Introduction
About 65 % of the global desert dust emissions stem from North Africa
. Saharan dust influences the Earth's radiative budget
directly through scattering and absorption of solar radiation
. Dust particles may also act as cloud condensation nuclei (CCN) or ice nucleating particles (INPs), thus affecting cloud properties and
contributing to a radiative forcing due to aerosol–cloud interactions
. The uncertainties in quantifying these effects remain
significant. Especially the ice phase has a major impact on cloud properties
by influencing cloud lifetime and precipitation . Furthermore, warm, liquid clouds generally lead to a negative
radiative forcing (cooling effect), whereas cirrus clouds potentially lead to
a positive radiative forcing and thus may warm the climate.
Several laboratory studies during the last 6 decades have indicated the
potent role of mineral dust as INP at temperatures below 263 K
with certain
feldspar minerals having the highest ice nucleating potential amongst the
main mineral dust components . Also in the atmosphere, mineral dust has been observed to
commonly be involved in ice nucleation in mixed-phase and cirrus clouds
. In some
case studies it has been shown that mineral dust is dominating ice nucleation
over Europe even outside of periods of high Saharan dust abundance
. However, climatological estimates of dust INP
concentrations are still missing .
Of the total emitted Saharan dust, 30–50 % is transported westward in the
Saharan Air Layer (SAL), making it the main global dust transport pathway
. The SAL can be identified throughout the
year . It follows a clear seasonal cycle
related to the general circulation pattern. Peak dust emissions in West
Africa are found in summer and are correlated with the northward shift of the
Intertropical Convergence Zone . The shift leads to
increased surface gustiness in West Africa as well as dry convection and
stronger vertical winds which results in an enhanced uplift of dust
particles. The African easterly jet then forces the dry, dust-laden warm air
to move westwards in the SAL at 600–800 hPa above the moist trade wind
inversion . In July and August, a maximum
in number and intensity of dust events is reported for the Izaña
Atmospheric Observatory in Tenerife, which is frequently located within the
SAL as reported by . The authors identified regions in
the subtropical Sahara, a stripe expanding from central Algeria to northern
Mauritania and Western Sahara, as main sources of dust advected to Izaña
during the summer.
Other Saharan dust transport pathways are from the Sahara northwards over
the Mediterranean towards Europe ; toward
the Eastern Mediterranean, Middle East and as
far as East Asia or California ; and
south, towards the Gulf of Guinea .
found that dust particles collected from the soil
surface in the Sahara were hardly mixed with nitrate or sulfate. After being
advected to Cabo Verde dust particles were increasingly internally mixed with
nitrate but not with sulfate. When sampled at a coastal station in Ireland,
the Saharan dust particles showed a very high degree of mixing with nitrate
and sulfate. , in contrast, used scanning electron
microscopy of aerosol samples collected with a cascade impactor at the
Izaña observatory and found that submicron mineral dust was coated with
sulfate. analyzed the bulk chemical composition of
aerosol particles in the SAL collected over 6 years. Their
study showed that desert dust collected at Izaña is often mixed with
nitrate, sulfate and ammonium as well as phosphorous originating from
industrial emissions on the North African coast. Hence, the different
transport pathways lead to different degrees of mixing of the dust aerosol.
thus distinguish between “mineral dust”, describing
only those inorganic mineral particles originating from the soil and “desert
aerosol”, meaning all airborne particulates found in the outflow of the dust
source.
Apart from being mixed with pollutants, the dust may undergo in-cloud or
photo processing. A range of laboratory studies have shown that the ice
nucleation ability of mineral dust particles can be altered by aging
processes. Condensation of sulfuric acid was observed to mostly impair ice nucleation, whereas ammonium
or the exposure to ozone can
promote it. observed that nitric acid promoted ice
nucleation above water saturation but inhibited deposition nucleation below
water saturation. Biological material, which is mixed with the dust particles
already in the soil or gets mixed during the atmospheric transport, may also
affect the ice nucleating behavior of the dust
. Some biological particles, like the
bacterium Pseudomonas syringae, have been observed to lead to ice
nucleation at temperatures warmer than 258 K (see , and
references therein). The importance of these different atmospheric processes
is highlighted by observations of clouds over Florida glaciating at
temperatures above 264 K during the presence of Saharan dust
which is above the ice nucleation onset temperatures found
in laboratory studies for pure mineral dust .
found a weak influence of Saharan dust events (SDEs) on the
immersion mode INP concentrations at 265 K at the Jungfraujoch in the Swiss
Alps but an order of magnitude lower INP concentrations during SDEs at
Izaña, suggesting that atmospheric processes led to enhanced ice
nucleation ability of the Saharan dust after long-range transport at this
temperature.
In light of spreading desertification great interest exists
in better estimating the role of atmospheric desert aerosol for the ice phase
in clouds and thus on the aerosol indirect effect. The objective of this
study is to quantify INP concentrations in freshly emitted dust plumes close
to the Sahara and the role of the composition of the desert aerosol on ice
nucleation. This study was part of the “Cloud Affecting particLes In Mineral
dust from the sAhara” (CALIMA) campaigns which took place at Izaña in
late July and August of 2013 and 2014. In the following, we give an overview
over the two campaigns and describe our methods to measure INPs and aerosol
size distribution and composition. We report INP concentrations at different
temperature and relative humidity conditions. Furthermore, we investigate the
effect of particle size and surface area on INP concentrations in different
air masses as well as the role of fluorescent biological aerosol particles (FBAPs)
and bulk chemical composition for ice nucleation. We discuss how
representative our measurements are considering the technical limitations of
our ice nucleation chamber PINC and compare our results to two common ice
nucleation parameterization schemes from the literature.
Methods
Site description
The two CALIMA campaigns took place from 30 July to 29 August 2013 and from
23 July to 27 August 2014 at the Izaña Atmospheric Observatory
(16∘29′58′′ W, 28∘18′32′′ N), located at
2373 m above sea level (a.s.l.) in Tenerife, Spain. The location usually
remains above the stratocumulus layer typical for the subtropical oceanic
boundary layer and is representative for the free
troposphere during nighttime. During daytime, orographic upward flows
transport water vapor and trace gases from the boundary layer to the location
of the observatory , which may result in new particle
formation . During the summer, the observatory is
frequently located within the SAL, which carries large amounts of dust from
North Africa over the Atlantic Ocean . Further details
about the meteorological characteristics can be found in
, and references therein.
Ice nucleating particle concentration measurements
During both CALIMA campaigns, ice nucleating particle concentrations ([INP])
were measured with the Portable Ice Nucleation Chamber (PINC;
). PINC follows the physical
principal of a continuous flow diffusion chamber (CFDC; ). The aerosol sample is drawn through a chamber between two
ice-coated walls at different subzero temperatures which provide
supersaturated conditions with respect to (wrt) ice. When the onset conditions
of an INP are reached an ice crystal grows on the aerosol particle.
