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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, Norway
Jen-Ping Chen and Anupam Hazra
Department of Atmospheric Sciences, National Taiwan University, Taiwan
∗
Current affiliation: Institute for Meteorology and Climate Research (IMK-AAF), Karlsruhe Institute of
Technology, Karlsruhe, Germany.
†
Corresponding author address: Corinna Hoose, Department of Geosciences, University of Oslo, P.O. Box
1022 Blindern, 0315 Oslo, Norway.
E-mail: corinna.hoose@geo.uio.no
1
ABSTRACT
An ice nucleation parameterization based on classical nucleation theory, with aerosol-specific
parameters derived from experiments, has been implemented into the global climate 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 0◦ C and −38◦ C. Immersion freezing is considered for insoluble particles which
are activated to cloud droplets, and deposition and contact nucleation is only allowed for
uncoated, unactivated aerosols. Immersion freezing by mineral dust is found to be the dominating 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 on 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. Due to the dominance of mineral dust, the simulated ice nuclei concentrations at temperatures below −20◦ C are found to correlate with coarse-mode aerosol
particle concentrations. The ice nuclei (IN) concentrations in the model agree overall well
with in-situ continuous flow diffusion chamber measurements. At individual locations, the
model exhibits a stronger temperature dependence of IN concentrations than what is observed. The simulated IN composition (82% mineral dust, 18% soot and 10−5 % biological
particles) lies in the range of observed ice nuclei and ice crystal residue compositions.
1
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 0◦ C and −38◦ C,
aerosol particles are required as ice nuclei (IN) to initiate either freezing of supercooled cloud
droplets or ice nucleation from the vapor phase. Various insoluble particles like 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 L−1 ) 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, and that 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 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
2
both methods, the composition of the subset of ambient aerosol particles which 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 com3
monly accepted ice nucleation mechanisms in supercooled liquid clouds. Meanwhile, the
atmospheric relevance of deposition nucleation at temperatures above −38◦ C is uncertain.
Favorable for the occurrence of deposition nucleation is the availability of uncoated IN, e.
g. dust particles, in 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 or not 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 about −20◦ C 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 about −15◦ C.
Theoretical formulations of heterogeneous ice nucleation include the so-called classical
nucleation theory (e. g. Fletcher 1962), which treats nucleation as a stochastic process,
and the semi-empirical singular hypothesis (e. g. Levine 1950), which assigns a defined
spontaneous freezing temperature to every aerosol particle. Parameterizations which are
4
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.
2. Model description and treatment of ice nucleation
a. CAM-Oslo
The aerosol-climate model CAM-Oslo is based on 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, 2010) has now been modified by a new treatment of ice nucleation in mixedphase clouds, see below.
The aerosol concentrations, mixing states and the fractions activated to cloud droplets
5
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. (subm.), with emissions
based on Burrows et al. (2009b) 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 µm for bacteria (Burrows et al. 2009b), 5 µm for fungal
spores (Elbert et al. 2007) and 30 µm for pollen (Jacobson and Streets 2009).
b. Simulation setup
A control experiment (CTL) with the previously used freezing parameterization (Lohmann
and Diehl 2006), simulation CNT (‘classical nucleation theory’) 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 simulation ‘CNT-highbact’, 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
6
and fungal spore ice nuclei by a factor 100. In simulation ‘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. In simulation ‘CNT-nobio’, all biological
freezing processes are set to 0.
All simulations are run in T42 resolution (2.8125◦ x 2.8125◦ ), with 26 vertical levels.
The simulations are integrated for 5 years after 4 months of spin-up, for present-day 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). As 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 laboratorygenerated soot from an acetylene burner and a graphite spark generator, respectively.
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
7
species. Lindemann et al. (1982) found that the ratio of bacteria which 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 can nucleate ice, not all cells
(Hirano and Upper 1995).
Concentration measurements of atmospheric concentrations of ice nucleation active fungi
are more rare. Two (out of 14 investigated) species of the Fusarium genus have been found
to nucleate ice with similar characteristics as 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 for 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
8
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 for taking 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.
d. Ice nucleation parameterizations
The ice nucleation parameterization used in this study is based on classical nucleation
theory (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 was
uncertain.
Chen et al. (2008) have presented a method for derivation of these parameters from laboratory experiments, and this method is applied here. Due to 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;
9
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. J, the rate of heterogeneous nucleation per aerosol
particle and time, is given by:
0
J =A ·
2
rN
·
p
f · exp
−∆g # − f · ∆gg◦
kT
!
(1)
A0 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, ∆g # is the activation energy
(aerosol-dependent and with different values for immersion and deposition nucleation), ∆gg◦
is the homogeneous energy of germ formation (specified below for immersion and deposition
nucleation), k is the Boltzmann constant and T the temperature in K.
10
Taking into account the effect of curved surfaces, the form factor f has the general form


