is present in the form of organic nitrate (Kiendler-Scharr et al., 2016),
which is likely to influence the hygroscopicity. Similarly, particulate
sulfate can be present as organosulfate (Vogel et al.,
2016) in which case particle hygroscopicity would be overestimated. At all
other stations, organics can play a more important role in terms of mass
contribution (up to 80 % at SMR, ATTO and JFJ, and up to 40 % at MHD
and FIK) and determination of the κ value. In the boreal forest,
organics constitute the largest mass fraction throughout the year and
especially during summer. In this season, the boreal forest is actively
growing and producing more VOCs, whose oxidation products either condense on
pre-existing particles or contribute to new particle formation events. Organic matter can
dominate the particle composition, especially in the absence of long-range
transport of other chemical constituents. In the rain forest (ATT), organic
matter also dominates, contributing some 60–70 % to PM1
throughout the year. Therefore, some of the observed hygroscopicity changes
can be associated with differences in organic aerosol composition (i.e. its
oxidation state), rather than differences in inorganic/organic fractions. At
the high alpine site (JFJ) the influence of organic matter (up to 70 %
mass contribution) becomes most important in summer because of boundary
layer air mass uplift, and again the impact on the calculated κ is
evident. At the coastal sites in the Mediterranean (FIK) and Atlantic (MHD),
the non-refractory submicron aerosol particle mass is driven by inorganic
components, predominantly sulfate (mass contribution of up to 50 %).
However, increased organic particle mass is observed during the biomass
burning season at FIK with 40 % mass contribution
(Bougiatioti et al., 2016), when κ reaches a
minimum, and in springtime at MHD (also 40 %), as has been observed
previously (Ovadnevaite et al., 2014). At MHD, κ is generally
> 0.5 owing to the influence of sea salt, but at the same time is
also very variable (0.45 to 0.92 in the monthly median) owing to the mixed
influences of marine organic aerosol and anthropogenic air pollution.
Figure 8 provides a further indication of how the CCN number concentration is
related to the aerosol particle mass and chemical composition. Binned
averages and standard deviations of inorganic (ammonium, nitrate, sulfate,
chloride and sea salt) and organic particle mass are shown against
CCN0.2 number concentrations. Bins represent 50 particles cm-3.
The solid lines are the linear fits through inorganic and organic mass
concentration data with all parameters indicated in the table. Generally,
the correlation between particle mass and CCN0.2 number concentration
is high and similar for organic and inorganic components (R > 0.81
for all cases except for inorganics at SMR where R = 0.66). The
similarity might be an indication for internally mixed particles or the
co-existence of different particle types at the observatories. At CES, the
CCN number concentration is more strongly influenced by the inorganic
aerosol particle mass, as can be concluded from the higher correlation
coefficient compared to the one of CCN0.2 number concentration and
organic particle mass (R = 0.93 versus 0.86). At FIK, the correlation
coefficient with inorganics is only slightly higher (0.97 versus 0.94),
while at MEL, MHD and JFJ they are roughly equal. This relates to the
average over the whole year, while seasonally there can be significant
differences, as Fig. 7 shows. In the forest environments, correlations of
CCN number concentrations with organic particle mass are higher than for
inorganic particle mass (0.94 versus 0.89 at ATTO, and 0.97 versus 0.66 at
SMR). From this perspective, it is clear that knowing the share of organic
particle mass is important for understanding the activation behaviour of the
specific particle population at each site.
A negative relationship of the composition-derived κIA+OA-BC
value and the ratio of organic to inorganic particle mass can be
observed as shown in Fig. 9. Generally, the curve follows a two-component
system that can be described by Eq. (2), with i standing for the inorganic and
organic aerosol components. The figure indicates how well κ can be
described when knowing the organic to inorganic aerosol ratio. The spread in
κ values between locations, especially at lower ratios, is due to the
heterogeneity in the composition of the inorganic particle components. For
example, at CES and MEL ammonium nitrate constitutes a large fraction of the
inorganic aerosol mass, while at ATT and SMR particulate sulfate such as
salt or acid dominates. However, the vertical distance in the lines for ATT
and SMR shows that it makes a significant difference whether sulfate is
present as sulfuric acid (κ = 0.73) or as ammonium sulfate
(κ = 0.6). For SMR, similar observations have been made
investigating the relationship of the organics-to-sulfate ratio to the GFs for
certain particle sizes (Hong et al., 2014).