Measurements were carried out at temperatures (T) ranging from
233 to 258 (±0.4) K and relative humidities wrt ice (RHi)
between 100 and 150 (±2) %. Ice nucleation in the deposition regime,
where ice forms directly from the vapor phase, was inferred by conducting
experiments below water saturation. Close to and above water saturation,
condensation freezing, where ice starts forming while water vapor condenses
on an INP, as well as immersion freezing, where the INP is immersed in a
droplet prior to initiating freezing, were investigated. The different
processes cannot be distinguished with our method and thus we refer to
deposition nucleation at RHw<100 % and to condensation
freezing at RHw≥100 %. Measurements in the deposition
(RHw=92 %) and condensation regime (RHw=105 %) were conducted most often at 240 K during the campaign. These
conditions were chosen such that a high enough fraction of the dust particles
should activate as INP to be measurable with PINC, to be able to clearly
distinguish between deposition and condensation/immersion mode, and to compare
to an earlier study on free-tropospheric INP at the Jungfraujoch in the Swiss
Alps conducted under similar conditions. Furthermore, scans of RH at
different temperatures were performed, starting at RHi=100 % up to RHw≥100 %. INP concentrations at
standard temperature and pressure (STP; T=273.15 K, p=1013 hPa) and
PINC T, RHi and RHw data were averaged over 1 min
intervals. INP concentrations are given in standard liters
(std L-1). Before and after each experiment, the sample flow is
drawn through a filter to measure the background INP concentration of the
chamber, which is subtracted from the measured INP concentration during
analysis.
Left panel: standard setup of PINC and WIBS during CALIMA2014.
Right panel: setup for PINC–PCVI–WIBS coupling. Note the different position
of the WIBS (black box). The gray parts are changed between the two different
setups. Adapted from . See text for details.
Due to the low number of INPs (down to below 1 in 106 particles) in the
atmosphere, their statistical counting uncertainties are determined based on
Poisson statistics . The limit of detection
(LOD) equals the error of the background concentration. To lower the LOD of
PINC, an aerodynamic lens concentrator (Enertechnix Inc.; Seattle, USA;
) was installed upstream of PINC. The concentration
factor for INPs was determined as 4.3 ± 2 by routinely comparing [INP]
of periods when the concentrator was off to periods when it was on. A
schematic of the experimental setup is given in Fig. . In the
standard setup an impactor with an aerodynamic D50 cutoff diameter of
0.9 µm (diameter at which 50 % of the particles impact) was
used upstream of PINC to allow a distinction by size between larger ice
crystals which formed in PINC and unactivated aerosol particles and droplets.
In the evaporation section at the lower part of PINC the wall temperatures
are kept both at the warm wall's temperature, maintaining RHi=100 % and RHw<100 %, leading to droplet evaporation
while ice crystals are preserved. Ice crystals, droplets and aerosol
particles in the size range 0.5–25 µm are detected with an optical
particle counter (OPC; Lighthouse REMOTE 5104; Fremont, USA) downstream of
PINC. Particles larger than 3 µm are classified as ice crystals.
Under high RHw conditions, droplets may grow to sizes larger than
3 µm and a differentiation by size is not possible anymore. This
droplet breakthrough occurs at RHw=108 % for our sampling
conditions (T=240 K). The effects of the impactor and the concentrator
on the INP measurements and their representativeness for ambient INP
concentrations are discussed in the results section.
Aerosol particle measurements
Aerosol size distribution
Aerosol particle size distributions and concentrations are monitored
continuously at Izaña within the framework of the Global Atmosphere Watch
program of the World Meteorological Organization .
Number concentrations of particles larger than 0.01 µm are
determined with a condensation particle counter (CPC; TSI, model 3776).
Mobility particle diameter (dm) between 0.01 and 0.44 µm
is measured with a scanning mobility particle sizer (SMPS; TSI; DMA model
3081, CPC model 3010) and the aerodynamic diameter (daer)
between 0.5 and 20 µm with an aerodynamic particle sizer (APS; TSI,
model 3321). The size distributions obtained by the SMPS and APS were merged
and the mobility and aerodynamic diameter were converted to volume equivalent
diameter (dve). For that, the shape factor and particle density
were determined on a daily basis from the known dust concentration and by
optimizing the size distribution overlap. All concentrations are given at STP
conditions, i.e., std cm-3, and in a specific size range x (Nx).
Aerosol size distribution data were not available during the first
2 days
of August 2013.
Mass closure of PMx composition at Izaña during CALIMA2013
and 2014. (a) PMx concentrations determined by gravimetry.
(b) Major PM components, including sulfate as ammonium salt
(a-SO42-), nitrate (NO3-), ammonium (NH4+), organic matter (OM)
and elemental carbon (EC). (c) Selected dust components:
non-ammonium sulfate (na-SO42-), aluminum (Al), potassium (K) and iron
(Fe).
PMT
PM10
PM2.5
PM1
2013
2014
2013
2014
2013
2014
2013
2014
µg std m-3
µg std m-3
µg std m-3
µg std m-3
(a)
PMx
98.8
51.8
88.5
42.0
41.8
25.3
19.5
13.2
(b)
dustx
95.8
48.4
82.0
39.2
29.9
14.7
11.3
3.7
a-SO42-
0.9
0.4
0.9
0.3
0.8
0.3
0.9
0.3
NO3-
1.2
0.8
1.2
0.8
0.2
0.4
0.1
0.1
NH4+
0.3
0.1
0.3
0.1
0.3
0.1
0.3
0.1
OM
1.2
1.5
1.2
2.1
1.2
3.7
1.2
1.5
EC
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
(c)
na-SO42-
2.5
0.7
1.9
0.7
0.9
0.3
0.3
0.3
Al
7.6
3.9
6.8
3.2
2.5
1.2
0.9
0.3
K
1.7
0.8
1.2
0.7
0.6
0.3
0.2
0.1
Fe
4.0
2.0
3.3
1.7
1.2
0.6
0.4
0.1
Bulk chemical composition
Chemical characterization of total particulate matter (PMT) and
particulate matter smaller than 10 µm (PM10), 2.5 µm
(PM2.5) and 1 µm (PM1) aerodynamic diameter,
collectively referred to as PMx hereafter, was performed in samples
collected during the two CALIMA campaigns. To avoid the daytime upward flows
from the boundary layer , these PMx samples were
collected at night (22:00–06:00 UTC), when free-tropospheric airflows
prevail, as part of the long-term aerosol chemical composition program started
in 1987 . A total of 30 and 26 PMT, 31 and
32 PM10, 31 and 30 PM2.5 and 31 and 30 PM1 nocturnal samples
and additionally 12 and 11 PM2.5 daytime (10:00–16:00 UTC) samples
were collected during CALIMA2013 and CALIMA2014, respectively.
Samples were collected on quartz microfiber filters (d=150 mm) using
high-volume (30 m3 h-1) samplers. PMx concentrations were
determined by conditioning the filters at 293 K and 30 % RH, applying
the EN-14907 gravimetric procedure (except for RH set to 30 % instead of
50 %). Chemical characterization included elemental analysis by
inductively coupled plasma atomic emission spectrometry and inductively
coupled plasma mass spectrometry (e.g., Al, Fe, Ca, K, Mg, Na, Ti, V, Ni),
anions by ion chromatography (NO3-, SO42- and Cl-),
ammonium by selective electrode (NH4+) and organic (OC) and elemental
(EC) carbon by the thermo-optical transmittance method (see details on the
program in ).