3
rN
1 − cos θ rg


1
1 +  r

f =

2 
2
1 − 2 cos θ rrNg + rrNg


 
3 
3
cos θ − rrNg
cos θ − rrNg


 

rN





r
r
2
−
3
+
+


2  
2  
rg
1 − 2 cos θ rrNg + rrNg
1 − 2 cos θ rrNg + rrNg


2
cos θ − rrNg


rN


r
−
1
(2)
+ 3 cos θ


2
rg
rN
rN
1 − 2 cos θ rg + rg
with the critical germ size rg = rg,imm for immersion freezing or rg = rg,dep for deposition
nucleation (parameterized below). The ice nucleus surface properties are contained in the
contact angle θ. Small contact angles facilitate the formation of ice germs on the particle
surface. Highly efficient ice nuclei have the lowest values of θ. In general, the contact angle
for a specific aerosol has different values for immersion (θimm ) and deposition nucleation
(θdep ).
All parameterizations described below are applied in the temperature range of 0 to −38◦ C.
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).
1) Immersion freezing
In the liquid phase, the critical germ size is given by:
rg,imm =
2vw σi/w
kT ln(aw esw /esi )
11
(3)
The parameters contained here (see also Tables 2 and 3) are the volume of a water molecule
(vw ), the surface tension between ice and liquid water (σi/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 .
◦
=
∆gg,imm
4π
2
σi/w · rg,imm
3
(4)
The prefactor A0 in Eq. (1), which includes parts of the Zeldovich factor and the molecule
flux toward the ice germ, can be parameterized from ambient parameters.
A0imm
vw n1,w
= 3
hrg,imm
r
3
n1,w k 3 T 3 ln2 (aw esw /esi )
◦
kT ∆gg,imm
=
2
π
4hvw σi/w
r
4σi/w
kT
(5)
Here 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 which is activated to liquid
droplets (fl,x ).
X d(fl,x Naer,x ) X
dNi =
−
=
Jimm,x fl,x Naer,x
dt imm
dt
x
x
(6)
Here 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 the
12
parameter rN in Eq. (1) and (2). The liquid activated fraction, fl,x , is calculated for the soot
and dust modes in the cloud droplet activation parameterization (Abdul-Razzak and Ghan
2000). In general, particles which 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 = fl,f ungi = fl,pollen = 1) because of their
large sizes and high wettability (Ariya et al. 2009). Possible immersion nuclei which 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 timestep (∆t), we obtain:
∆Ni,imm =
X
Min (fl,x Naer,x fi,max,x , fl,x Naer,x (1 − exp (−Jimm,x ∆t)))
(7)
x
The fraction of particles acting as immersion nuclei per model timestep of 30 minutes
(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 . 250K for
soot and T . 268K for Pseudomonas syringae-like bacteria and fungal spores. For mineral
dust, no limit is imposed (fi,max,dust = 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 = 1) reach IN fractions of 100% at T . 258K. 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
13
DeMott (1990), Pitter and Pruppacher (1973), Yankofsky et al. (1981) and Diehl et al.
(2002) and are listed in Table 4. The derivation follows the fitting method by Chen et al.
(2008). Fig. 1 shows the parameterized nucleation rate Jimm as a function of temperature.
◦
Jimm is calculated from Eq. (1), with rg,imm , ∆gg,imm
and A0imm as specified in Eqs. (3)–(5).
Also included in Fig. 1 are the measurements which were used to derive the parameters θ
and ∆g # . The freezing onset, if defined as Jimm > 10−5 s−1 , is about −8◦ C for birch pollen,
−13◦ C for montmorillonite and −24◦ C 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 10−5 s−1 . At −5◦ C, the freezing rate exceeds 10−7 s−1 , which
corresponds to a typical freezing onset temperature in experiments with relatively large liquid
samples (e. g. Vali et al. 1976).
2) Deposition nucleation
The variables entering Eq. (1) for deposition nucleation are given below in analogy to
immersion freezing. For a detailed derivation, see Chen et al. (2008). The critical germ size
◦
rg,dep , the homogeneous energy of germ formation ∆gg,dep
and the prefactor A0dep are functions
of the temperature and of the water vapor pressure e (equivalent: the supersaturation over
ice Si = e/esi ).
rg,dep =
2vw σi/v
kT ln(e/esi )
4π
2
σi/v · rg,dep
3
r
σi/v
e2 vw
=
mw kT νs kT
◦
∆gg,dep
=
A0dep
14
(8)
(9)
(10)
Here σi/v is the surface tension between ice and water vapor, mw the mass of a water molecule,
and νs 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 −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 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 slow-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 can