For higher ratios, κ values from all stations converge when assuming
one single hygroscopicity for OA, i.e. κorg = 0.1, because
κorg starts to dominate the result. Note that the
asymptotic-like approach of the curves towards a certain κ value
cannot be interpreted as κorg > 0.1 for that reason.
Relationship of the composition-derived hygroscopicity parameter,
κ, to the binned and averaged ratio of organic (OA) to inorganic (IA)
aerosol components. The vertical bars denote the standard deviation. Note that
the asymptotic-like approach of the curves towards a κ value higher
than 0.1 cannot be interpreted as κ being larger than 0.1 for these sites,
because κ = 0.1 was used as assumption to derive the κ values
shown on the y-axis. Note that the standard deviation for the lowest OA / IA
ratios at FIK are so small that they do not go beyond the symbol.
Closure study
Achieving closure between measured and predicted CCN number concentrations
has been tried in a large number of studies reflecting conditions in a
variety of environments such as cities, high alpine stations, and boreal,
tropical and mid-latitude forests etc. (e.g. Almeida et al., 2014; Asmi
et al., 2012; Hong et al., 2014; Jurányi et al., 2010; Kammermann et
al., 2010b; Pöhlker et al., 2016; Wu et al., 2013). Most of these
studies, however, rely on relatively short data sets from days to several
weeks at most. Ervens et al. (2010)
present an overview of closure studies from six different sites and an
extensive comparison with other studies discussing the influence of the
particles' mixing state and the hygroscopicity of the organic fraction, as
well as the distance from emission sources. Generally, they find that ratios
of predicted over measured CCN number concentrations can range from 0.2 to 7.9,
with results further away from emission sources becoming more reliable.
This observation has been confirmed, for example, by closure studies at the
high alpine sites, which are relatively far away from emission sources
(Asmi et al., 2012; Jurányi et al., 2010). However, other studies
suggest that poor performance of closure studies near sources can likely be
attributed to difficulties in measuring the relevant aerosol properties with
sufficient resolution in time and at relevant particle sizes, rather than to
intrinsic limitations of the applied κ–Köhler theory
(Jurányi et al., 2013). Ervens et al. (2010)
suggest that organic particle matter can be treated as hygroscopic (they use
κorg = 0.12) a few tens of kilometres downwind from emission
sources. With this κorg value and varied assumptions about
aerosol particle hygroscopicity and state of mixing – that can lead to
similar results due to compensating effects – reasonable closure within a
factor of 2 can be achieved, even though the true nature of the aerosol
particle population is not known. Jurányi et al. (2010)
also show that uncertainties in the bulk κ value can lead to
only a factor of 2 difference between measurement and prediction at low SS
and even less at high SS. Larger discrepancies hence suggest that either the
classical κ–Köhler theory does not hold (e.g. because of the
particles' surface tension, Ovadnevaite et al.,
2017; kinetic limitations; or other reasons) or, which is mostly the case,
that there are issues with the measured data of particle number
concentration, size distribution and CCN number concentrations (see Sect. S2).
Based on these previous results and the fact that all stations with
available chemical composition data are at least 20 km away from large
emission sources, we performed simple closure studies assuming internal
mixtures and a κorg value of 0.1. We focus on the long-term
performance of the instruments that were run in monitoring mode, implying
less attendance than during intensive field campaigns, and the sensitivity
of the results to changes in the following assumptions:
varying the approach to translate composition measurements to κ values
as given in Table 2,
applying a fixed shape of the particle number size distribution (the average
of the entire data set) while keeping the total number concentration of
particles temporally variable as measured and applying κIA+OA-BC and
applying the temporally variable particle number size distribution, but
scaled to the median particle number concentration as measured at each
station with κIA+OA-BC.
This approach is similar to the one shown by
Jurányi et al. (2010) in their Fig. 6, focusing
on a 1 month data set at JFJ. Within this study, however, closure
performance of seven stations over at least one year can be compared.
(a) Results from closure studies for the seven stations with
aerosol chemical composition data. The coefficient of the correlation between
predicted to measured CCN number concentration at SS = 0.5 % is shown in
the vertical axis while the geometric mean of the ratio of predicted and measured
CCN number concentration is given on the horizontal axis. The different marker
symbols represent the stations while the colours indicate details of the closure
study. Kappa values refer to how the hygroscopicity parameter was calculated as
described in Sect. 2.3.2 and as listed in Table 2. “Fixed size” refers to
closure studies where the fixed average shape of all size distributions from the
data set was applied while keeping the temporally variable particle number
concentrations as measured at each station. “Nmedian” means that
closure studies were performed fixing the particle number concentration at
each station to its median value while keeping the temporally variable shape
of the size distribution. (b) Closure results for all stations without
chemical composition data using κ = 0.3 and an average kappa per site
category – VAV: rural background, κ = 0.48; PUY: alpine,
κ = 0.41 (e.g. JFJ); BRW and NOT: coastal background, κ = 0.55;
SEO: urban, κ = 0.1.