Chemical characterization was used for a mass closure of PMx (see
Table ). Nitrate occurred mostly in the supermicron fraction,
whereas ammonium was found in the submicron range, indicating that the latter
is associated with sulfate. Concentrations of sulfate vs. ammonium in the
submicron aerosol samples showed a high correlation and linearity (R2=0.89, number of observations nobs=60). The fit line has a
slope of 3.39, much closer to the theoretical ratio of sulfate to ammonium in
ammonium sulfate (= 2.66) than in ammonium bisulfate (= 5.33). Hence,
we split the observed sulfate in two fractions: ammonium sulfate
(a-SO42-) and non-ammonium sulfate (na-SO42-). NO3- and
na-SO42- were assumed to be present as Ca-salts and the remaining Ca
to be present as carbonate. From earlier analysis of dust samples at
Izaña we determined a ratio of Si / Al = 2
and that 40 % of the observed iron is present as oxide
. The dust mass was then calculated as the sum of
Al2O3 + Fe + SiO2 + CaCO3 + Fe2O3 + Ti
+ Sr + P + K + Na + Mg and then normalized such that Al
accounts for 8 % of the dust, i.e., the mean Earth crust value. More
details are provided in . The undetermined fraction of
PM, i.e., the difference between the gravimetrically determined PM and the sum
of the chemical compounds, was significantly higher in PM1 and
PM2.5 than in PM10 and PMT, which has been observed in
earlier studies . It is attributed to water residuals not
fully removed during filter conditioning.
Hourly values of PM2.5 and PM10 concentrations were calculated by
multiplying the aerosol volume concentrations, derived from the APS size
distributions, with experimentally determined volume-to-mass conversion
factors (density equivalent) as described in . PMx
values are given per std m-3. Measurements of the absorption and
scattering coefficients continuously performed at Izaña within the
framework of GAW were used to identify biomass-burning aerosol.
Fluorescent biological aerosol particles
During CALIMA2014, size-resolved FBAP concentration was measured with a Waveband Integrated Bioaerosol
Sensor (WIBS-4; ). The WIBS-4 makes use of the
UV light-induced fluorescence (UV-LIF) method where the auto-fluorescence in
two spectral bands (320–400 and 410–650 nm) of the particles is measured
after subsequent illumination with laser pulses at 280 and 370 nm, resulting
in the three detection channels F1 (excitation at 280 nm and detection in
320–400 nm), F2 (excitation at 280 nm and detection in 410–650 nm) and
F3 (excitation at 370 nm and detection in 410–650 nm). These excitation
and detection wavelengths were chosen such that typical components of
biological particles (e.g., coenzymes such as NADH, proteins or amino acids
such as tryptophan; ) can be detected. In the present
study, we used the simultaneous fluorescence in channels F1 and F3 of WIBS-4
as the criterion for the detection of FBAPs . However, also
non-biological particles such as mineral dust can exhibit simultaneous
fluorescence in these two channels, resulting in a residual fraction of
misclassified FBAPs. Several mineral dust samples thus have been
examined previously in the laboratory to find threshold values for each
detection channel and their combinations to distinguish FBAP from mineral
dust particles. With this method, a small percentage of particles can still
be wrongly classified. The highest cross-sensitivity was found for a pure
feldspar sample (NFBAP/Ntot=1.5 %). Other mineral
dust samples (illite and Arizona test dust) showed a much lower
cross-sensitivity (NFBAP/Ntot≤0.1 %).
Coupling of PINC–PCVI–WIBS
In order to study the fluorescence and thus biological content of INPs
directly, PINC and WIBS were occasionally coupled during CALIMA2014. An
overview of the coupled setup is given in the right panel of
Fig. . Downstream of the PINC OPC, a pumped counterflow
virtual impactor (PCVI, model 8100, Brechtel Manufacturing Inc., USA;
) was installed to solely select ice
crystals while omitting the smaller, unactivated aerosol particles and
droplets. The crystals then were warmed up to room temperature and evaporated
and the remaining residuals were sampled by the WIBS. As the overlapping size
of particles which pass the impactor upstream of PINC
(≤ 0.9 µm) and which are measured with full efficiency by the
WIBS (≥ 0.8 µm) was very restricted, for these periods the
impactor upstream of PINC was replaced by a cyclone (URG-2000-30EG, URG
Corporation, Chapel Hill, NC, USA) with a cutoff diameter of 3.5 µm
at a volumetric flow of 12 L min-1. It was confirmed by tests at
RHi=100 % that no aerosol particles entered which were in
the size range of the ice crystals and could thus be miscounted as INPs.
Before each experiment, the PCVI pump and add flow were adjusted such that
for a period of about 5 min no particles were counted with a condensation
particle counter (TSI, model 3772) behind the PCVI at ice saturation but
instead only at supersaturated conditions wrt ice. Thus, the PCVI cutoff was
set to a size above the largest aerosols and droplets and below the ice
crystal size range. This yielded a pump volume flow of 13.4 L min-1
and an add flow of 2.8 L min-1. A dilution flow of 1.2 L min-1
was added downstream of the PCVI to meet the required 2.5 L min-1 WIBS
flow. A description of the characterization of the PCVI can be found in the
Supplement and in .
Back trajectories
Ten-day air mass back trajectories were calculated with the Lagrangian model
LAGRANTO . ECMWF analysis data were used as input and the
model was run with a resolution of 0.25∘. To best capture
bifurcations, trajectory end points were set to the location and altitude of
the Izaña observatory as well as 0.5∘ north, south, west and east
and ±50 hPa, similar to the method described in .
Data analysis
It has been shown that INPs can differ largely in size, depending on the
environment . In dusty environments as in the present study,
INPs are rather large (see Sect. 3.3), whereas in clean marine air the
majority of INPs might be 0.02≤d≤0.2 µm
. Furthermore, heterogeneous ice nucleation is a
surface-area-dependent process . The number of ice
nucleation active sites per particle surface area, ns,
is a simplified
concept to quantify the several proposed effects which lead to ice nucleation
. It is calculated as
ns(T,RHi)=-ln1-AF(T,RHi)A‾ve≈AF(T,RHi)A‾ve=INP(T,RHi)Atot,
where A‾ve and Atot are the average and
total volume equivalent aerosol surface area, respectively, and AF=[INP] / Ntot the ratio of INP concentration to
total aerosol particle concentration. The approximation is only valid for
AF≤0.1, which was the case throughout the field study. We
calculated ns by integrating the surface area of each size bin,
assuming Ave=πdve2, over the full size
range of the volume equivalent diameter, dve=0.02–20 µm. For calculating ns as well as AF the
particle losses due to the impactor and the particle enrichment due to the
concentrator were accounted for based on laboratory characterization
measurements. As described in the Appendix of , a
size-dependent loss curve of the impactor was measured using montmorillonite
and Arizona test dust. The size-dependent enrichment of the concentrator was
determined using Arizona test dust. These loss and gain terms were multiplied
with the aerosol particle size distributions.