not 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
15
description (Fig. 2). This is not true for the soot data. We have selected parameters
which 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
supersaturations (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 4 aerosol species.
X d((1 − fl,x )(1 − fx,coated )Naer,x )
dNi =
−
dt dep
dt
x
X
=
Jdep,x,RHw =0.98 (1 − fl,x )(1 − fx,coated )Naer,x
(11)
x
The modal size of the particles is used for the parameter rN in Eqs. (1) and (2). The
liquid activated fraction, fl,x , is obtained 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
16
internally mixed modes, requiring a minimum coverage of one monolayer.
Integrating Eq. (11) over one model timestep (∆t), we obtain:
∆Ni,dep =
X
Min ((1 − fl,x )(1 − fx,coated )Naer,x fi,max,x ,
(12)
x
(1 − fl,x )(1 − fx,coated )Naer,x (1 − exp (−Jdep,x,RHw =0.98 ∆t)))
The fraction of particles acting as deposition nuclei per model timestep of 30 minutes is
limited to 1% for soot. Activated fractions of up to 5% have been observed by Petters
et al. (2009), but most experimental data are reported for activated fractions of less than 1%
(Möhler et al. 2005; Kanji and Abbatt 2006). No upper limit is imposed for mineral dust, and
in rare cases, the simulated dust deposition IN fraction reaches 60%. This is in agreement
with activated fractions up to 69% reported by Field et al. (2006). Deposition nucleation on
biological particles is not considered, because they are all assumed to be activated to cloud
droplets (see immersion freezing description) and because the necessary observational 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 about a factor 4
smaller than the critical germ radius for deposition nucleation (Eq. 8), evaluated at water
saturation. Therefore the onset temperature for contact nucleation is much higher than the
17
onset temperature for deposition nucleation.
The equilibrium number of possible contact nucleation germs per particle is given by
#
Ng,contact
◦
(rg,imm )
∆gdep + f ∆gg,dep
e
2
√
≈ 4πrN
exp −
kT
νs 2πmw kT
!
(13)
The homogeneous nucleation energy for germ formation from the vapor phase (Eq. 9) is
evaluated at the (smaller) critical size for immersion freezing germs. Because of the steep
decrease of germ number for larger sizes, the total number can be approximated by evaluating
the integral at rg,imm . The form factor f is evaluated for f (θdep , rg,imm ).
The contact nucleation rate is given by the collision rate between droplets and aerosols
which contain at least one contact nucleation germ. As in the case of deposition nucleation,
only uncoated, non-activated particles are allowed to act as contact nuclei. The total contact
nucleation rate is given (as for deposition nucleation) by the sum over two modes containing
black carbon and two modes containing mineral dust, denoted by the index x. As the
biological particles are assumed to be fully activated to cloud droplets, they do not contribute
to contact nucleation.
X
dNi =
Kcoll (rN,x , rl ) · Nl · (1 − fl,x )(1 − fx,coated )Naer,x · Max(Ng,contact,x , 1) (14)
dt contact
x
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.
18
As above, we obtain by integration over one model timestep (∆t):
∆Ni,contact =
X
Min ((1 − fl,x )(1 − fx,coated )Naer,x fi,max,x ,
(15)
x
(1 − fl,x )(1 − fx,coated )Naer,x (1 − exp (−Kcoll (rN,x , rl ) · Nl · Max(Ng,contact,x , 1)∆t)))
Same as for deposition and immersion nucleation, a limit of 1% is applied for the ice nucleating fraction of soot.
Fig. 2 includes contact nucleation rates under the assumption of a typical collision rate
(Kcoll · Nl ) of 10−3 aerosol-droplet collision 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 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 −5◦ C at the assumed relative humidity
inside mixed-phase clouds of 98%, and for soot at −9◦ C. 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
19
to observations in the next section.
1) Clouds and radiative properties
Table 5 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 short-wave cloud forcing. The
CNT simulation exhibits an approximately 6% lower global mean liquid water path (LW P )
than the CTL simulation because of enhanced freezing. This also leads to a decrease in
short-wave cloud forcing. The change in ice water path (IW P ) between simulations CTL
and CNT is roughly proportional to the change in LW P , 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 LW P is significantly higher in simulation CNT-lowdust than in simulation CNT due
