AUGUST 2010
HOOSE ET AL.
2483
A Classical-Theory-Based Parameterization of Heterogeneous Ice Nucleation
by Mineral Dust, Soot, and Biological Particles in a Global Climate Model
CORINNA HOOSE* AND JÓN EGILL KRISTJÁNSSON
Department of Geosciences, University of Oslo, Oslo, Norway
JEN-PING CHEN AND ANUPAM HAZRA1
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
(Manuscript received 24 December 2009, in final form 8 March 2010)
ABSTRACT
An ice nucleation parameterization based on classical nucleation theory, with aerosol-specific parameters
derived from experiments, has been implemented into a global climate model—the Community Atmosphere
Model (CAM)-Oslo. The parameterization treats immersion, contact, and deposition nucleation by mineral
dust, soot, bacteria, fungal spores, and pollen in mixed-phase clouds at temperatures between 08 and 2388C.
Immersion freezing is considered for insoluble particles that are activated to cloud droplets, and deposition
and contact nucleation are only allowed for uncoated, unactivated aerosols. Immersion freezing by mineral
dust is found to be the dominant ice formation process, followed by immersion and contact freezing by soot.
The simulated biological aerosol contribution to global atmospheric ice formation is marginal, even with high
estimates of their ice nucleation activity, because the number concentration of ice nucleation active biological
particles in the atmosphere is low compared to other ice nucleating aerosols. Because of the dominance of
mineral dust, the simulated ice nuclei concentrations at temperatures below 2208C are found to correlate with
coarse-mode aerosol particle concentrations. The ice nuclei (IN) concentrations in the model agree well
overall with in situ continuous flow diffusion chamber measurements. At individual locations, the model
exhibits a stronger temperature dependence on IN concentrations than what is observed. The simulated IN
composition (77% mineral dust, 23% soot, and 1025% biological particles) lies in the range of observed ice
nuclei and ice crystal residue compositions.
1. Introduction
Ice in tropospheric clouds is important for cloud radiative properties and precipitation formation, but its
formation is neither theoretically fully understood nor
empirically well constrained (Cantrell and Heymsfield
2005). At temperatures between 08 and 2388C, aerosol
particles are required as ice nuclei (IN) to initiate either
freezing of supercooled cloud droplets or ice nucleation
* Current affiliation: Institute for Meteorology and Climate Research (IMK-AAF), Karlsruhe Institute of Technology, Karlsruhe,
Germany.
1
Current affiliation: Indian Institute of Tropical Meteorology,
Pune, India.
Corresponding author address: Corinna Hoose, Department of
Geosciences, University of Oslo, P.O. Box 1022, Blindern, 0315
Oslo, Norway.
E-mail: corinna.hoose@kit.edu
DOI: 10.1175/2010JAS3425.1
Ó 2010 American Meteorological Society
from the vapor phase. Various insoluble particles such
as mineral dust, soot, metallic particles, volcanic ash, or
primary biological particles can act as IN (Pruppacher
and Klett 1997; Szyrmer and Zawadzki 1997). IN concentrations are usually low (0.01–100 L21) compared to
total aerosol concentrations.
The dependence of heterogeneous ice nucleation on
temperature, particle composition, size, coating, and various other parameters has been the subject of numerous
laboratory experiments (e.g., Schaller and Fukuta 1979;
Levin and Yankofsky 1983; Knopf and Koop 2006;
Bundke et al. 2008; Durant et al. 2008; Welti et al. 2009).
In general, it is found that some bacteria and the artificial IN silver iodide nucleate ice at the warmest temperatures, followed by other biological particles and mineral
dust; combustion particles are relatively inefficient IN.
Atmospheric in situ observation of ice nucleation and
the involved particles is very difficult. One possibility is
to examine the ice nucleation properties of particles
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JOURNAL OF THE ATMOSPHERIC SCIENCES
at cloud altitude under controlled conditions in an
aircraft-borne continuous flow diffusion chamber and to
relate the IN counts to the ambient particle properties
(DeMott et al. 2003b). Alternatively, sampling of cloud
ice crystals and investigation of the residual aerosol
particles after evaporation can give information on the
IN composition (Cziczo et al. 2004; Targino et al. 2006;
Cozic et al. 2008; Cziczo et al. 2009b; Pratt et al. 2009).
As a combination of both methods, the composition of
the subset of ambient aerosol particles that formed ice
in a continuous flow diffusion chamber has been characterized in a number of studies (DeMott et al. 2003a;
Richardson et al. 2007; Prenni et al. 2009a,b). The compilation of a large number of such data by Phillips et al.
(2008) suggests that mineral dust is the dominant atmospheric IN. Additionally, Phillips et al. (2008) report
a large portion of carbonaceous IN, but their exact
composition (elemental or organic carbon) was not determined. Recently, Prenni et al. (2009b) and Pratt et al.
(2009) observed high percentages of biological IN in the
Amazon basin and in a wave cloud over North America,
respectively.
The variety of different IN types, and their scarcity,
complicates the measurement and simulation of heterogeneous ice nucleation. In addition, heterogeneous
ice nucleation can occur via several different mechanisms, called nucleation modes (Vali 1985). For ‘‘immersion freezing’’ an ice nucleus within a supercooled
cloud droplet initiates the freezing process. The term
‘‘contact freezing’’ commonly refers to a supercooled
droplet colliding with a dry ice nucleus, such that the
freezing process is initiated from the outside. In addition, ‘‘inside out’’ contact freezing has been observed
when the immersed ice nucleus contacts the droplet
surface from the inside (Durant and Shaw 2005; Fornea
et al. 2009). Contact freezing is often observed at higher
temperatures than immersion freezing (e.g., Pitter and
Pruppacher 1973). ‘‘Deposition nucleation’’ refers to the
direct growth of ice from the vapor phase on a dry ice
nucleus. The term ‘‘condensation freezing’’ is used for
the process when a (at least partially insoluble) cloud
condensation nucleus subsequently initiates the freezing. From a mechanistic standpoint, the differentiation
between condensation and immersion freezing is vague,
and in the following only the term ‘‘immersion freezing’’
is used.
Contact and immersion freezing involve liquid droplets and are therefore the most commonly accepted ice
nucleation mechanisms in supercooled liquid clouds.
Meanwhile, the atmospheric relevance of deposition nucleation at temperatures above 2388C is uncertain. Favorable for the occurrence of deposition nucleation is
the availability of uncoated IN (e.g., dust particles) in
VOLUME 67
regions with low temperatures and supersaturation over
ice. Wiacek and Peter (2009) performed trajectory calculations originating near the surface of the Chinese
Taklimakan desert and found that most trajectories
pass through ice-saturated (but water-subsaturated)
regions (where deposition nucleation is the only possible ice formation mechanism) before reaching water
saturation. At this stage, the dust particles have not
undergone cloud processing and are possibly uncoated,
such that deposition nucleation would be relatively
efficient.
However, observations of whether deposition nucleation occurs in mixed-phase conditions are ambiguous.
On the one hand, Ansmann et al. (2009) observed that
tropical altocumulus clouds over Cape Verde, investigated with ground-based lidar, almost always had a liquid cloud top and concluded that deposition nucleation
was unimportant during the initial phase of altocumulus
glaciation. Also in the presence of high dust concentrations in Morocco, cloud temperatures needed to be
lower than approximately 2208C and liquid clouds were
required before ice formed (Ansmann et al. 2008). On
the other hand, lidar observations of a cloud influenced
by boreal forest fire smoke (Sassen and Khvorostyanov
2008) showed ice nucleation prior to liquid cloud formation (i.e., below water saturation) at approximately
2158C.
Theoretical formulations of heterogeneous ice nucleation include the so-called classical nucleation theory
(CNT; e.g., Fletcher 1962), which treats nucleation as a
stochastic process, and the semiempirical singular hypothesis (e.g., Levine 1950), which assigns a defined
spontaneous freezing temperature to every aerosol particle. Parameterizations that are used in large-scale models
are mostly empirical (Lohmann 2002; Lohmann and Diehl
2006; Hoose et al. 2008; Morrison and Gettelman 2008;
Phillips et al. 2008; Storelvmo et al. 2008a). Biological
particles have so far not been considered as IN in global
models.
In this article, an ice nucleation parameterization
based on classical nucleation theory is formulated for
use in a global model. Immersion and contact freezing as
well as deposition nucleation are included. The necessary aerosol-related parameters are derived from laboratory experiments. Mineral dust and soot are considered
as possible ice nuclei, as well as several primary biological particles: bacteria, fungal spores, and pollen.