Mean MODIS aerosol optical depth during CALIMA2013 and CALIMA2014.
Results and discussion
The CALIMA2013 and 2014 campaigns: an overview
The two CALIMA campaigns differ in frequency and amount of dust being present
at the observatory. Figure shows the aerosol optical depth
(AOD) over the North Atlantic, averaged for the time period of CALIMA2013 and
CALIMA2014, respectively. Table shows the mean chemical
composition and mass closure of PMT during both campaigns. Mean
PMT was 99 µg std m-3 during CALIMA2013 and
52 µg std m-3 in 2014, consistent with the satellite
observations of the SAL. During CALIMA2013, the SAL was on
average expanded northward over the Canary Islands (Fig. a),
whereas during CALIMA2014 the SAL frequently occurred along a narrow corridor
between 14 and 24∘ N, i.e., south of the Canary Islands
(Fig. b). The dust load at Izaña is correlated with a
northward (high load)–southward (low load) shift of the SAL associated
with the variability of the North African dipole intensity, i.e., the
intensity of the Saharan high compared to the monsoon tropical low
.
In the SAL, PMT is to over 90 % constituted by dust, which is
mixed with low amounts of ammonium sulfate, nitrate and organic matter, each
accounting for 0.1–1 % of PMT (Table a). This is
also true for PM10 (Table b), given that
PMT is mostly constituted by PM10. Under dust-free
conditions, PM10 is very low
(< 3 µg std m-3). Therefore, the hourly PM10
records are a good proxy of hourly bulk dust10, i.e., concentrations of
dust particles smaller than 10 µm. In the smaller size ranges,
mineral dust is also dominant, accounting for 70 and 60 % of PM2.5
and for 60 and 30 % of PM1 during CALIMA2013 and 2014, respectively.
Following , we classified SDEs with
PM10≥100 µg std m-3 as major (mSDE),
with 50 ≤ PM10 ≤ 100 µg std m-3 as
intermediate (iSDE) and with
10 ≤ PM10 ≤ 50 µg std m-3 as minor
dust events.
Figures and show time series of INP and aerosol
concentrations during CALIMA2013 and CALIMA2014, respectively. The first days
of CALIMA2013 were subject to an extreme dust event with PM10 values of
100–700 µg std m-3 (1–3 August, Fig. c,
mSDE1), followed by a second, smaller but still major, dust event of PM10=100–200 µg std m-3 (3–6 August, mSDE2). During the
following weeks, Izaña was within the SAL most of the time, with
PM10 values of 50–100 µg std m-3 (6–13 August,
iSDE1 and iSDE2), 25–50 µg std m-3 (13–19 August) and
100–250 µg std m-3 (19–25 August, mSDE3 and mSDE4).
Dust-free conditions due to North Atlantic air masses prevailed the last days
of the campaign, with PM10 value of
0.1–3 µg std m-3 (25–30 August). During this period,
a biomass-burning event caused by wildfires in North America was
also detected (27–28 August, BB1).
CALIMA2013: [INP] in (a) condensation and
(b) deposition mode at 240 K. (c) PM10,
(d) aerosol particle number concentration as measured by the APS and
(e) as measured by the SMPS. Yellow shading indicates major dust
events (mSDE, PM10 ≥ 100 µg std m-3)
and orange shading intermediate dust events (iSDE,
50 ≤ PM10 ≤ 100 µg std m-3). Minor
dust events (20 ≤ PM10 ≤ 50 µg std m-3)
are not indicated. Green shading indicates a biomass-burning event (BB1).
The first days of the CALIMA2014 campaign (Fig. ) had low
PM10 values of 0.1–2 µg std m-3
(24 July–5 August, Fig. c) during northwesterly incoming flow
from the Atlantic and North America, including a long-range transported
biomass-burning event (25–30 July, BB1). An intermediate dust event
(5–8 August, iSDE1) with PM10≤60 µg std m-3
was followed by prevailing dust-free conditions (8–17 August, PM10=0.1–3 µg std m-3). The end of the campaign experienced
higher dust impact, with three iSDEs (iSDE2: 17–19 August,
50–95 µg std m-3; iSDE3: 21–22 August,
30–70 µg std m-3; iSDE4: 23–24 August,
30–75 µg std m-3) as well as two major dust events
(mSDE1: 19–20 August, 100–280 µg std m-3; mSDE2:
26–27 August, 150–230 µg std m-3). Dust-free conditions
prevailed from 25 to 26 August (PM10=0.1–3 µg std m-3).
As in Fig. but for CALIMA2014.
Size distribution measurements showed that (i) the number of particles with
dve≥0.5 µm (Figs. d and d)
tracks dust events; (ii) during biomass-burning events, the concentration of
particles with 0.5≤dve≤1 µm increased but did
not lead to an elevation of PM10 levels; (iii) the increase in the
height of the planetary boundary layer and new particle formation during daytime is visible by the daily oscillation of the concentration of particles
with dve≤0.5 µm (Fig. e) during
periods of low to no dust (e.g., 9–17 August 2014). At nighttime, the
observatory is located in the free troposphere and particle concentration
decreases but shows (iv) higher values during biomass-burning periods due to
an increase in the free-tropospheric background. Lastly (v), during dust
events the concentration of particles dve≤0.1 µm
is reduced and the daily variation vanishes (Figs. e and
e) as the larger dust particles serve as coagulation sink for them
.
Average [INP] during CALIMA2014, excluding data points below the
limit of detection, including them and setting them to the LOD and including
them and setting them to 0. The last column gives the maximum bias between
columns 3 and 5.
T (K)
RHw (%)
[INP] (std L-1)
[INP] (std L-1)
[INP] (std L-1)
max. bias
> LOD
[INP≤LOD]=!LOD
[INP≤LOD]=!0
233
92
39.9
26.7
26.6
0.3
233
100
192.6
192.6
192.6
0
238
92
3.35
1.74
1.53
0.54
238
102
26.5
24.7
24.7
0.07
240
105
22.6
19.3
19.2
0.15
240
92
1.21
0.72
0.51
0.58
242
92
1.39
0.66
0.46
0.67
242
102
26.5
22.5
22.5
0.15
248
80
0.80
0.40
0.15
0.81
Ice nucleating particle concentrations
The higher frequency and intensity of the dust events during CALIMA2013 in
comparison to CALIMA2014 is reflected in the average INP concentrations. Mean
condensation mode [INP240K,105%RHw] ± σ were
229 ± 468 std L-1 in 2013 and
23 ± 43 std L-1 in 2014 and mean deposition mode
[INP240K,92%RHw] were
1.5 ± 2.3 std L-1 in 2013 and
1.2 ± 1.1 std L-1 in 2014. The time series of [INP] in the
condensation mode at 240 K (Figs. a and a) show that
generally INP concentrations increased during dust events. During the extreme
dust event in 2013 (mSDE1), [INP240K,105%RHw] ≥ 2500 std L-1 were
observed. During the biomass-burning events, however,
[INP240K,105%RHw] stayed below
10 std L-1, which is comparable to those during clean background
conditions when air masses came from over the North Atlantic.