Comparison of geometric to arithmetic mean values of the ratios of
predicted and measured CCN0.5 number concentrations based on calculations
with the composition-derived κIA+OA-BC.
Station
Geometric mean
Arithmetic mean
ATT
1.06
0.94
CES
3.10
2.31
FIK
0.87
0.84
JFJ
1.09
0.93
MEL
1.23
1.28
MHD
1.14
1.14
SMR
1.32
1.19
The results are shown in Fig. 10a for SS = 0.5 % with the correlation
coefficient of predicted over measured particle number concentrations on the
vertical axis and the geometric mean of the particle number concentration
ratio on the horizontal axis. We use the geometric instead of the arithmetic
mean, because particle and CCN number concentrations are log-normally
distributed. This can result in slightly different mean values compared to
the arithmetic mean, which has been used more frequently in previous studies
(e.g. Ervens et al., 2010).
Table 3 provides a comparison of both means. The
correlation coefficient is a measure of the agreement between instruments
over time, i.e. the stability of instrumental performance. The ratio of the
predicted and measured CCN0.5 number concentrations indicates the
quality of the average prediction with 1 being a perfect prediction and
numbers < 1 (> 1) being an under- (over-)prediction.
Looking only at closure results with κIA+OA-BC
and κIB+OA-BC, predictions fall within a range of ratios between 0.87
and 1.37, which qualifies as a rather good agreement compared to the findings in
the overview by Ervens et al. (2010), but
reflect a similar range of results as described by Kammermann et al. (2010b)
based on hygroscopicity tandem DMA studies. Values for the correlation coefficient R
fall between 0.87 and 0.98, i.e. the accuracy of predicting temporal
variability is high. This means that for this particular selection of stations,
only the average bulk hygroscopicity of the particles needs to be known
to obtain a realistic estimate of the CCN number concentration. Data for the
CES observatory are located in the area of over-prediction between a factor
of 2.5 and 3.1 due to losses of small particles in the aerosol sampled by
the CCNC (see Sects. 2.2 and S1 for more details). Results are shown
nevertheless for completeness. Including BC concentrations in the
calculation of κ has a limited influence on the overall closure
performance, not enlarging the range of predicted versus measured data. This
means that for long-term observations, neglecting the BC mass concentrations
has only a limited effect at such types of sites. Slight variations in the
chemical composition and, with that, in the aerosol particle hygroscopicity
only play a minor role for the accurate prediction of CCN0.5 number
concentrations that fall within a factor of 2 for this data set. This has
been expressed in a number of previous studies (e.g. Dusek et al., 2006;
Jurányi et al., 2011; Jurányi et al., 2010; Pöhlker et al.,
2016). Even a fixed κ of 0.3 can represent the aerosol particle
hygroscopicity sufficiently well for CCN predictions, with a range of 0.82
to 1.38 for the ratio of predicted over measured CCN0.5 number
concentrations. A κ of 0.3 has been suggested earlier to be generally
representative of polluted continental environments (Andreae
and Rosenfeld, 2008). This also seems to hold for other environments that
partly represent free tropospheric conditions (JFJ) and the Amazon rain
forest conditions in the dry and rainy season including natural forest
emissions and long-range transport of Amazonian and African biomass burning
aerosol pollution, as well as Saharan dust (ATT). Coastal sites (MHD, FIK)
can also be represented by the same κ value. However, this value is
too high for the city in East Asia (SEO).
An influence on the closure results is also observed when the shape of the
particle number size distribution is fixed, but scaled to the measured
particle number concentration at each site (dark blue symbols in Fig. 10a).
The predictability of averaged CCN0.5 number concentrations decreases
moderately for all stations (except CES), and is within the boundaries of
the ratio of 0.80 and 1.96. However, the correlation between the predicted
and measured CCN number concentration naturally decreases as the fixed shape
of the particle number size distribution does not represent the changing
number fraction of particles with diameters larger than Dcrit over time.