Section 2 describes the model and the new ice nucleation parameterization. In section 3, the relative importance of the different freezing processes is presented. Ice
nuclei concentrations and composition are compared to
observations. Finally, implications and uncertainties are
discussed in section 4.
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HOOSE ET AL.
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TABLE 1. Simulation descriptions.
Simulation
CTL
CNT
CNT-highbact
CNT-lowdust
CNT-nosootct
CNT-nobio
Description
CAM-Oslo with warm cloud microphysics as in Hoose et al. (2009) and cold cloud microphysics
as in simulation WBF in Storelvmo et al. (2008b). Freezing parameterizations after Lohmann and Diehl (2006).
As in CTL, but with the new freezing parameterizations from classical nucleation theory.
As in CNT, but with a higher fraction of bacteria assumed to be ice-nucleation active.
As in CNT, but with higher wet scavenging of dust.
As in CNT, but without contact freezing by soot.
As in CNT, but without freezing by biological aerosol particles.
2. Model description and treatment of ice
nucleation
a. CAM-Oslo
The aerosol–climate model CAM-Oslo is based on
version 3 of the Community Atmosphere Model (CAM3;
Collins et al. 2006). It has been extended to include a detailed aerosol module (Seland et al. 2008) and a prognostic
double-moment cloud microphysics scheme (Storelvmo
et al. 2006; Hoose et al. 2009). The microphysical scheme
for mixed-phase clouds (Storelvmo et al. 2008a,b) has
now been modified by a new treatment of ice nucleation
in mixed-phase clouds (see below).
The aerosol concentrations, mixing states, and the
fractions activated to cloud droplets are simulated online
in CAM-Oslo and provide the input parameters for the
ice nucleation parameterization. The CAM-Oslo aerosol scheme treats sea salt, mineral dust, sulfate, black
carbon, and organic aerosols in 16 modes and 44 size
bins with process-determined mixing states. Aerosol and
precursor gas emissions are taken from the AeroCom
inventory (Dentener et al. 2006). Compared to the
original scheme by Seland et al. (2008), Hoose et al.
(2009) reduced the in-cloud scavenging ratio for mineral
dust from 1 to 0.1 for a better agreement of background
dust concentrations and cloud droplet numbers over land
with observations. The primary biological particles are
treated as described in Hoose et al. (2010), with emissions based on Burrows et al. (2009) for bacteria, Heald
and Spracklen (2009) for fungal spores, and Jacobson and
Streets (2009) for pollen. These particles are assumed to
be spherical and monodisperse, with diameters of 1 mm
for bacteria (Burrows et al. 2009), 5 mm for fungal spores
(Elbert et al. 2007), and 30 mm for pollen (Jacobson and
Streets 2009).
b. Simulation setup
A control experiment (CTL) with the previously used
freezing parameterization (Lohmann and Diehl 2006),
a simulation (CNT) with the new freezing parameterization as described below, and several sensitivity
experiments have been conducted (Table 1). The sensitivity experiments explore different assumptions about
the ice nucleation active fraction of bacteria and fungal spores, suppression of contact freezing by soot,
and mineral dust scavenging. In the simulation CNThighbact, the ice nucleation active fraction (discussed
below) of bacteria and fungal spores is increased from
1% to 10%, and the parameter fi,max is increased from
0.1% to 1%. This results in a maximum increase of
bacteria and fungal spore ice nuclei by up to a factor of
100. In CNT-lowdust, the scavenging ratio for mineral
dust is raised from 0.1 to 0.5, resulting in lower background dust concentrations. Simulation CNT-nosootct
excludes contact freezing by soot particles, which is
considered the most uncertain freezing process. Finally, in CNT-nobio all biological freezing processes
are set to 0.
All simulations are run in T42 resolution (2.81258 3
2.81258) with 26 vertical levels. The simulations are integrated for 5 yr after 4 months of spinup, for both presentday and preindustrial aerosol emissions (Dentener et al.
2006).
c. Ice nucleation active aerosol particles
This study considers ice nucleation for mineral dust,
soot, and primary biological particles (bacteria, fungal
spores, and pollen). Because particles of these categories
are in reality of varying chemical composition and morphology, representative ice nucleation properties have to
be assigned.
Mineral dust is assumed to have the ice nucleation
properties of montmorillonite/illite (i.e., rather efficient ice nuclei). This has been shown to give similar
results to simulations with mixed-mineralogy particles
depending on the source region (Hoose et al. 2008) because the most efficient ice nucleating dust component
determines the average freezing rate. For soot, not many
suitable experimental data are available. The data selected here are for laboratory-generated soot from
an acetylene burner and a graphite spark generator,
respectively.
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JOURNAL OF THE ATMOSPHERIC SCIENCES
For biological particles, the variability in ice nucleation properties is largest. Only a small fraction of the
atmospheric primary biological particles belong to ice
nucleation active species. Lindemann et al. (1982) found
that the ratio of bacteria that produce colonies active as
IN to the total number of colony-forming units ranged
between 0.04% and 4%, measured over bare soil and
different crops. Maki and Willoughby (1978) identified
2 out of 13 (15%) bacteria strains in snow samples as ice
nucleation active, but 0 out of 5 strains isolated from
rain. Constantinidou et al. (1990) measured a fraction of
5.5% ice nucleation active bacteria strains during a rain
event over a soybean field. A relative abundance on the
order of ,5% for the Pseudomonadaceae family, to
which the Pseudomonas genus with several ice nucleation active species belongs, was found in several air and
snow samples from a high elevation site in Colorado
(Bowers et al. 2009). Based on these observations, we
assume that on global average 1% of all bacteria belong
to ice nucleation active species (see also Phillips et al.
2009), represented by Pseudomonas syringae. These
are called ‘‘Pseudomonas syringae–like’’ in the following. Note that also for Pseudomonas syringae–like bacteria species, only a small fraction of all cells of this
species—not all cells—can nucleate ice (Hirano and
Upper 1995).
Concentration measurements of atmospheric concentrations of ice nucleation active fungi are rarer. Two
(out of 14 investigated) species of the Fusarium genus
have been found to nucleate ice with characteristics
similar to Pseudomonas bacteria (Pouleur et al. 1992).
The whole Fusarium genus again contributed to less
than 3% of the total airborne fungal flora measured on
Finnish farms (Lappalainen et al. 1996). In addition,
some lichen fungi have been identified as ice nucleators
(Kieft 1988; Henderson-Begg et al. 2009). We therefore
assume that, as with the bacteria, 1% of all fungal spores
belong to ice nucleation active (Pseudomonas syringae–
like) species, which probably gives an upper estimate of
the contribution of fungal spores to ice nucleation in the
atmosphere. Ideally, the fraction of ice nucleation active
bacteria and fungi species would be simulated as a
function of climatic zones (Schnell and Vali 1973), but at
present observations are too scarce to take this variation
into account.
A wide variety of pollen species have been found to
nucleate ice (Diehl et al. 2002; von Blohn et al. 2005; Chen
et al. 2008). Von Blohn et al. (2005) concluded that the ice
nucleating ability seems to be a general pollen property.
Therefore we assume that 100% of all pollen have ice
nucleation properties similar to birch pollen (Diehl et al.
2002), which gives a high estimate of the pollen ice
nucleation.
VOLUME 67
d. Ice nucleation parameterizations
The ice nucleation parameterization used in this study
is based on CNT. Similar parameterizations based on
CNT have been applied successfully in models on different scales (Khvorostyanov and Curry 2005; Morrison
et al. 2005; Liu and Penner 2005), but so far the determination of the required aerosol-specific parameters has
been uncertain.
Chen et al. (2008) have presented a method for derivation of these parameters from laboratory experiments, and this method is applied here. Because of
missing information about some experimental parameters (in particular, the number of particles per droplet
for immersion freezing experiments, and the observation time), the conversion of the observed onset or
median freezing temperatures into freezing rates is associated with considerable uncertainty, which is translated into the derived parameters. Similar derivations
(e.g., Marcolli et al. 2007; Eastwood et al. 2008; Fornea
et al. 2009; Welti et al. 2009; Kanji and Abbatt 2010;
Kulkarni and Dobbie 2010; Luond et al. 2010), some
with simplified formulations of classical nucleation
theory, have demonstrated a large spread associated
with the derived parameters, in particular for contact
angles. The parameterization presented here has the
advantage that other experimental results can be easily
incorporated.