Deposition mode [INP] time series are shown in Figs. b and
b. [INP240K,92%RHw] were in
general lower than those in condensation mode at the same temperature. The
mSDEs led to an increase in [INP240K,92%RHw] to up to 32 std L-1 but the iSDEs
hardly influenced the [INP].
As shown by , measurements of ambient INP concentrations
are significantly biased towards too high values when a large number of data
points fall below the LOD of the INP counter. There is
no standardized method to account for these sub-LOD measurements. In
Table we therefore report the average [INP] during CALIMA2014 at
different T and RH conditions in three ways: (1) excluding all
[INP] ≤ LOD, (2) including [INP] ≤ LOD and setting [INP≤LOD]=LOD and (3) including [INP] ≤ LOD and setting
[INP≤LOD] = 0. The last column contains the maximum
percentage of this theoretical positive bias of the reported INP
concentrations due to this LOD effect. This value is highest (up to 81 %)
for warm T and low RH and becomes 0 at T=233 K and RHw=100 %. For the temperature and RH conditions that we focus on in the
following, [INP240K,92%RHw] and
[INP240K,105%RHw], the maximum bias
due to excluding data below the LOD is 58 and 15 %, respectively.
Currently, there is no commonly used method for these types of observations to
account for sub-LOD data. Thus, to stay comparable to other observations, the
data below detection limit are excluded in the following analysis.
Ice nucleating particle dependency on size
Several earlier studies have shown that the efficiency of INPs of the same
type to nucleate ice increases with the size of the INPs . showed that ambient
[INP] could be parameterized by using the concentration of aerosol particles
with d≥0.5 µm and temperature. This was further supported by
, who observed a better correlation of ambient [INP] in
deposition mode at 240 K with aerosol particles of 0.5≤daer≤0.6 µm (correlation coefficient R2=0.88,
nobs=131) than with particles of 0.3≤daer≤0.5 µm (R2=0.69, nobs=131).
found that a large fraction (40–95 % at 248 K) of
INPs in ground-based measurements were larger than 1 µm in
diameter.
We investigated the correlation of [INP] and aerosol particles of different
sizes during CALIMA2014. Figure shows the resulting R2
values for different size bins and [INP240K,105%RHw] for all periods (nobs=2107) and dust periods only (nobs = 698). Generally, the R2
is higher when only the dust periods are taken into account. Already for
particles of 0.1–0.2 µm the correlation is fairly good (R2=0.5) for the dust periods. With increasing aerosol size, the dust aerosol
dominates the aerosol load more and more and the R2 values converge. For
the dust periods, the R2 stays approximately constant at sizes
≥ 0.3 µm. The 0.1 µm threshold found for the dust-dominated aerosol corresponds to the lower size limit found by
. This does not necessarily imply that the atmospheric
INPs at T=240 K are as small as 0.1 µm, but it highlights
that particles smaller than 0.5 µm also need to be considered
relevant for atmospheric ice nucleation when dust is present.
Correlation of [INP240K,105%RHw] and aerosol concentration (Nx) of
particles of different sizes during CALIMA2014 for dust periods and all
periods together. Also shown is the R2 for [INP240K,92%RHw] with N0.5–1µm.
Comparing the R2 of [INP240K,105%RHw] with N0.5–1µm
(R2 = 0.76, nobs=2358) to that with
N0.5–20µm (R2=0.83, nobs=2358)
shows that the upper size limit of particles entering PINC of 1 µm
only has a minor effect on the correlations with [INP] compared to all
particles of 0.5≤dve≤20 µm.
RHi scans of [INP], AF and ns at 233, 240
and 248 K during CALIMA2014. The dashed vertical lines indicate water
saturation. Event types are the same as in Fig. with the addition
of BG for background conditions, i.e., not affected by Sahara dust or biomass
burning. Error bars are drawn for every third data point.
Figure also shows that [INP240K,92%RHw] correlates only very weakly (R2=0.14,
nobs=1539) with aerosol particles of 0.5≤dve≤1 µm and even less with particles of smaller
sizes. This corresponds to the observation that only the mSDEs led to a
noticeable increase of deposition mode [INP240K,92%RHw].
Ice nucleating particle dependency on surface area
By comparing [INP], AF and ns, number and size-related effects on
ice nucleation can be segregated. Figure shows scans of
RHi at three different constant temperatures ≤ 248 K at
different times during CALIMA2014. At 253 K and RHi≤130 % no [INP] above the detection limit was observed (not shown). The
scans during mSDE1 led to more than a factor of 8 times higher [INP] at 233,
240 and 248 K (Fig. a, b and c) compared to the non-dust
background periods (BG), the biomass-burning period, as well as the other
dust events. This is in part simply due to the high number of particles as
seen in the AF shown in Fig. d–f. The differences between
the mSDE1 and scans during other periods get smaller compared to the
differences in [INP]. At 240 K the scan during mSDE2 shows a comparable high
AF as that during mSDE1. At last, the ns in
Fig. g–i reveals that during dust-dominated periods the
aerosol particles are more ice-active even when the higher concentration and
larger surface area are accounted for. In addition, differences of up to 1
order of magnitude in ns between the different SDEs are found,
which must be related to the composition of the aerosol particles. These
factors will be discussed in the following sections.
Biological aerosol particles as INP
In this and the following section we investigate the dependence of [INP] on
the biological content of single aerosol particles and the bulk chemical
composition.
Indication of an enrichment of FBAPs during SDEs compared to
non-dust periods was determined by WIBS measurements at the Jungfraujoch in
the Swiss Alps . During CALIMA2014, WIBS measurements
were conducted at the Izaña observatory to study how many FBAPs the
desert aerosol already contains close to its emission source and what effect
this has on ice nucleation. Figure shows the time series of
[INP240K,105%RHw] and
[INP240K,92%RHw] during CALIMA2014
together with that of fluorescent particles (NFBAP) and total
aerosol particles (Ntot) of 0.8≤dp≤20 µm as measured by the WIBS as well as the ratio of the latter
two.
The black and green data points in Fig. c are the ambient
Ntot and NFBAP, respectively, measured in
parallel to PINC. It can be seen that during dust events, both ambient
Ntot and ambient NFBAP increased; i.e.,
there were more FBAPs during SDEs than during non-dust times. However, the ratio
of NFBAP / Ntot (black data points in
Fig. d) decreased, showing that the fraction of FBAPs is lower
in the desert aerosol than it is for the non-dust-dominated aerosol.
[INP] during CALIMA2014 in (a) condensation and
(b) deposition mode at 240 K. Purple data points indicate times
when the WIBS was connected downstream of PINC. (c) Total and
fluorescent particle concentration as measured by the WIBS in parallel (black
and green) and in series with PINC (purple and magenta)
(d) Fluorescent to total particle concentration in parallel (black)
and in series (purple) to PINC.
The fluorescent and total INP concentrations measured by the WIBS downstream
of PINC, NFBAP, INP and Ntot, INP (magenta and
purple data points in Fig. c), were much lower than those of
the ambient aerosol because only a few particles act as INP. The higher ratio
of NFBAP,INP / Ntot,INP (purple data points
in Fig. d) compared to the ambient
NFBAP / Ntot right before or after the
PINC–PCVI–WIBS coupled measurements shows that more fluorescent particles
were found in the INPs compared to the ambient aerosol. Up to 25 % of the
INPs measured with the WIBS were FBAPs, also during SDEs. In contrast, a
maximum fraction of 20 % of the ambient aerosol particles were
fluorescent during non-dust periods and ≤ 5 % during dust events.