The correlation coefficient drops more strongly for the MEL and SMR, which
is due to the regular presence of a large numbers of small particles at
these observatories due to new particle formation events (Birmili and
Wiedensohler, 2000; Dal Maso et al., 2005; Manninen et al., 2010). The
relatively large fraction of small particles can be seen in Fig. 3b
expressed as the Dg frequency. The fixed shape of the particle number
size distribution represents these two stations least accurately.
Keeping the number concentration of particles fixed at each station's median
and scaling the temporally variable particle number size distribution to it,
generally results in the poorest predictability (ratios between 0.65 and 2.28).
The temporal prediction skills drop to correlation coefficients < 0.7
for all stations as the temporal variability in the data set
is mostly driven by changes in particle number concentrations. This is
especially true for MHD, where the correlation coefficient is as low as 0.2,
because the particle concentrations are more variable at this location than
at any other one (see Fig. 4 in Schmale et al., 2017).
Applying these observations to the stations without aerosol chemical
particle composition measurements, we performed closure studies at SS = 0.5 %
based on a calculated average κ value per site category:
rural background, κ = 0.48 from MEL and CES; PUY: alpine,
κ = 0.41 from JFJ; BRW and NOT: coastal background, κ = 0.55
from MHD and FIK. For the urban station, SEO, we use κ = 0.1
(Schmale et al., 2017). In addition, κ = 0.3 is applied to all
stations. Results are shown in Fig. 10b. CCN number concentrations can be
reproduced within 1.02 and 1.99 for the category-averaged κ values
and within 1.03 and 1.75 for κ = 0.3. For NOT the averaged
κ value is representative, likely because of the mixture of the
highly hygroscopic sea salt and sulfur-rich marine accumulation mode
particles with the local aerosol populations. At BRW, the Arctic coastal
environment, particles seem slightly less hygroscopic, leading to better
results with κ = 0.3 rather than 0.55. For SEO, the urban
κ value is also better suited than the suggested global average of 0.3,
while for PUY there is only a small difference between the alpine and
global average κ values. At VAV, the rural background κ value
is too high, leading to a significant over-prediction by a factor of 2. In
the previous estimate at the rural continental site, VAV, by Paramonov et
al. (2015), κ values are around or below 0.3 depending on dry particle
diameter, which are closer to the κ values presented in Table 2 at
the forest station SMR. This is not surprising since the size distributions
at VAV and SMR are similar (Fig. 3) and VAV is also a northern station, and
is surrounded by forest regions similar to SMR. Furthermore, it is possible
that particulate nitrate and sulfate at CES and MEL were associated with
organic matter in which case the hygroscopicity of the particles would be
overestimated even though results in Fig. 10a do not suggest so. Hence, care
must be taken when choosing representative κ values. Two stations in
the same site category could have κ values that are actually
significantly different (compare the forest stations in Fig. 9), and two
stations in two different site categories could have similar κ values.
In general, the correlation coefficients range between 0.70 and 0.93 for
site-category-specific κ values and for an invariant κ value
of 0.3. Given that these κ values do not reflect the temporal
variability of the chemical composition at the stations, the prediction
accuracy is reasonably high.
Other than the varied parameters shown in Fig. 10a, the value of the surface
tension of the solution in the droplet might play a role. Based on JFJ data,
using the closure calculations with κIA+OA-BC, a 30 %
decrease (increase) in σsol would result in a 17 %
under-prediction (over-prediction of 25 %, see Sect. S2) of CCN0.5.
This is within the range of change introduced by fixing the particle number
concentration or size distribution. However, such a large change in σsol
is not likely as a 30 % decrease can happen if very strong
surfactants are present (Petters and Kreidenweis, 2013).
Furthermore, small errors in determining the measured instrument
supersaturation will have very little influence on the ratio of predicted
versus measured CCN number concentrations, i.e. roughly 5 % when
misrepresenting SS by an assumed 10 % (see Sect. S2). Based on this,
determining the particle number concentration and size distribution as
precisely as possible is most important for the successful prediction of CCN
number concentrations at regionally representative observatories in all
regions studied here.
For model simulations, this means that it should be sufficient to represent
the particle number concentration and size distribution correctly and
roughly the chemical composition. However, it remains to be shown whether
this is true for other stations not studied here.
Summary and conclusions
We have analysed long-term data from collocated measurements of CCN number
concentrations, particle number size distributions and, in some cases,
submicron aerosol chemical composition from different regions.