to reduced liquid-to-ice conversion. Also the CNT-nosootct and CNT-nobio simulations
exhibit less freezing and a higher LW P than simulation CNT. As far as the 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
20
exceeding 1000 cm−3 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 cm−3 , and typical
tropospheric concentrations are 1−10 cm−3 . 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 10−2 − 10−1 cm−3 for bacteria, 10−3 −
10−2 cm−3 for fungal spores and 10−6 −10−5 cm−3 for pollen, with a large seasonal variability.
The biological particle concentrations are in fair agreement with measurements (Hoose et al.
subm.). Note that the concentrations shown here are total aerosol concentrations, not IN
concentrations, and that only a small subset of all aerosol particles serves as ice nuclei.
3) Ice nucleation rates
Figs. 4, 5 and 6 display the zonal annual mean freezing rates (i. e.
1
∆Ni
∆t
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 dominating 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-
21
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 which 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, only 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 −38◦ C 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 (and that means the anthropogenic)
contribution to heterogeneous ice nucleation would increase.
The main difference from the partitioning of the freezing processes as simulated in
ECHAM5-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 low frequency of contact freezing events.
22
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), 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 − 20s (depending on the instrument setup), the
particles which have grown to ice crystals larger than 1 µm are optically detected. While the
CFDC has the advantage of allowing real-time airborne measurements, some limitations have
to be accounted for. Due to the short residence time, the dominant ice nucleation modes
in the CFDC are deposition and condensation nucleation. The largest aerosol particles
(> 1.2 − 2 µm 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
23
(termed ‘model IN(10s)’) has been defined as a 10 s integral over the time-step mean, incloud freezing rates (sum over Eqs. (7), (12) and (15), multiplied by 10s/∆t). 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 4). These upper bounds are
accounted for in the values of the ‘model IN(10s)’.
Fig. 8 shows the simulated ‘model IN(10s)’ concentrations as a function of temperature,
sampled at all global gridpoints at an arbitrary timestep. The simulated IN(10s) concentrations attain significant values at temperatures below −11◦ C and increase strongly with
decreasing temperature until around −20◦ C. In this temperature range, ‘model IN(10s)’
concentrations are mostly between 0.5 and 20 L−1 . 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 grid-point temperature. Therefore the
model ice nuclei concentrations for lower temperatures tend to be valid for higher altitudes
and latitudes, where also the aerosol concentration is in general lower.
For a more detailed comparison, this analysis is repeated for the gridboxes closest to the
CFDC measurement locations (Fig. 9). Fort Collins and Storm Peak fall into the same global
24
model gridbox, but we have selected data from different vertical levels to account for the
altitude of the Storm Peak laboratory (3200m). The data at Barrow, Alaska were collected
by aircraft within the lowest 2000m 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 2◦ -intervals from −6 to −38◦ C 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 −12 to −24◦ C and then flatten off or even decrease again.
This behavior at T < −24◦ C is consistent with the observations at Storm Peak. The strong
temperature dependence at T > −20◦ C is not confirmed by the Fort Collins and Barrow
data, which are more scattered. At Storm Peak and at Barrow for T < −15◦ C, the observed
data fall between the 25th and 75th percentiles of the simulated data. The majority of
observations at Fort Collins shows higher IN concentrations than simulated, and this is also
true for T > −15◦ C at Barrow. Not much 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
25
the square of the particle radius (Eq. (1)). Fig. 10 displays the ‘model IN(10s)’ concentration versus the concentration of aerosol particles with diameter > 0.5 µm. If sampled at