Correlation of [INP] and ns with Ntot and
NFBAP as measured by the WIBS. nobs gives the
number of observations used for each correlation.
R ([INP240K,105%RHw])
nobs
R ([INP240K,92%RHw])
nobs
Ntot
0.95
59
0.56
32
NFBAP
0.7
56
0.42
31
NFBAP / Ntot
-0.35
56
-0.23
31
R (ns,240K,105%RHw)
nobs
R (ns,240K,92%RHw)
nobs
Ntot
0.65
59
-0.14
32
NFBAP
0.40
56
-0.27
31
NFBAP / Ntot
-0.51
56
-0.49
31
It should be kept in mind that the counting statistics for the WIBS
measurements downstream of PINC were low due to the generally low number of
INPs and the restriction of the PINC–PCVI–WIBS coupling to only three
measurement intervals of a few hours each during CALIMA2014. To study the
relationship of FBAPs and [INP] in more detail, we correlated [INP] to
ambient Ntot and NFBAP measured by WIBS in
parallel. Figure a depicts a very good correlation of
Ntot with [INP240K,105%RHw] (R2=0.91, nobs=59)
and Fig. c a fairly good correlation of NFBAP
with [INP240K,105%RHw] (R2=0.49,
nobs=56). The correlations of deposition mode
[INP240K,92%RHw] with both
Ntot and NFBAP are much weaker (R2=0.31 and
0.18, nobs=32 and 31; see Fig. b and d).
Figure c furthermore shows that there were not enough FBAPs
to explain all observed condensation mode [INP240K,105%RHw], as NFBAP≤70 std L-1; hence there are about a factor of 4 less NFBAP than
INPs. This agrees well with the maximum ratio of 25 % of
NFBAP / Ntot found for INPs
(Fig. d) and is likely also why condensation [INP] at 240 K
weakly anticorrelate with the NFBAP / Ntot
ratio of the ambient aerosol (R2=0.12, nobs=56) in
Fig. e. Even though FBAPs are enriched in the INPs compared
to the ambient aerosol, their concentration is too low to be the dominant INP
type. For the deposition mode [INP], the NFBAP concentration
would be sufficient but the correlations are so weak that a predominant role
of FBAP as INP is unlikely.
Part of the effectiveness of FBAPs to nucleate ice can be due to their often
large size. Furthermore, the desert aerosol FBAPs can also be mineral dust
particles with enough biological material on the surface to fluoresce such
that these particles are classified as FBAPs. To exclude the size effect, we
did the same analysis as above for ns instead of [INP]. The
resulting correlation coefficients for different conditions are given in
Table . The correlation of ns with Ntot
and NFBAP is weaker than that for [INP] showing that a large
portion of the observed [INP] can be explained by the size of the aerosol
particles. However, it also shows that about 16 % (RFBAP,ns2=0.42, nobs=56) of the variation of condensation
ns is related to the concentration of FBAPs.
Correlation of [INP] at 240 K in (a, c, e) condensation
mode and (b, d, f) deposition mode with total and FBAP concentration
and the ratio of FBAPs to total particles as measured by the WIBS in parallel
to PINC. Error bars are the Poisson statistics based uncertainty.
Time series of nighttime measurements during CALIMA2013 and
CALIMA2014. y-axis labels indicate axes of the respective data in each
row.
Aerosol chemistry and ice nucleation
The analysis of the relationship between [INP] and the bulk chemical
composition was done for nighttime measurements only, when the aerosol
chemistry was determined under the prevailing free-tropospheric air masses.
Figure shows time series of nighttime averages of
[INP240K,105%RHw],
[INP240K,92%RHw], dust1 and
dust10 during CALIMA2013 (Fig. a) and CALIMA2014
(Fig. b). In general, the averaged [INP] in the condensation
and deposition mode follow the dust1 and dust10 concentration. The
scatter plots of [INP] vs. dust1 presented in Fig. a
and b depict the fairly good positive correlation (R2=0.44,
nobs=22, for [INP240K,105%RHw] and R2=0.32, nobs=17,
for [INP240K,92%RHw] with dust1).
The samples collected within the SAL (dust10≥10 µg std m-3, red) were segregated from those collected
under dust-free Atlantic air mass conditions (dust10<10 µg std m-3, blue) for further analysis. The
[INP240K,105%RHw] fall within a regime
confined by the two dashed lines,
smin=2.95×103 INP µg-1 and
smax=24.5×103 INP µg-1, which represent
the minimum and maximum concentration of INP per microgram of dust1. We
investigate how the chemical composition of the dust itself and the mixing of
dust with pollutants influences the ratio INP / dust1 between those
limits. We observe more variability in the INP / dust1 ratio for the
[INP240K,105%RHw] than for the
[INP240K,92%RHw], similar to our
findings for the size dependency and FBAPs.
[INP240K,105%RHw] showed a higher
correlation with Al, Fe, Mg and Mn (R: 0.43–0.67, nobs=22)
than with other elements (R: -0.1 to +0.4 for Ca, Na and CO32-,
nobs=22). This is consistent with the idea that feldspar
and some clays (e.g., kaolinite;
) may play a more relevant role as atmospheric
INP than other minerals. The variability in dust composition is illustrated
in Fig. c and d, which show the ratios of K, Mg, Ca and
na-SO42- to Al in the dust samples collected within the SAL. It can be
seen that during the 15 days in 2013 when Izaña was permanently
experiencing dusty conditions (1–15 August 2013, Fig. c), the
ratios varied significantly. This indicates different degrees of mixing
between Mg-, K-, Ca- and na-SO42--containing minerals. For example, a
high ratio of Ca and na-SO42- to Al indicates the presence of evaporite
minerals (e.g., calcite, gypsum or anhydrite) stemming from dry lake beds
. Although certain K-feldspars are considered to be more
efficient INPs than clays , we did not find correlations
between [INP240K,105%RHw] and a
certain dust elemental composition (i.e., ratios to Al). This is likely due to
the similar elemental composition of feldspars and clay minerals which are
dominated by Al and Si and which makes it impossible to identify changes in
their degree of mixing with the method used here.
Figure e and f show concentrations of nitrate and ammonium
sulfate (a-SO42-) in PM1 and PM10 together with [INP]. The
concentrations of these pollutants showed a large variability during the
dusty periods. Figure c shows the ratio
[INP240K,105%RHw] / dust1
vs. a-SO42- to Al, with Al as tracer of clays and feldspars. Out
of 14 submicron dust samples (i.e., 71 %) collected in the SAL under
Saharan influence, 10 follow a linear trend (R2=0.44). These samples are
highlighted by open (2013) and filled (2014) red circles. No trend is found
for deposition mode [INP240K,92%RHw] / dust1 vs.
a-SO42- / Al for the Saharan samples (red circles in
Fig. d).