In classical theory, the ice nucleation is seen as a stochastic process (Pruppacher and Klett 1997). An energy
barrier has to be passed to add more molecules to small
agglomerates of ice (subcritical germs) on the ice nucleus
surface, until a critical germ size is reached. Following the
notation in Chen et al. (2008), both immersion and deposition nucleation can be expressed in the same general
form. The rate of heterogeneous nucleation per aerosol
particle and time J is given by
J 5 A9r2N
!
pffiffiffi
Dg# f Dg8g
f exp
,
kT
(1)
where A9 is a prefactor depending only on ambient parameters (specified below for immersion and deposition
nucleation), rN is the aerosol particle (nucleus) radius, f
is a form factor containing information about the aerosol’s ice nucleation ability, Dg# is the activation energy
(aerosol dependent and with different values for immersion and deposition nucleation), Dg8g is the homogeneous
energy of germ formation (specified below for immersion
and deposition nucleation), k is the Boltzmann constant,
and T is the temperature in K.
Taking into account the effect of curved surfaces,
f has the general form
AUGUST 2010
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HOOSE ET AL.
8
33
2
3
!
!
>
>
r
r
>
6
7
6
7
>
!3>
1 cosu N
cosu N
>
6
7
6
7
rg
rg
6
7
6
7
rN <
1
71
6 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi7
v
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
f5
1 16
2
3
6
7
6
7
!
!
!
!
u
u
2
2
>
2
rg >
6u
6u
>
rN
rN 7
rN
rN 7
>
4t
5
4t
5
>
>
1
1
1 2 cosu
1 2 cosu
:
rg
rg
rg
rg
2
33 9
2
3
!
!
>
>
r
r
>
6
7>
7
!26
cosu N
cosu N
>
6
7>
6
7
rg
rg
6
7=
7
rN 6
6
7
6
7
v
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
v
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
16
!
!2 7 > 1 3 cosu r 6 u
!
!2 17 ,
u
>
6u
7
6
7
g
u
>
r
r
r
r
4t
5>
4t
5
>
>
1 2 cosu N 1 N
1 2 cosu N 1 N
;
rg
rg
rg
rg
h
2
i
with the critical germ size rg 5 rg,imm for immersion
freezing or rg 5 rg,dep for deposition nucleation (parameterized below). The ice nucleus surface properties are
contained in the contact angle u. Small contact angles facilitate the formation of ice germs on the particle surface.
Highly efficient ice nuclei have the lowest values of u. In
general, the contact angle for a specific aerosol has different
values for immersion uimm and deposition nucleation udep.
All parameterizations described below are applied in
the temperature range of 08 to 2388C. Note that in this
temperature range, heterogeneous freezing is the trigger
for cloud glaciation via the Wegener–Bergeron–Findeisen
process (e.g., Storelvmo et al. 2008b).
(2)
where n1,w is the number of molecules in contact with a
unit area of particle surface and h is the Planck constant.
Having calculated the nucleation rate per particle for
all considered ice nuclei, the total change in ice crystal
concentration Ni through immersion freezing can be obtained by summing up the contributions from the different aerosol species x, multiplied by the aerosol number
concentration Naer,x and the fraction of these particles
that is activated to liquid droplets fl, x:
dN i 5
dt imm
åx
d( f l,x N aer,x )
dt
5
åx Jimm,x f l,x N aer,x ,
(6)
1) IMMERSION FREEZING
In the liquid phase, the critical germ size is given by
2y w si/w
.
(3)
rg,imm 5
kT ln(aw esw /esi )
The parameters contained here (see also Tables 2 and 3)
are the volume of a water molecule y w, the surface tension between ice and liquid water si/w, the water activity
aw, and the saturation vapor pressures over liquid water
esw and ice esi. The freezing point depression through the
solute effect is included by taking into account the water
activity (,1) of the cloud droplet.
Next, the homogeneous energy of germ formation is
calculated from rg,imm:
4p
.
(4)
s r2
Dgg8,imm 5
3 i/w g,imm
In Eq. (1), which includes parts of the Zeldovich factor and the molecule flux toward the ice germ, A9 can be
parameterized from ambient parameters:
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
yw n1,w 3
kTDgg8,imm
A9imm 5 3
hrg,imm p
rffiffiffiffiffiffiffiffiffiffiffi
n1,w k3 T 3 ln2(aw esw /esi ) 4si/w
5
,
(5)
kT
4hy w s2i/w
where x stands for three different modes of soot (two
process-tagged Aitken modes and an internally mixed
accumulation mode), two modes of dust (accumulation
and coarse mode), and bacteria, fungal spores, and pollen. The modal size of the particles is used for rN in Eqs.
(1) and (2). Here, fl,x is calculated for the soot and dust
modes in the cloud droplet activation parameterization
(Abdul-Razzak and Ghan 2000). In general, particles
that are coated with soluble material are more easily
activated to cloud droplets (higher fl,x) than uncoated
particles. The biological particles are assumed to be
100% activated to cloud droplets (fl,bacteria 5 fl,fungi 5
fl,pollen 5 1) because of their large sizes and high ‘‘wettability’’ (Ariya et al. 2009). Possible immersion nuclei
that have entered droplets via collision scavenging are
not considered because this would require a separate
tracking of in-droplet particles.
Integrating Eq. (6) over one model time step Dt, we
obtain
DN i,imm 5
åx Minff l,x N aer,x f i,max,x ,
f l,x N aer,x [1 exp(J imm,x Dt)]g.
(7)
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VOLUME 67
TABLE 2. List of symbols.
Symbol (units)
22
21
A9 (m s )
A9dep (m22 s21)
A9imm (m22 s21)
aw
e (Pa)
esi (Pa)
esw (Pa)
f
fi,max,x
fl,x
fx,coated
Dg# (J)
Dg#
d,dep (J)
Dgg8 (J)
Dgg8,dep (J)
Dgg8,imm (J)
h (J s)
J (s21)
Jcontact (s21)
Jdep (s21)
Jimm (s21)
Kcoll (m23 s21)
k (J K21)
mw (kg)
Naer,x (m23)
Ng,contact
Ni (m23)
DNi,contact (m23)
DNi,dep (m23)
DNi,imm (m23)
Nl (m23)
n1,w (m22)
rg (m)
rg,dep (m)
rg,imm (m)
rN (m)
rN,x (m)
Si
T (K)
Tc (8C)
t (s)
Dt (s)
y w (m3)
x
u (8)
udep (8)
uimm (8)
ns (s21)
ri (kg m23)
si/y (J m22)
si/w (J m22)
Description
Prefactor in the nucleation rate calculation [Eq. (1)]
Prefactor for deposition nucleation
Prefactor for immersion nucleation
Water activity [parameterized following Chen (1994)]
Water vapor pressure
Saturation vapor pressure over ice (Murphy and Koop 2005)
Saturation vapor pressure over water (Murphy and Koop 2005)
Form factor
Maximum ice nucleating fraction for particles of species x
Fraction of particles of species x activated to cloud droplets
Coated fraction for particles of species x
Activation energy (Table 3)
Activation energy for deposition nucleation (Table 3)
Homogeneous energy for germ formation
Homogeneous energy for germ formation in the vapor phase
Homogeneous energy for germ formation in the liquid phase
Planck constant (56.63 3 10234)
Ice nucleation rate per particle and time
Contact freezing rate per particle and time
Deposition nucleation rate per particle and time
Immersion freezing rate per particle and time
Collision kernel
Boltzmann constant (51.38 3 10223)
Mass of a water molecule (52.99 3 10226)
Aerosol number concentration for species x
Number of contact ice germs per aerosol particle
Ice crystal concentration
Change in ice crystal concentration due to contact nucleation
Change in ice crystal concentration due to deposition nucleation
Change in ice crystal concentration due to immersion nucleation
Cloud droplet concentration
Number of single molecules in contact with unit area of the substrate
(in liquid water; 5 1019; Chen et al. 2008)
Critical germ radius
Critical germ radius for deposition nucleation
Critical germ radius for immersion freezing
Nucleus (aerosol particle) radius
Nucleus (aerosol particle) radius for species x
Supersaturation over ice
Temperature
Temperature
Time
Model time step (52400)
Volume of a water molecule in ice (5mw/ri)
Aerosol species index
Contact angle (Table 3)
Contact angle for deposition nucleation (Table 3)
Contact angle for immersion freezing (Table 3)
Frequency of vibration of water vapor molecule adsorbed on solid substrate
(51013; Pruppacher and Klett 1997, p. 299)
Density of ice [5916.7 2 0.175Tc 2 5 3 1024T c2; Pruppacher and Klett 1997, Eq. (3–2)]
Surface tension between ice and vapor f5 si/w 1 sw/y 5 [(76.1 2 0.155Tc) 1 (28.5 1 0.25Tc)] 3 1023;
Pruppacher and Klett 1997, Eqs. (5–46), (5–47a), and (5–12)g
Surface tension between ice and water [5(28 1 0.25Tc) 3 1023; Pruppacher and Klett 1997, Eq. (5–47a)]
The fraction of particles acting as immersion nuclei per
model time step of 40 min fi,max,x is limited to 1% for
soot and 0.1% for Pseudomonas syringae–like bacteria
and fungal spores, based on typically observed maximum
values (DeMott 1990; Möhler et al. 2008; Yankofsky
et al. 1981; Phillips et al. 2009). These limits are reached
at T & 250 K for soot and T & 268 K for Pseudomonas
syringae–like bacteria and fungal spores. For mineral
AUGUST 2010
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HOOSE ET AL.