all temperatures (Fig. 10(a)), only a modest correlation is obtained, due to low IN concentrations at warm subzero temperatures. But if sampled only at T < −20◦ C (Fig. 10(b)),
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 µm) which 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 which 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 which have nucleated ice in a CFDC can be analyzed, under the limitations of the CFDC measurements as
discussed above. Table 6 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
26
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
dominating 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 simulation, with the new freezing parameterization and additional contributions by biological IN, this distribution remains very
similar: 82 and 18%. 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 rainforest canopy, where temperatures are always above 0◦ C and ice nuclei can not
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 66◦ N), an enhancement of the mineral dust component (to 90%)
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
27
comparisons are difficult to infer from the observations listed in Table 6 due to differences
in sampling and instrumentation.
The results of the sensitivity studies (also listed in Table 6) demonstrate the sensitivity
of the model to assumptions entering the ice nucleation parameterization. In the simulation
CNT, global ice nucleation is split between mineral dust, soot and biological particles to 82,
18 and 10−7 %, respectively. The biological IN fraction is increased to 9 in 107 particles in
the ‘CNT-highbact’ simulation. In the ‘CNT-lowdust’ simulation, mineral dust contributes
only to 46% of the ice nucleation, while the soot fraction is raised to 54%. This partitioning
is in worse agreement with the observations than the other simulations, as all field studies
listed in Table 6 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 ‘CNTnosootct’ simulation. Finally, without biological particles as IN (simulation ‘CNT-nobio’),
the partioning of heterogeneous freezing between mineral dust and soot is very similar to
that in simulation CNT.
In summary, 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 that 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, remains to be solved.
28
c. Aerosol indirect effect
In Table 5, the global mean differences between the present-day and preindustrial cloud
and radiative properties are listed. The model includes direct, semi-direct, 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 de-activation indirect effect in mixed-phase clouds (Lohmann and Hoose
2009)) counteract each other. This complicates interpretation of the resulting net effect.
In the CTL simulation, the change in top-of-the-atmosphere net radiation (Fnet ) is
−1.68 ± 0.09 W m−2 (5 year average with standard error), which is less negative than if
only warm-phase indirect effects are included (Hoose et al. 2009, −2.1 W m−2 ). The differences compared to Storelvmo et al. (2008b, 2010) stem from model updates in the warmphase physics. In simulation CNT, ∆Fnet slightly more negative (−1.76 ± 0.17 W m−2 )
than in CTL, probably due to a different vertical distribution of cloud liquid water and a
lower glaciation indirect effect, which tends to counteract the indirect effect of warm clouds
(Lohmann 2002). This hypothesis is confirmed by the even more negative indirect effect in
the CNT-nosootct simulation (−1.84 ± 0.11 W m−2 ), 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 LW P 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 ∆Fnet is found. This can be explained on
29
the one hand by a higher LWP in the CNT-nobio simulation, 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 CNT-highbact, thus less potential for anthropogenic soot to
trigger cloud glaciation.
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 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
30
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, even less 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, 82% of the simulated heterogeneous nucleation is initiated by mineral dust particles, 18% by soot, while biological particles
only contribute a fraction of 10−7 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
on the probability of bacteria and fungal spores to act as ice nuclei, the biological aerosol