(a, b) Correlation of [INP] with dust1; (c, d) correlation of [INP] / dust1 with a-SO42- / Al;
(e) correlation of [INP] / N0.5–1µm
with a-SO42- / Al; (f) N0.5–1µm
vs. dust1. Panels (a, c, e) show condensation mode [INP] and
(b, d) deposition mode [INP] at 240 K. All samples were taken
between 22:00 and 06:00 UTC. Blue squares indicate Atlantic air masses, red
circles Sahara influence (open: 2013, filled: 2014) and triangles denote
outliers (see text for details).
A possible explanation for this behavior is the weaker interaction with water
molecules of large singly charged ions, such as NO3- and NH4+
compared to that of small ions with a high ionic charge density, such as
Al3+, Mg2+, Na+ or Ca2+, often referred to as kosmotropes
. The low charge density ions (often referred to as
chaotropes) are weakly hydrated, meaning that they bind weaker with water
molecules than the hydrogen bonds of the water itself. This leads to an
increase in entropy of the water near the ion and makes the water more mobile
compared to pure water and even more so compared to water
close to a kosmotropic ion. We suggest this increase in mobility due to
NH4+ ions at the surface of dust particles allows the water molecules to
rearrange more easily compared to water molecules close to a pure dust
surface. Thus, they can form an ice-like structure more easily as temperature
decreases. This was similarly suggested for K+ (weak chaotrope according
to the definition by ) vs. Ca2+ and Na+
(kosmotropes) by as an explanation of the higher (warmer)
freezing temperature of K-feldspar particles compared to Ca- and
Na-feldspars. NH4+ ions are more weakly hydrated than K+; therefore we
expect this effect to also be the case for ammonium sulfate on K-feldspar
particles. We infer that the a-SO42- exists as coating on the dust
based on the observations by , who observed dust coated by
sulfate for the submicron aerosol particles, a size range where we observe
that sulfate is predominantly available as ammonium sulfate (accounting for
74 % of the total submicron sulfate). In the case of RHw=92 % the particles are not diluted enough for freezing point depression
to be negligible. Thus, an increase in the a-SO42- / Al ratio has a
weak negative effect on [INP240K,92%RHw] / dust1 as observed in our field
measurements (Fig. d).
Another possible explanation for the increase of [INP240K,105%RHw] / dust1 with
a-SO42- / Al is that a-SO42- suggests that the aerosol is
more neutral; hence the acidity of the particles is reduced. As described
earlier, several laboratory studies have observed a decrease in ice
nucleation ability due to condensation of sulfuric acid which alters the dust
surface.
Four outliers to the observed linear trend of [INP240K,105%RHw] / dust1 vs.
a-SO42- / Al were identified. The only point with a distinctly
higher [INP240K,105%RHw] / dust1 than the fit line (red
filled triangle) occurred during the night of 19–20 August 2014, when
15 µg std m-3 of dust1 and the highest average
nighttime [INP240K,105%RHw]
(367 std L-1) of the two CALIMA campaigns were recorded. We
attribute this event to a higher fragmentation of the dust agglomerates
, i.e., a higher dust number to mass ratio than in other
events of similar dust load. On 19–20 August 2014, the mean
N0.5–1µm=11.6 std cm-3 was 1.5 to
2 times that of events with similar dust1 concentration (23 and
25 August 2013 and 27 August 2014: 15–17 µg std m-3,
N0.5–1µm=5.8–7.7 std cm-1). Hence, the
particle concentration N0.5–1µm to dust1 ratio
was about 1.5 to 2 times higher than during similar high dust1 event when
[INP240K,105%RHw] ranged from
55 to 120 std L-1 (see Figs. a, c, d and a, c,
d). The total surface area was also significantly larger on 19 August 2014
(1.8–2.7×10-10 m2 std cm-3) compared to days of similarly high dust1 (0.8–2×10-10 m2 std cm-3). As
shown in Fig. h, the surface area alone could not fully
explain the differences in observed [INP]. This indicates that the degree of
fragmentation of the dust agglomerates influences the
variability of the number of INP. If fragmentation was constant, a linear
relationship between N0.5–1µm and the dust1 mass
would be expected. The scattering of the N0.5–1µm to
dust1 plot (Fig. f) thus illustrates the variability in
the dust agglomerates fragmentation.
The three outliers (red open triangles) that fall below the general trend in
Fig. c (11, 18 and 24 of August 2013) are marked by rather
low dust1 (3, 5 and 9 µg std m-3) and
N0.5–1µm (2.4, 2.7 and 7.0 std cm-3).
However, these are the only three dust events in both CALIMA campaigns when
the a-SO42- / Al ratio was > 1. This suggests that either a
significant fraction of the a-SO42- is externally mixed with the dust
and consequently has a minor influence on the dust ice nucleation properties
or that the higher ratio of a-SO42- / Al exceeds a threshold above
which the a-SO42- reduces the ice nucleation ability of the dust
particles potentially due to a depression of the freezing point in highly
concentrated dust coatings. This is further supported by the deposition mode
data in Fig. d which show a weak decrease of
INP240K,92%RHw / dust1 for
the sample of a-SO42- / Al > 1.
In summary, the number of INPs in the condensation mode at 240 K per
µg dust1 varies within a factor of 7, i.e., from
2.95 × 103 to 24.5 × 103 INP240K,105%RHw µg-1. The linear
relationship between [INP240K,105%RHw] / dust1 and a-SO42- / Al,
which we found in 71 % of the nighttime samples, suggests that mineral
dust particles present in the dust1 composition may have experienced dust
processing, which led to enhanced mobility of the water molecules close to the
dust surface by chaotropic ions and increased the ice nucleation ability of
the dust. This relationship was not observed in the deposition mode
([INP240K,92%RHw]).
Effect of concentrator and impactor on aerosol particle size
distribution (PSD) for different event types during CALIMA2014:
(a) biomass-burning event, (b) intermediate Saharan dust
event 1, (c) background conditions and (d) major dust
event 1. In blue are the ambient PSDs; in red are the corrected PSDs as they are
inside of PINC. Each ambient PSD curve was measured at noon on the respective
day.
Potential sampling bias
A limitation in PINC arises from using an impactor to allow size-based
differentiation of unactivated aerosol and INPs. Figure shows
the aerosol size distributions as measured for the ambient air as well as
calculated for the expected size distribution sampled by PINC, i.e., after the
concentrator and impactor, for different aerosol types dominating the air
masses during CALIMA2014. Especially during the dust events, a significant
fraction of the large particles are not sampled by PINC. As the ice
nucleation ability usually increases with particle size, it is expected that
a substantial fraction of INPs is missed and the ambient INP concentrations
would be underestimated by this method when many large particles are present.
To investigate this for our measurements at 240 K and
105 % RHw, we calculated, based on the measured size
distributions and the characterization curves of the concentrator and the
impactor, how many particles and accordingly which surface area
Aomitted were missed. With the ns value calculated
based on the size distribution in the PINC chamber, and assuming
ns to stay constant for larger particles, we determined the
expected INP concentration which was omitted by our measurement method:
INPomitted=nsAomitted.
A size-independent ns has been found for NX illite by
.