TABLE 3. Parameters for the ice nucleation parameterization, derived from laboratory data.
Aerosol
Reference
Nucleation mode
u (8)
Dg# (10220 J)
fi,max,x
Soot
Dust
1% of all bacteria
1% of all fungal spores
Pollen
Soot
Dust
DeMott (1990)
Montmorillonite (Pitter and Pruppacher 1973)
Pseudomonas syringae (Yankofsky et al. 1981)
Pseudomonas syringae (Yankofsky et al. 1981)
Birch (Diehl et al. 2002)
Möhler et al. (2005)
Illite (Zimmermann et al. 2008)
Immersion
Immersion
Immersion
Immersion
Immersion
Deposition
Deposition
40.17
30.98
14.82
14.82
25.16
28.00
12.70
14.4
15.7
17.6
17.6
17.3
220.0
20.621
0.01
1.0
0.001
0.001
1.0
0.01
1.0
dust, no limit is imposed (fi,max,dust 5 1), but the simulated IN fractions in CAM-Oslo never exceed 25% before the Wegener–Bergeron–Findeisen process sets in
and further nucleation is suppressed. Pollen (fi,max,pollen 5
1) reach IN fractions of 100% at T & 258 K. By imposing
upper limits for the IN fractions, we account for the
probably limited validity of the stochastic assumption of
classical nucleation theory over the global model time
step length (e.g., Vali 1994).
The aerosol-specific immersion nucleation parameters
are based on measurements by DeMott (1990), Pitter and
Pruppacher (1973), Yankofsky et al. (1981), and Diehl
et al. (2002) and are listed in Table 3. The derivation
follows the fitting method by Chen et al. (2008). Figure 1
shows the parameterized nucleation rate Jimm as a function of temperature. Note that Jimm is calculated from
Eq. (1), with rg,imm, Dgg8, imm , and A9imm as specified in
Eqs. (3)–(5). Also included in Fig. 1 are the measurements used to derive u and Dg#. The freezing onset,
defined as Jimm . 1025 s21, is approximately 288C for
birch pollen, 2138C for montmorillonite, and 2248C for
soot. The maximum nucleation rate is highest (meaning
that freezing is fastest) for dust and birch pollen. For
Pseudomonas syringae, the freezing rate does not exceed
1025 s21. At 258C, the freezing rate exceeds 1027 s21,
which corresponds to a typical freezing onset temperature in experiments with relatively large liquid samples
(e.g., Vali et al. 1976).
e2 y w
A9dep 5
mw kTns
rffiffiffiffiffiffiffi
s i/y
.
kT
(10)
Here si/y is the surface tension between ice and water
vapor, mw is the mass of a water molecule, and ns is the
vibration frequency of a water molecule attached to a
surface. All constants and temperature-dependent parameters are listed in Tables 2 and 3.
The isolines of constant Jdep in the T 2 Si space (Fig. 2)
can be compared to the alignment of nucleation onset
points (e.g., Zimmermann et al. 2008) and to the threshold (T, Si) values required for a certain activated fraction
(e.g., Schaller and Fukuta 1979; Welti et al. 2009) from
2) DEPOSITION NUCLEATION
The variables entering Eq. (1) for deposition nucleation are given below by analogy to immersion freezing.
For a detailed derivation, see Chen et al. (2008). The
critical germ size rg,dep, Dgg8, dep , and A9dep are functions
of the temperature and of the water vapor pressure e
(equivalent: the supersaturation over ice Si 5 e/esi):
2yw si/y
rg,dep 5
,
kT ln(e/esi )
Dgg8,dep 5
4p
s r2 ,
3 i/y g,dep
(8)
and
(9)
FIG. 1. Parameterized immersion freezing rates for soot (for rN 5
40 nm, as in the experiment), montmorillonite dust (750 nm), birch
pollen (12.5 mm), and Pseudomonas syringae bacteria (500 nm).
The crosses indicate freezing rates derived from measurements
(see Table 3). The sizes of the CAM-Oslo aerosols are variable,
depending on the mode and aging processes, and deviate from the
radii shown here. The immersion freezing rates change accordingly.
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FIG. 2. Isolines of Jdep for illite dust (rN 5 2.5 mm) and soot (90 nm). The red crosses indicate the onset of nucleation in measurements (see Table 3). The black lines are parameterized deposition nucleation. The red line is
simulated isoline of Jdep, which corresponds to the observed data. The blue lines are isolines of constant Jcontact,
assuming a collision rate of 1023 s21. The blue lines correspond to nucleation rates of 1026, 1023, and 1022 s21 and
the black lines to nucleation rates of 1026, 1023, 1022, 1021, and 1 s21 (from bottom to top).
laboratory studies. The isolines calculated from the above
formulas are either parallel to lines of constant Si (i.e.,
Jdep is independent of T), or are bent to higher Si at lower
T (i.e., at constant Si, Jdep decreases with decreasing
temperature). The latter behavior seems unexpected and
contradicts most observations but can be physically explained with the lower absolute value of e at lower temperatures and the slowing down of the deposition process.
At lower temperatures, some observations (Möhler et al.
2005; Shilling et al. 2006; Stetzer et al. 2008; Welti et al.
2009) reflect a slight decrease of Jdep with decreasing T,
but closer to water saturation most data show the opposite behavior (see, e.g., Schaller and Fukuta 1979; Möhler
et al. 2006; Bundke et al. 2008; Welti et al. 2009). This
feature cannot be explained by the classical description
for deposition nucleation on a dry substrate. A possible
explanation is hygroscopic growth or surface wetting of
the particles close to water saturation, such that the formation of ice germs from the vapor phase is inhibited.
Some empirical formulations have been developed to
cover this regime (Fukuta and Schaller 1982; DeMott
1995), but no general theoretical description is available.
For derivation of the parameters used in this study,
data for illite (Zimmermann et al. 2008) and for soot
(Möhler et al. 2005) have been used. The illite data show
a constant onset Si, independent of temperature, and can
therefore be matched well by the theoretical description
(Fig. 2). This is not true for the soot data. We have
selected parameters that match the observations close
to water saturation, but at lower temperatures the
parameterization severely underestimates the deposition
ice nucleation on soot.
Because deposition nucleation before the formation
of a liquid cloud is questionable, we consider here only
in-cloud deposition nucleation. Based on observations
by Korolev and Isaac (2006), a relative humidity of 98%
(over water) is assumed inside mixed-phase clouds. The
particles available for this process are uncoated dust and
soot particles, which are not activated to liquid droplets.
We assume here that coated particles are completely
deactivated, which is a simplification of recent experimental results (Eastwood et al. 2009; Cziczo et al. 2009a).
These studies demonstrated that coated dust particles
require higher supersaturation (or lower temperatures)
than uncoated particles to be activated.
The change of ice crystal number with time by deposition nucleation is given by the sum over two different modes of soot (one externally mixed mode and
one partially coated mode) and two modes of dust (accumulation and coarse mode, both partially coated); only
the uncoated fractions of these modes contribute to deposition nucleation. The index x runs over these four
aerosol species:
dN i 5
dt dep
5
åx
d[(1 f l,x )(1 f x,coated )N aer,x ]
dt
åx Jdep,x, RH 50.98 (1 f l,x )(1 f x,coated )N aer,x .
w
(11)
AUGUST 2010
The modal size of the particles is used for rN in Eqs. (1)
and (2). We obtain fl,x from the cloud droplet activation
parameterization (Abdul-Razzak and Ghan 2000). The
coated fraction fx,coated is calculated by distributing the
available soluble mass (organic and sulfate) over the dust
and black carbon cores in the internally mixed modes,
requiring a minimum coverage of one monolayer.