Regional variability
It is evident that CCN number concentrations vary considerably with region.
However, there are only a few long-term studies that have compared number
concentrations from the same station category across different regions.
Previous model studies (Pringle et al., 2009) have
investigated the effect of applying particle number size distribution data
representative of one region to another when attempting to predict the
number of cloud droplets, and found that errors can be as large as 75 %
in the high latitudes and in regions with persistent stratocumuli. Even
though the number of stations is limited to 12, this study comprises
sites from Europe, the Americas and Asia with four stations representing
coastal background, three stations rural background, two alpine sites, two
forest sites and one urban location. Our results (Figs. 1b, 3 and 4) show
that CCN0.2 number concentrations do not only vary considerably by
region but also within one station category, e.g. by up to a factor of 30
in spring among the coastal stations between the Arctic and Asian Pacific,
or by up to a factor of 4 in spring among the rural background stations.
The alpine stations exhibit differences around a factor of 2, while the
two particular forest environments are relatively similar despite
representing high and tropical latitudes. In terms of particle activation
behaviour, Fig. 6 shows that, while most non-coastal stations exhibit similar
characteristics, the Amazon rain forest is different, and there is a
relatively large spread among the coastal stations. This demonstrates that a
broad regional data coverage is necessary to understand the actual
variability of CCN0.2 number concentrations especially for coastal sites.
Seasonal variability
CCN0.2 number concentrations follow a seasonal cycle at most stations
(Figs. 4 and 5). This means that short-term measurements can only be
representative of the season in which they were performed. A comparison with
data from the short-term EUCAARI data set relying on comparable measurement
protocols (Paramonov et al., 2015), covering three of the stations
discussed here for a short duration, shows significant differences in the
CCN0.2 number concentrations. At CES, this study's average
concentration is 4 times higher than the EUCAARI summer 2008 data. In the
Amazon, the winter 2008 average represents only 10 % of the annual
average covered here; and at FIK, the summer through autumn observations in 2007
covering the biomass burning season result in an average concentration
that is twice as high as the full year 2015 measurements. Comparing our data
with EUCAARI data covering one or more years and not overlapping with our
observation period at JFJ, SMR and VAV results in discrepancies no larger
than a factor of 1.3, and for MHD in a factor of 2. This means that the
long-term observations covered in this study are largely representative for
those sites; however, inter-annual variability can still lead to differences
in concentrations. Looking at CCN0.2 number concentration persistence,
all stations, except the urban environment, show marked differences between
winter and summer. This indicates as well that short-term observations
cannot be extrapolated over seasons, an important aspect to keep in mind
when comparing model results with observations.
Prediction of CCN0.5 number concentrations
From the closure studies, we learn that when applying a simple
κ–Köhler formulation assuming internal mixture and size-independent
particle hygroscopicity, the geometric mean ratio between predicted and
measured CCN0.5 number concentrations end up in the range between 0.87
and 1.37. The ratio exhibits a high reproducibility of temporal variability
reflected by statistically significant correlation coefficients between 0.87
and 0.98. This prediction accuracy is rather high compared to previous
synthesis studies that found a range between 0.2 and 7.9
(Ervens et al., 2010), potentially owing
to the relatively remote location of the observatories discussed here and
the apparently high data quality. These results were obtained by using the
ion composition to derive κ for inorganic aerosol constituents, while
κorg was assumed to be 0.1 and no information on BC mass
concentrations was used. Assuming κorg = 0.1 worked
sufficiently well in the present study, as the OA contribution to the
submicron aerosol mass is mostly below 50 %, except at the forest sites,
where it is higher. In the latter case, however, κorg = 0.1
still seems to be a reasonable approximation. Pöhlker et al. (2016)
determined an effective κorg of 0.12 for the Amazon rainforest.
When assuming an overall κ = 0.3, similarly good agreement between
measured and predicted CCN0.5 number concentrations is obtained.
Sensitivity studies show that the temporal variability in CCN0.5 number
concentrations would be poorly represented with an unknown actual particle
number concentration, i.e. the correlation coefficient drops below 0.7 for
all stations and as low as 0.2 for MHD. Also an invariant particle number
size distribution can lead to very low correlation coefficients of < 0.35 for
some stations. This means that temporally resolved data of particle
number concentration and their size distribution are essential to predict
CCN0.5 number concentrations. Conversely, a fixed κ value does
not significantly reduce the correlation coefficients but influences the
CCN0.5 number concentration predicted on average (Fig. 10). Care must
be taken when applying station-type averaged κ values to stations of
the same category without chemical observations. While on average the
prediction accuracy lies within a factor of 1.36, for individual stations
the overestimation can be as large as 200 %, in this case for VAV. VAV
belongs to the rural background site category, which apparently is not
suitable for VAV in terms of predicted CCN0.5 number concentrations
from site-category-specific κ values. Namely, VAV κ values
are more similar to the values at the forest station category.