A time series of the measured to total INP
(= INPmeas+INPomitted) ratio during
CALIMA2013 and 2014 is presented in Fig. . The ratio between
measured and total assumed INPs varies a lot between 8 and 99 %. During dust
events the measured INP ratio is generally lower, between 10 and 65 %,
whereas during the CALIMA2014 biomass-burning event basically all INPs were
captured. These findings are in line with a recent study by
,
who found that about 40 % of INPs at 248 K were larger than
1 µm at a location at 2182 m altitude. Of course, our assumptions
have several uncertainties. Apart from the measurement uncertainties, the
assumption that particles above the PINC cutoff have the same ns
as smaller particles might only be true were a significant composition
dependence on size absent in our samples, which we cannot confirm.
(a) Time series in 2013 and (b) in 2014 of the
ratio of measured [INP] to total potential [INP], i.e., the sum of measured
[INP] and the calculated omitted [INP] due to the use of the impactor. Color-coding refers to events described in Fig. and Fig. .
Predictability of INP concentrations close to the Sahara
Ice nucleation is still not understood well enough to be implemented in
global climate models based entirely on theory. As a simplification,
parameterizations are used, based either on classical nucleation theory
, laboratory experiments or ambient observations . Since the current study is the first of its kind so close to the
Sahara, we tested how well two of the ambient observation-based
parameterizations, namely , called D10 and D15
in the following, predict our observations. Both parameterizations are based
on the ice nucleation temperature and the concentration of aerosol particles
larger than d=0.5 µm, N>0.5, and predict [INP] at
105 % RHw. D10 was developed based on data from several
ground-based and airborne studies in North America, Brazil and over the
Pacific and includes aerosol of different type. It takes the following form:
[INPT]=a(273.16-T)b(N>0.5)(c(273.16-T)+d),
with a=0.0000594, b=3.33, c=0.0264 and d=0.0033
.
D15 follows the form of the parameterization by but was
particularly adapted for dust INPs based on data from two flights through
dust-laden air layers over the Pacific and the USA as well as from laboratory
data on dust samples. The laboratory samples as well as the dusty air layers
stemmed from the Sahara and Asian deserts. D15 is given as
[INPT]=(cf)(N>0.5)(α(273.16-T)+β)exp(γ(273.16-T)+δ),
with α=0, β=1.25, γ=0.46 and δ=-11.6. cf
is an instrumental calibration factor specifically derived for the CFDC
. It is set to cf = 1 for the present study.
Figure shows the predicted vs. the observed [INP] at
RHw=105 % for both CALIMA campaigns, color-coded for dust
and biomass-burning-dominated periods. As input for the parameterizations,
N0.5≤dve≤20µm were used. In
Fig. a and c, INP concentrations as they were measured with PINC
are compared to INP concentrations, which were calculated using the ambient
particle size distribution corrected for the effect of impactor losses and
concentrator gains. Figure b and d refer to ambient
concentrations. The [INP] displayed on the x axes are those measured with
PINC and corrected for the omitted INPs, as described in the previous
section. Error bars include the Poisson error of the measured [INP], 10 %
uncertainty of the aerosol particle number concentration and 10 % of the
aerosol particle size measurements, 20 % uncertainty assumed for the
impactor loss curve and a 40 % uncertainty due to the aerosol
concentrator curve. For the predicted [INP] the ambient size distribution of
particles between 0.5≤dve≤20 µm was used
without further corrections. The blue data points are condensation mode [INP]
from an earlier campaign (CLACE2014) on the Jungfraujoch in the Swiss Alps
described in . During this campaign, measurements were
conducted with PINC at T=241 K and RHw=103 % in the
wintertime free troposphere.
Observed [INP] from CALIMA2013 and CALIMA2014 at 105 %
RHw and from CLACE2014 at 103 % vs. predicted [INP] based on
the parameterizations from (a, b) and (c, d) . Green data points refer to biomass-burning events,
orange and red points to intermediate and major dust events, respectively,
and black data points to the remaining time periods. CLACE2014 data are shown
in blue. The 1 : 1 line is given as thick solid line, the dashed lines
indicate a factor of 2 and the thin solid line a factor of 5. The 95 %
confidence interval given by is about a factor of 4. For
the predicted [INP] in (a, c) aerosol particle concentrations
corrected for impactor and concentrator were used. For (b, d), the
omitted potential INPs were included in the observed [INP] and ambient
N0.5–20µm were used for the predicted [INP]. Error
bars are drawn for 100 random data points per plot. They include the
uncertainties of the [INP], ns and aerosol size distribution
measurements.
For the CALIMA campaigns, D10 (Fig. a) has a median ratio of
[INPpred] / [INPmeas] of 0.98 and predicts
50 % of the observed [INP] within a factor of 5 and 60 % within an
order of magnitude. Only during the biomass-burning events are [INP] clearly
overpredicted by about 2 orders of magnitude. D15 (Fig. c)
generally overpredicts [INP] by a median factor of 17. Only 5 and 15 % of
the [INP] predicted by D15 fall within a factor of 5 and 10, respectively, of
the observed [INP]. D15 works best for the major dust events and worst for
the biomass-burning events.
The Jungfraujoch data, shown in blue, are better predicted by both
parameterizations. Of the predicted [INP] based on D10, 60 % fall within a
factor of 5 of the observed [INP]. For D15 this ratio lies at 81 and 50 %
even fall within a factor of 2. Both parameterizations agree significantly
better with the observations at the Jungfraujoch than at Izaña. This,
together with the fact that the field data in D10 and D15 were measured far
away from the Sahara, but influenced by Asian and Saharan dust, suggests that
the ice nucleation properties of the dust change between a location close to
the Sahara and one with a much longer atmospheric transport time. Especially
comparing our results to D15, which was derived particularly for dust INPs,
suggests that dust particles measured close to the Sahara are less efficient
than those which have been transported longer and experienced more
atmospheric and cloud processing, such as dust arriving at the Jungfraujoch.
However, the free-tropospheric Jungfraujoch [INP] were mostly below
10 std L-1; thus a comparison at higher aerosol particle and INP
concentration is not possible. Similar measurements during SDEs with a high dust load at the Jungfraujoch would therefore yield
valuable insight into the role of atmospheric aging on ice nucleation.
As we have shown in the last section, the ratio of INPs which are omitted by
our measurement technique can vary greatly. We thus did the same comparison
for our data, including the omitted INPs. In the case of D10
(Fig. b) this hardly has an effect on the parameterization
statistics: 48 % of the measured [INP] from CALIMA are predicted within a
factor of 5 and the median ratio of predicted / observed [INP] is 0.7. The
biomass-burning event is again strongly overpredicted. For the Jungfraujoch
data, 77 % are predicted within a factor of 5. In Fig. d the
median overprediction by D15 for the CALIMA data is a factor of 10, and
13 % of the predicted [INP] are less than a factor of 5 different from
the observed [INP]. For the Jungfraujoch data, the median ratio of
predicted/observed [INP] is 1.1. Hence, we conclude that the method of
accounting for potentially omitted INPs does not significantly alter the
results from the comparison to the D10 and D15 parameterizations.