Integrating Eq. (11) over one model Dt, we obtain
DN i,dep 5
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HOOSE ET AL.
contact nucleation is much higher than the onset temperature for deposition nucleation.
The equilibrium number of possible contact nucleation germs per particle is given by
N g,contact ’ 4pr2N
ns
"
e
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2pmw kT
3 exp åx Minf(1 f l,x )(1 f x,coated )N aer,x f i,max,x,
#
Dg#
dep 1 f Dg8g,dep (r g,imm )
kT
.
(13)
(1 f l,x )(1 f x,coated )N aer, x
The homogeneous nucleation energy for germ formation
from the vapor phase [Eq. (9)] is evaluated at the
3 [1 exp(J dep,x,RH 50.98 Dt)]g.
(12)
w
(smaller) critical size for immersion freezing germs.
The fraction of particles acting as deposition nuclei per Because of the steep decrease of germ number for larger
model time step of 40 min is limited to 1% for soot. sizes, the total number can be approximated by evaluatActivated fractions of up to 5% have been observed by ing the integral at rg,imm. We evaluate f for f(udep, rg,imm).
The contact nucleation rate is given by the collision
Petters et al. (2009), but most experimental data are
rate
between droplets and aerosols that contain at least
reported for activated fractions of less than 1% (Möhler
one
contact
nucleation germ. As in the case of deposiet al. 2005; Kanji and Abbatt 2006). No upper limit is
tion
nucleation,
only uncoated, nonactivated particles
imposed for mineral dust, and in rare cases the simulated
are
allowed
to
act
as contact nuclei. The total contact
dust deposition IN fraction reaches 60%. This is in
nucleation
rate
is
given
(as for deposition nucleation) by
agreement with activated fractions up to 69% reported
the
sum
over
two
modes
containing black carbon and
by Field et al. (2006). Deposition nucleation on bitwo
modes
containing
mineral
dust, denoted by x. As the
ological particles is not considered because they are all
biological
particles
are
assumed
to be fully activated to
assumed to be activated to cloud droplets (see immersion
cloud
droplets,
they
do
not
contribute
to contact nufreezing description) and because the necessary obsercleation.
vational data for the parameter derivation are missing.
3) CONTACT FREEZING
As suggested by Chen et al. (2008), we calculate contact freezing following ‘‘Cooper’s hypothesis.’’ Cooper
(1974) postulated that subcritical ice germs, formed
through deposition from the vapor phase on a dry particle surface, can initiate immediate freezing upon collision with a liquid droplet, if their size is at or above the
critical germ size for immersion nucleation. The critical
germ radius for immersion nucleation [Eq. (3)] is approximately a factor of 4 smaller than the critical germ
radius for deposition nucleation [Eq. (8)], evaluated at
water saturation. Therefore, the onset temperature for
DN i,contact 5
dN i 5
dt contact
åx Kcoll (rN,x , rl )Nl (1 f l,x )
3 (1 f x,coated )N aer, x Max(N g,contact,x , 1) .
(14)
Here Kcoll(rN, rl) is the collision kernel for aerosols of
size rN and droplets of size rl; Kcoll includes Brownian
movements, thermophoresis, and diffusiophoresis and
is calculated following Young (1974) and Cotton et al.
(1986) for 98% relative humidity over water and the
modal aerosol size.
As above, we obtain by integration over one model Dt:
åx Minh(1 f l,x )(1 f x,coated )N aer,x f i,max,x , (1 f l,x )(1 f x,coated )N aer,x
(15)
3 f1 exp[Kcoll (rN ,x , r1 )N l Max(N g,contact,x , 1)Dt]gi.
As for deposition and immersion nucleation, a limit of
1% is applied for the ice nucleating fraction of soot.
Figure 2 includes contact nucleation rates under the
assumption of a typical collision rate KcollNl of 1023
aerosol–droplet collisions per aerosol particle per second
(e.g., Croft et al. 2010). The contact nucleation probability is found to be a steep function of Si. The increase
of contact nucleation with increasing relative humidity
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TABLE 4. Global annual mean cloud cover (CC, %), liquid water path (LWP, g m22), ice water path (IWP, g m22), shortwave cloud
forcing (SWCF, W m22), longwave cloud forcing (LWCF, W m22) and net radiation at the top of the atmosphere Fnet (W m22) for the
present-day simulations, and differences of these variables between present-day and preindustrial simulations. Standard errors are given
for simulations CTL and CNT (and are similar for the other simulations).
CC
DCC
LWP
DLWP
IWP
DIWP
SWCF
DSWCF
LWCF
DLWCF
Fnet
DFnet
CTL
CNT
CNT-highbact
CNT-lowdust
CNT-nosootct
CNT-nobio
64.9 6 0.1
20.2 6 0.1
109.3 6 0.1
4.11 6 0.25
31.0 6 0.04
0.12 6 0.08
257.7 6 0.02
21.30 6 0.07
32.3 6 0.05
20.38 6 0.09
0.13 6 0.04
21.68 6 0.09
64.7 6 0.1
0.0 6 0.1
101.7 6 0.3
2.94 6 0.36
29.9 6 0.04
0.13 6 0.07
256.2 6 0.08
21.40 6 0.10
31.5 6 0.02
20.23 6 0.05
0.94 6 0.11
21.55 6 0.17
64.7
0.0
101.6
3.37
30.0
0.21
256.1
21.49
31.5
20.29
0.94
21.74
65.1
20.1
105.3
1.14
30.9
0.32
257.1
21.23
32.2
20.44
1.12
21.72
64.7
20.2
103.4
4.45
29.8
20.04
256.4
21.61
31.6
20.26
0.65
21.89
64.7
0.0
101.9
3.12
29.9
0.07
256.2
21.49
31.6
20.17
0.98
21.55
is consistent with the (qualitative) results by Svensson
et al. (2009). For the deposition nucleation parameters
for illite, this implies possible contact nucleation already
around 258C at the assumed relative humidity inside
mixed-phase clouds of 98%, and for soot at 298C. These
values are rather high compared to experiments (e.g.,
Pitter and Pruppacher 1973; Diehl and Mitra 1998), but
at present no other theoretically consistent parameterization is available.
3. Results
a. Simulation of clouds and heterogeneous freezing
In this section, results from the CAM-Oslo model with
the new freezing parameterizations are presented. First,
a brief overview over the simulated clouds is given.
Second, the relevant aerosol concentrations are shown.
Third, we discuss the contributions of the different aerosol particles to heterogeneous ice nucleation. The simulated ice nucleation rates are compared to observations
in the next section.
1) CLOUDS AND RADIATIVE PROPERTIES
Table 4 lists global mean values for cloud-related variables from the different experiments. In general, all
simulations agree well with satellite retrievals (see e.g.,
Lohmann et al. 2007, their Table 2), except for an overestimation of the shortwave cloud forcing. The CNT
simulation exhibits an approximately 7% lower global
mean liquid water path (LWP) than the CTL simulation
because of enhanced freezing. This also leads to a decrease in shortwave cloud forcing. The change in ice
water path (IWP) between simulations CTL and CNT
is roughly proportional to the change in LWP because
more frequent cloud glaciation entails enhanced precipitation release. Most cloud properties in the different
CNT sensitivity experiments are very similar, except in
simulation CNT-lowdust. The global mean LWP is significantly higher in simulation CNT-lowdust than in simulation CNT because of reduced liquid-to-ice conversion.
Also, the CNT-nosootct and CNT-nobio simulations exhibit less freezing and a higher LWP than simulation
CNT. As far as global average cloud properties are concerned, the CNT-highbact simulation is not significantly
different from the CNT simulation.
2) PARTICLE NUMBER CONCENTRATIONS
The zonal average number concentrations of mineral
dust, soot, and biological particles in simulation CNT
are shown in Fig. 3. Soot particles, which originate from
natural and anthropogenic combustion processes, are
most numerous, with zonal average concentrations exceeding 1000 cm23 at the surface in the Northern Hemisphere. These particles are mainly in the Aitken mode.