General implications
The potential CCN number concentration alone cannot determine the actual
CDNC, the variable that is important to
describe cloud radiative properties. Other factors such as the updraft
velocity and the resulting water vapour supersaturation, at which particles
are activated, play an important role (Reutter
et al., 2009). The CCN-limited regime applies to lower CCN number
concentrations of, for example, less than 9000 cm-3 for SS = 0.2 % and a
κ value around 0.4, which is roughly representative of this data set.
This means that all stations considered here would fall into the CCN-limited
regime, except for SEO occasionally. Against this background and given the
results of the closure studies performed here with κIA+OA-BC,
CCN number concentration predictions are within the range of roughly
±30 % for stations with aerosol particle chemical composition information.
Based on Sotiropoulou et al. (2006), who found that errors in CCN
prediction result at most in half the error for CDNC, we find that CDNC can
be predicted within ±15 % from data collected at regionally
representative observatories. Similarly, Moore et
al. (2013) found a CDNC sensitivity of 10–30 % to CCN abundance over
the continents, which would further reduce the uncertainties of CDNC
predictions based on this data set. Considering our results for stations
without particle chemical observations, CCN number concentrations are
overestimated on average by 36 %, leading to CDNC overestimation of
≤ 18 %. However, at individual stations like VAV, the CCN number
concentration is overestimated by a factor of 2 in our closure experiments
which would result in an overestimation of ≤ 50 % of the CDNC. Such
a misrepresentation would result in precipitation underestimation for
locations with shallow cloud formation, as precipitation efficiency in
shallow convection is reduced with increasing CDNC (Andreae and Rosenfeld, 2008; Rosenfeld, 2000).
Recommendations
Given that operating extensive equipment for aerosol particle
characterisation is expensive and labour intensive, it will not be possible
to undertake the same observational efforts as discussed here at many
stations across the globe. However, information of the CCN number
concentration in many locations is important for modelling ACI more accurately
and to constrain their radiative forcing
better. Based on this study, we can recommend that observations of particle
number size distributions at regionally representative sites would be
sufficient when CCN number concentration measurements are run in parallel
for the duration of at least one year. From the collocated observations, a
temporally resolved κ value based on the simple formulation of the
κ–Köhler theory can be derived and applied to the particle
number size distribution to derive the CCN number concentration once the
direct measurements have been concluded. This avoids operational expenses
from sustained operation of a CCNC as well as from instruments capable of
producing highly time-resolved aerosol chemical composition data. This
statement is, however, only applicable to the context of investigating
ACI as discussed here. Chemical composition
measurements are indispensable in other contexts, e.g. when studying air
quality. Furthermore, suggesting to find an alternative to measuring highly
time-resolved particle chemical composition is not to say that such data are
not desirable, especially because they allow for source apportionment
studies that can provide results that are highly valuable to interpret CCN number
concentrations (e.g.r Bougiatioti et al.,
2016). In the ACI context, not using composition-derived κ values
also circumvents added uncertainty from the measured aerosol chemical
component concentrations and the bias towards the mass size distribution
maximum. With respect to monitoring only particle number size distributions
and applying a critical diameter to derive CCN number concentrations, a
study for JFJ confirms that such an approach is reasonable;
Hoyle et al. (2016) showed that 79 % of the
variance in CDNC can be explained by the CCN
number concentration based on a Dcrit of 80 nm. Based on the suggested
simplified measurement strategy together with our observation of high CCN
number concentration variability within site categories, it is conceivable
to operate several “migrating CCNCs” around the world where long-term
particle number size distribution data are already available. These CCNCs
would have to be calibrated regularly at the World Calibration Centre for
Aerosol Physics in Leipzig, Germany, to assure data quality (http://actris-ecac.eu/reports.html).
Last but not least, we encourage the modelling community to make use of this
data set to evaluate CCN results near the observatories and discuss the
simulation skills of the models, and to provide recommendations for priority
observation sites where our simplified measurement recommendation can be employed.