Mineral dust particles, which are in the accumulation
and coarse mode size range, reach a maximum zonal
average surface concentration of 65 cm23, and typical
tropospheric concentrations are 1–10 cm23. As discussed
by Seland et al. (2008), CAM-Oslo has a rather strong
vertical mixing, linked to efficient deep convective vertical transport. This can also be seen for a previous version
of CAM-Oslo in the AeroCom model intercomparison
(Textor et al. 2006).
Primary biological particles are present in significantly
lower concentrations: typical annual average concentrations over continents are 1022–1021 cm23 for bacteria,
1023–1022 cm23 for fungal spores, and 1026–1025 cm23
for pollen, with a large seasonal variability. The biological
particle concentrations are in fair agreement with measurements (Hoose et al. 2010). Note that the concentrations shown here are total aerosol concentrations, not IN
AUGUST 2010
HOOSE ET AL.
2493
FIG. 3. Zonal annual mean particle number concentrations in simulation CNT. Note the two different color bars for the top
and bottom rows.
concentrations, and that only a small subset of all aerosol
particles serves as ice nuclei.
3) ICE NUCLEATION RATES
Figures 4, 5, and 6 display the zonal annual mean
freezing rates [i.e., DNi/Dt from Eqs. (7), (12), and (15),
weighted with the cloud fraction and separated by aerosol component]. For Fig. 7, these rates are vertically integrated and globally averaged. Dust immersion freezing
is found to be the dominant ice nucleation mechanism,
followed by soot immersion and soot contact nucleation,
which contribute approximately equally. Bacteria, fungal spore, and pollen immersion freezing rates are several orders of magnitude lower than the dust and soot
freezing processes. Bacteria immersion freezing is highest in the lower troposphere at mid and high latitudes,
while most other ice nucleation processes peak around
600–400 hPa in the midlatitudes and around 400–300 hPa
in the tropics.
In general, the processes that occur at lower temperatures (e.g., soot immersion freezing) peak at higher
altitudes than the warm-temperature freezing mechanisms (e.g., dust contact freezing). The contact freezing
rates exhibit two maxima (Fig. 6): one close to the surface sources, where the number concentrations of uncoated particles are highest, and one at upper levels,
where low temperatures occur more often. Soot deposition nucleation, which is limited to temperatures close
to the homogeneous freezing onset, mainly occurs in the
upper tropical troposphere and in the lower troposphere
over Siberia and Alaska. We note that the efficiency of soot
deposition nucleation above 2388C is a matter of debate
in recent literature (Gorbunov et al. 2001; Dymarska
et al. 2006). If soot deposition nucleation was effective at
higher temperatures than assumed here, the total soot
(i.e., the anthropogenic) contribution to heterogeneous
ice nucleation would increase.
The main difference from the partitioning of the freezing processes as simulated in ECHAM5-Hamburg Aerosol Model (HAM; Hoose et al. 2008) is the lower soot
contact freezing rate in CAM-Oslo. This is because the
freezing parameterization in Hoose et al. (2008) (based
on Diehl and Wurzler 2004) did not directly depend on
the concentration of soot and dust particles but only on
their fractional contribution to the total aerosol, which
can lead to artifacts. Therefore, contact freezing by soot
was omitted in the follow-up study by Lohmann and
Hoose (2009). Here we explicitly calculate the collision
rate between externally mixed, uncoated soot particles
and droplets, which results in a lower frequency of contact freezing events.
b. Comparison to observations
Ice nucleation schemes in global models are difficult
to evaluate. While laboratory and field measurements
have been used for comparison with parameterizations
in a parcel model framework (Eidhammer et al. 2009),
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FIG. 4. Zonal annual mean immersion freezing rates (DNi,imm/Dt) in simulation CNT.
for global models so far only the ice crystal concentrations and ice crystal sizes, which are determined by both
primary and secondary ice formation and sink processes,
have been compared to observations (Lohmann and
Diehl 2006; Storelvmo et al. 2008a). Here we show comparisons to available data from in situ IN observations.
This comparison is possible only in a statistical sense
because the model is not able to capture the exact conditions at the sampling points in both space and time.
1) IN CONCENTRATIONS
The most common instrument for measuring ice nuclei
concentrations in the atmosphere is the continuous-flow
diffusion chamber (CFDC) (Rogers et al. 2001). In this
instrument, aerosol particles enter through an inlet and
are exposed to a chosen temperature and ice supersaturation. After a residence time of 5–20 s (depending on the
instrument setup), the particles that have grown to ice
crystals larger than 1 mm are optically detected. While the
CFDC has the advantage of allowing real-time airborne
measurements, some limitations have to be accounted
for. Because of the short residence time, the dominant
ice nucleation modes in the CFDC are deposition and
condensation nucleation. The largest aerosol particles
(.1.2–2 mm in diameter) have to be removed upstream
of the chamber to avoid confusion with the nucleated ice
crystals.
For comparison to CFDC chamber measurements, the
model ice nuclei concentration [hereafter termed ‘‘model
IN(10s)’’] has been defined as a 10-s integral over the
time-step mean, in-cloud freezing rates [sum over Eqs.
(7), (12), and (15), multiplied by 10/Dt]. Classical nucleation theory predicts a constant freezing rate (i.e., the
number of ice-nucleating particles would increase approximately proportionally to the sampling time) as long
as the aerosol and droplet populations are not significantly depleted. Here, limitations on the maximum fraction of active particles per species are imposed (see Table
3). These upper bounds are accounted for in the values of
the model IN(10s).
Figure 8 shows the simulated model IN(10s) concentrations as a function of temperature, sampled at all global
grid points at an arbitrary time step. The simulated IN(10s)
concentrations attain significant values at temperatures
below 2118C and increase strongly with decreasing temperature until around 2208C. In this temperature range,
AUGUST 2010
HOOSE ET AL.
2495
FIG. 5. Zonal annual mean deposition nucleation rates (DNi,dep/Dt) in simulation CNT.
model IN(10s) concentrations are mostly between 0.5 and
20 L21. Also shown in Fig. 8 are CFDC IN concentrations
from a number of campaigns at different locations. The
measured IN concentrations are of the same order of
magnitude and reflect the same temperature dependence
as the simulated concentrations. However, when the different studies are investigated individually, the observed
temperature dependence is weaker. We have to keep in
mind that the CFDC measurements report the ice nuclei
concentration at a selected chamber temperature, which
can be different from the environmental temperature,
while the simulated ice nuclei concentrations are reported for the actual gridpoint temperature. Therefore
the model ice nuclei concentrations for lower temperatures tend to be valid for higher altitudes and latitudes,
where the aerosol concentration is also lower in general.
For a more detailed comparison, this analysis is repeated for the grid boxes closest to the CFDC measurement locations (Fig. 9). Fort Collins and Storm Peak
fall into the same global model grid box, but we have
selected data from different vertical levels to account for
the altitude of the Storm Peak laboratory (3200 m). The
data at Barrow, Alaska, were collected by aircraft within
the lowest 2000 m of the atmosphere, and the model
data are sampled from the corresponding vertical levels.
In this comparison, the model IN(10s) concentrations are
diagnosed for 17 different temperatures at 28 intervals
from 268 to 2388C by repeating the freezing rate calculations with specified temperature values. Note that
the model IN(10s) concentrations calculated in this way
still depend on the simulated cloud parameters (liquid
activated fraction, cloud droplet sizes, etc.) and are
not completely equivalent to the processes occurring in
a CFDC.
At all investigated locations, the mean model IN(10s)
concentrations increase with decreasing temperatures
from 2128 to 2248C and then flatten off or even decrease
again. This behavior at T , 2248C is consistent with the
observations at Storm Peak. The strong temperature
dependence at T . 2208C is not confirmed by the Fort
Collins and Barrow data, which are more scattered. At
Storm Peak and at Barrow for T , 2158C, the observed
data fall between the 25th and 75th percentiles of the
simulated data. The majority of observations at Fort
Collins show higher IN concentrations than simulated,
and this is also true for T . 2158C at Barrow. Not much
FIG. 6. Zonal annual mean contact freezing rates (DNi,contact/Dt) in simulation CNT.
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FIG. 8. Ice nuclei concentrations (calculated as 10-s integrals
over the freezing rate), sampled at all global grid points at an arbitrary time step of simulation CNT (black dots). The colored
symbols represent CFDC IN measurements at various locations.
FIG. 7. Global annual mean vertically integrated nucleation rates in
simulation CNT.
can be said about regional variations. The vertical and
temporal variability at the Fort Collins/Storm Peak gridpoint is as large as the difference between Colorado and
Barrow.
The CFDC IN concentrations have been found to
correlate well with the concentration of coarse mode
aerosol particles (DeMott et al. 2006) but not with total
aerosol concentration, which is dominated by smaller
particles. Similar results were obtained earlier by Georgii
and Kleinjung (1967). This is in agreement with the
nucleation rate increasing with the square of the particle
radius [Eq. (1)]. Figure 10 displays the model IN(10s)
concentration versus the concentration of aerosol particles with diameter .0.5 mm. If sampled at all temperatures (Fig. 10a), only a modest correlation is obtained
because of low IN concentrations at warm subzero temperatures. But if sampled only at T , 2208C (Fig. 10b),
the model IN(10s) concentration increases systematically with coarse mode aerosol particle concentration.
The fit to the data from several campaigns by DeMott
et al. (2006), which is included for comparison, shows
a similar but steeper slope. Georgii and Kleinjung (1967)
find a slope (measured for aerosol particles with a diameter .0.6 mm) that is similar to the model results.
The reason for the high correlation in the model is that
dust particles, which constitute the majority of the IN,
are also the most abundant coarse mode aerosols in regions with low temperatures. Such temperatures are not
common in the marine boundary layer, where sea salt is
the dominant coarse mode aerosol and no such correlation can be expected.
2) COMPOSITION OF ICE NUCLEI AND ICE
CRYSTAL RESIDUES
The composition of particles obtained from evaporated ice or snow crystals (residues) can give indications
about the composition of the ice nuclei that were responsible for the freezing. However, the residues also
contain particles scavenged by collisions, complicating
the interpretation of the data. Alternatively, the composition of particles that have nucleated ice in a CFDC
can be analyzed, under the limitations of the CFDC
measurements as discussed above. Table 5 lists a number of ice crystal residue (Targino et al. 2006; Pratt et al.
2009), snow crystal residue (Kumai 1961; Kumai and
Francis 1962), and CFDC IN (Phillips et al. 2008; Prenni
et al. 2009a,b) composition measurements. The dataset
compiled by Phillips et al. (2008) is the most comprehensive one. Not all measurements distinguish organic
and elemental carbonaceous particles. All observations
agree on mineral dust as the dominant IN/ice crystal
residue component (50%–88% in number), but the carbonaceous fraction is more variable (0%–47%). Biological particles were only identified in three cases, and
with very different fractions: 1% (Kumai 1961), 33%
(Pratt et al. 2009), and up to 47% (Prenni et al. 2009b).
These numbers are compared to the global average IN
composition from the model. In the CTL simulation,
which includes only mineral dust and soot IN, these
contribute to 84% and 16%, respectively. In the CNT
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HOOSE ET AL.
2497
FIG. 9. Ice nuclei concentrations for specified temperatures (calculated as 10-s integrals over the freezing rate),
sampled at the grid points closest to the measurement locations, at 10 arbitrary time steps during the specified month,
in simulation CNT (black boxes and whiskers). The whiskers represent the 5th and 95th percentiles, and the boxes the
25th and 75th percentiles and the median. The asterisks mark the simulated mean concentrations. The colored
symbols represent CFDC IN measurements.
simulation, with the new freezing parameterization and
additional contributions by biological IN, this distribution remains very similar: 77% and 23%. On global average, only 1 in 107 ice nuclei is of biological origin. For
dust and soot, these values lie within the broad range of
observed values. The simulated biological IN fraction is
much lower than the high values reported by Pratt et al.
(2009) and Prenni et al. (2009b). However, the Prenni
et al. (2009b) data were sampled directly above the rain
forest canopy, where temperatures are always above 08C
and ice nuclei cannot be diagnosed in the model, and the
Pratt et al. (2009) data stem from only one individual
cloud. While these data might not be representative of
the global contribution of biological particles to ice nucleation, they suggest that biological influence on clouds
can be strongly enhanced on local scales.
For the Arctic (north of 668N), an enhancement of
the mineral dust component (to 88%) is found. This is
consistent with the Arctic measurements by Prenni et al.
(2009a) showing a larger mineral dust fraction (64%)
than the more comprehensive dataset of Phillips et al.
(2008) (52%), which is mainly based on the same instrumental method. Further regional comparisons are
difficult to infer from the observations listed in Table 5
because of differences in sampling and instrumentation.
The results of the sensitivity studies (also listed in
Table 5) demonstrate the sensitivity of the model to
assumptions entering the ice nucleation parameterization. In the simulation CNT, global ice nucleation is split
among mineral dust, soot, and biological particles as
77%, 23%, and 1027%, respectively. The biological IN
fraction is increased to 5 in 107 particles in the CNThighbact simulation. In the CNT-lowdust simulation,
mineral dust contributes only to 39% of the ice nucleation, while the soot fraction is raised to 61%. This
partitioning is in worse agreement with the observations
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FIG. 10. Ice nuclei concentrations (calculated as 10-s integrals over the freezing rate) in simulation CNT, displayed
as a function of the number concentrations of aerosol particles with d . 0.5 mm, for (a) all T and (b) T # 2208C.
The dashed lines are power-law fit to observations (DeMott et al. 2006; Georgii and Kleinjung 1967); d . 0.6 mm,
T 5 2218C.
than the other simulations, as all field studies listed in
Table 5 show a larger mineral dust IN fraction than
carbonaceous IN fraction. When contact freezing by
soot is switched off, the mineral dust IN fraction increases to 88%, which is at the high end of the observed
values. The soot fraction is 12% in the CNT-nosootct
simulation. Finally, without biological particles as IN
(simulation CNT-nobio), the partitioning of heterogeneous freezing between mineral dust and soot is very
similar to that in simulation CNT.
The simulated ice nucleation is to a larger extent
dominated by mineral dust than most field observations,
but this result is sensitive to assumptions on dust scavenging. The observed carbonaceous IN fractions are
highly variable, with the simulated percentage (in all experiments except CNT-lowdust) well in the middle of the
observed range. For biological particles, globally representative data are not available yet, so no definite
conclusions on the model performance for these particles can be drawn. The nature and origin of the ‘‘other’’
particles, which make up 1%–34% of the measured IN/
ice crystal residues, remain to be solved.
c. Aerosol indirect effect
In Table 4, the global mean differences between the
present-day and preindustrial cloud and radiative properties are listed. The model includes direct, semidirect,
and indirect aerosol effects in warm and mixed-phase
clouds. Aerosol effects on ice clouds at temperatures below the homogeneous freezing threshold are not considered in this study. The different aerosol indirect effects
(cloud albedo effect and cloud lifetime effect in warm
clouds and the glaciation and deactivation indirect effect
in mixed-phase clouds; Lohmann and Hoose 2009) counteract each other. This complicates the interpretation of
the resulting net effect.
In the CTL simulation, the change in top-of-theatmosphere net radiation Fnet is 21.68 6 0.09 W m22
(5-yr average with standard error), which is less negative
than if only warm-phase indirect effects are included
(Hoose et al. 2009; 22.1 W m22). The differences compared to Storelvmo et al. (2008b) stem from model updates in the warm-phase physics. In simulation CNT,
DFnet is slightly less negative (21.55 6 0.17 W m22) than
in CTL, probably because of a different vertical distribution of cloud liquid water and a stronger glaciation
indirect effect, which tends to counteract the indirect
effect of warm clouds (Lohmann 2002). This hypothesis
is confirmed by the more negative indirect effect in the
CNT-nosootct simulation (21.89 6 0.11 W m22), in which
part of the soot glaciation capability is suppressed. The
similarly high indirect effect in the simulation CNT-lowdust
is presumably linked to the increased LWP and corresponding increase in warm-phase indirect effects. Variation of the assumptions on biological ice nucleation
(simulations CNT-highbact and CNT-nobio) results in
nonlinear changes of the indirect effect. For both enhanced and reduced biological ice nucleation, a more
negative DSWCF is found. This can be explained on the
one hand by a higher LWP in the CNT-nobio simulation
(and thus more clouds contributing to the warm cloud
indirect effect) and on the other hand by natural aerosol
dominating the onset of freezing in simulation CNThighbact (and thus less potential for anthropogenic soot
to trigger cloud glaciation).
AUGUST 2010
2499
HOOSE ET AL.
TABLE 5. Ice nuclei composition from observations and simulations, given as fractional frequency of occurrence. From observations, ice
and snow crystal residue composition and CFDC IN compositions from atmospheric samples at individual locations are reported. From
the model, the global or regional average contributions to the total heterogeneous freezing rates are listed. ‘‘Mineral dust’’ includes the
‘‘dust/metallic’’ category in Phillips et al. (2008), ‘‘metal oxides/dust,’’ ‘‘metal oxides/dust 1 sulfates/salts,’’ and ‘‘metal oxides/dust 1
carbonaceous’’ particles sampled by Prenni et al. (2009a), and ‘‘dust’’ and ‘‘dust 1 carbonaceous’’ particles in Prenni et al. (2009b). For
‘‘carbonaceous’’, the ‘‘soot,’’ ‘‘biological,’’ and ‘‘organic carbon-nitrate’’ particles in Pratt et al. (2009), the ‘‘combustion product’’ and
‘‘micro-organism’’ particles in Kumai (1961), and the soot and bacteria, fungal spores, and pollen from the model are summed up, and the
‘‘low-Z’’ fraction from Targino et al. (2006) is reported. Prenni et al. (2009b) inferred from indirect evidence that the carbonaceous IN
fraction measured in the Amazon basin was dominated by biological particles.
Phillips et al. (2008), from six campaigns
Prenni et al. (2009a), Mixed-Phase Arctic Clouds
Experiment (MPACE), Alaska
Prenni et al. (2009a), Surface Heat Budget of the Arctic Ocean
(SHEBA)–First International Satellite Cloud Climatology
Project (ISCCP) Regional Experiment–Arctic Cloud
Experiment (FIRE.ACE), Arctic Ocean
Prenni et al. (2009b), Amazon basin
Pratt et al. (2009), Wyoming
Targino et al. (2006), Scandinavia
Kumai (1961), Hokkaido, Japan
Kumai (1961), Honshu, Japan
Kumai (1961), Michigan
Kumai and Francis (1962), Greenland
Simulation CTL, global
Simulation CNT, global
Simulation CNT, Arctic
Simulation CNT-highbact, global
Simulation CNT-lowdust, global
Simulation CNT-nosootct, global
Simulation CNT-nobio, global
4. Conclusions
A new ice nucleation parameterization has been introduced in the CAM-Oslo model, treating more processes and ice nuclei species than previous global model
studies. Primary biological particles (bacteria, fungal
spores, and pollen) are included with simple emission parameterizations recently published in the literature. These
emission functions and the resulting concentrations bear
considerable uncertainties (e.g., with respect to seasonal
variability). Further developments are required and can
help to improve our estimates in the future [see, e.g., Vogel
et al. (2008) for a detailed pollen emission parameterization in a regional model]. For biological particles as well as
for mineral dust and soot, the simulated concentrations in
the upper troposphere are sensitive to vertical transport
and to assumptions on the particle mixing state and scavenging (e.g., Koch et al. 2009; Croft et al. 2010).
The ice nucleation parameterization is based on classical theory, which provides a theoretically sound and
consistent framework. Nevertheless, some observations
are in conflict with the assumption of a stochastic nature
of ice nucleation, especially with a freezing rate that is
constant in time. A distribution of contact angles and
Mineral dust
Carbonaceous
Soot
Biological
Other
0.52
0.64
0.37
0.35
n/a
n/a
n/a
n/a
0.11
0.01
0.64
0.17
n/a
n/a
0.19
0.50
0.50
0.58
0.57
0.88
0.87
0.85
0.84
0.77
0.88
0.77
0.39
0.88
0.77
0.47
0.41
0.23
0.09
0.04
0.02
0.00
0.16
0.23
0.12
0.23
0.61
0.12
0.23
n/a
0.04
n/a
0.08
0.04
0.02
0.00
0.16
0.23
0.12
0.23
0.61
0.12
0.23
up to 0.47
0.33
n/a
0.01
0.00
0.00
n/a
0.00
1 3 1027
1 3 1027
5 3 1027
2 3 1027
1 3 1027
0.00
0.03
0.09
0.19
0.34
0.08
0.11
0.15
0.00
0.00
0.00
0.00
0.00
0.00
0.00
activation energies instead of one fixed parameter per
aerosol species could be a way to alleviate this discrepancy (Marcolli et al. 2007; Luond et al. 2010). With
a distribution of the efficiency of ice nucleation within
the aerosol population, the most efficient ice nuclei
would be depleted after the first initiation of freezing,
and further nucleation would be delayed. However, a
distribution of contact angles and activation energies is
difficult to derive from the measured nucleation rates
and would entail complications in the implementation.
Here, these problems are circumvented in a simplified
way by applying upper limits to the percentage of aerosols acting as ice nuclei, but the values of these upper
limits are also arguable. In-cloud deposition nucleation
is included for uncoated mineral dust and soot particles.
Contact freezing is the most uncertain process in our
description, and further experimental and theoretical
studies are required before its parameterization can be
improved. Other less well-understood freezing mechanisms (e.g., inside-out contact nucleation; Durant and
Shaw 2005) are not considered here.
With the new ice nucleation parameterization applied
for mineral dust, soot, bacteria, fungal spores, and pollen,
it is found that on global average 77% of the simulated
2500
JOURNAL OF THE ATMOSPHERIC SCIENCES
heterogeneous nucleation is initiated by mineral dust
particles and 23% by soot, while biological particles only
contribute a fraction of 1027 of all ice nucleation events.
Immersion freezing is the dominant freezing mechanism, but for soot—which is often externally mixed and
not activated to cloud droplets—contact freezing is also
relevant. Even with more extreme assumptions as to the
probability of bacteria and fungal spores acting as ice
nuclei, the biological aerosol contribution to global
freezing remains marginal because of their low number
concentrations. Nevertheless, the simulated concentration of bacterial IN in precipitation is of the same order
of magnitude or higher than the measured concentrations of biological IN in snow samples (Hoose et al.
2010). However, we cannot rule out the local importance
of biological particles nor the possibility that in some
cases at warm subzero temperatures the few but very
active biological IN can initiate glaciation of clouds,
which would have remained liquid without this trigger.
The simulated ice nuclei concentrations are compared
to CFDC measurements, and in a statistical sense a good
agreement is found. At temperatures below 2208C, the
simulated IN concentrations correlate with the coarse
mode aerosol concentration, similar to observations. The
effect of the new ice nucleation parameterization on
the simulated indirect effect is overall small. Although
the contribution of anthropogenic soot to heterogeneous
ice nucleation is slightly higher than in the control simulation, the glaciation indirect effect is lower than in previous studies and cannot significantly offset the indirect
effects of warm clouds.
Numerous uncertainties remain concerning the numerical description of ice nucleation in large-scale models,
especially for biological particles: emissions, size distributions, ice nucleation active fractions, hydrophilicity,
wet deposition, freezing rates, the role of preactivation,
the abundance of different biological species in different climatological regions, and maximum ice nucleating
fractions. Furthermore, the ice nucleation efficiency of
mineral dust has also been linked to biogenic contamination, which would mean that biological ice nucleation
is already implicitly contained when mineral dust ice
nucleation is included. The importance of further possible ice nucleators, such as volcanic ash and anthropogenic metallic particles, cannot be assessed yet because
their global sources are not well known.
With the uncertain parameters selected to the best of
our present knowledge, we find that mineral dust dominates cloud glaciation and that the role of biological
particles for ice formation in mixed-phase clouds is small
in a global average. This implies that a possible aerosol
influence on precipitation formation via the Wegener–
Bergeron–Findeisen process varies with past or future
VOLUME 67
change in dust emissions. We suggest that further laboratory and field experiments are mandatory in order to
obtain a larger database for improved modeling studies.
Acknowledgments. C. H. thanks Xiaohong Liu and
Stephan Weinbruch for helpful discussions, Paul DeMott
for providing data, Trond Iversen, Alf Kirkevåg, and
Øyvind Seland for development of the CAM-Oslo aerosol module, and Trude Storelvmo for providing the
double-moment cloud microphysics scheme and valuable
comments. Three anonymous reviewers are acknowledged for their constructive comments, which helped to
improve this article. This research was supported by the
projects NorClim (Norwegian Research Council Grant
178246), EUCAARI (European Integrated Project
036833-2), and POLARCAT (Norwegian Research
Council Grant 460724), and computing time was provided through a grant from the Norwegian Research
Council’s Program for Supercomputing.
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