contribution to global freezing remains marginal, due to 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. subm.). However, we can not rule out local importance of biological particles
nor 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.
31
The simulated ice nuclei concentrations are compared to CFDC measurements, and in a
statistical sense a good agreement is found. At temperatures below −20◦ C, 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 can not 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, like volcanic ash and anthropogenic metallic particles, can
not 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 change in dust emissions. We suggest that further laboratory and
field experiments are mandatory, in order to obtain a larger data base for improved modeling
studies.
32
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 No. 178246), EUCAARI
(European Integrated project No. 036833-2) and POLARCAT (Norwegian Research Council
grant No. 460724), and computing time was provided through a grant from the Norwegian
Research Council’s program for Supercomputing.
33
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49
List of Tables
1
Simulation descriptions.
52
2
List of symbols.
53
3
Table 2 continued.
54
4
Parameters for the ice nucleation parameterization, derived from laboratory
data.
5
55
Global annual mean cloud cover (CC), liquid water path (LW P ), ice water path (IW P ), short-wave cloud forcing (SW CF ), long-wave cloud forcing
(LW CF ) and net radiation at the top of the atmosphere (Fnet ) for the presentday 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).
50
56
6
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 + sulfates/salts” and “metal oxides/dust
+ carbonaceous” particles sampled by Prenni et al. (2009a) and “dust” and
“dust + 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
57
biological particles.
51
Table 1. Simulation descriptions.
simulation
description
CTL
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, 2010).
Freezing parameterizations after Lohmann and Diehl (2006).
CNT
as CTL, but with the new freezing parameterizations from classical nucleation
theory.
CNT-highbact as CNT, but with a higher fraction of bacteria assumed to be ice-nucleation
active.
CNT-lowdust as CNT, but with higher wet scavenging of dust.
CNT-nosootct as CNT, but without contact freezing by soot.
CNT-nobio
as CNT, but without freezing by biological aerosol particles.
52
symbol
A0 [m−2 s−1 ]
A0dep [m−2 s−1 ]
A0imm [m−2 s−1 ]
aw [#]
e [Pa]
esi [Pa]
esw [Pa]
f [#]
fi,max,x [#]
fl,x [#]
fx,coated [#]
∆g # [J]
#
∆gd,dep
[J]
◦
∆gg [J]
◦
∆gg,dep
[J]
◦
∆gg,imm [J]
h [J s]
J [s−1 ]
Jcontact [s−1 ]
Jdep [s−1 ]
Jimm [s−1 ]
Kcoll [m−3 s−1 ]
k [J K−1 ]
mw [kg]
Naer,x [m−3 ]
Ng,contact [#]
Ni [m−3 ]
∆Ni,contact [m−3 ]
∆Ni,dep [m−3 ]
∆Ni,imm [m−3 ]
Nl [m−3 ]
n1,w [m−2 ]
rg [m]
rg,dep [m]
rg,imm [m]
rN [m]
rN,x [m]
Si [#]
T [K]
Tc [◦ C]
t [s]
∆t [s]
vw [m3 ]
x [#]
Table 2. List of symbols.
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 4)
activation energy for deposition nucleation (Table 4)
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 (6.63 · 10−34 J s)
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 (1.38 · 10−23 J K−1 )
mass of a water molecule (2.99 · 10−26 kg)
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) (1019 m−2 (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 in ◦ C
time
model time step (1800 s)
volume of a water molecule in ice (= mw /ρi )
53
aerosol species index
symbol
θ [◦ ]
θdep [◦ ]
θimm [◦ ]
νs [1/s]
ρi [kg m−3 ]
σi/v [J m−2 ]
σi/w [J m−2 ]
Table 3. Table 2 continued.
description
contact angle (Table 4)
contact angle for deposition nucleation (Table 4)
contact angle for immersion freezing (Table 4)
frequency of vibration of water vapor molecule adsorbed on solid
substrate, 1013 s−1 (Pruppacher and Klett 1997, p. 299)
density of ice (= 916.7−0.175Tc −5·10−4 Tc2 (Pruppacher and Klett
1997, Eq. (3-2)))
surface tension between ice and vapor (= σi/w + σw/v = (76.1 −
0.155 ∗ Tc + 28.5 + 0.25 ∗ Tc ) · 10−3 (Pruppacher and Klett 1997, Eq.
(5-46), (5-47a) and (5-12))
surface tension between ice and water (= (28+0.25Tc )·10−3 (Pruppacher and Klett 1997, Eq. (5-47a)))
54
Table 4. Parameters for the ice nucleation parameterization, derived from laboratory data.
aerosol
reference
nucleation mode θ in ◦ ∆g # in 10−20 J fi,max,x
soot
DeMott (1990)
immersion
40.17
14.4
0.01
dust
montmorillonite (Pit- immersion
30.98
15.7
1.0
ter and Pruppacher
1973)
1% of all bacte- Pseudomonas
sy- immersion
14.82
17.6
0.001
ria
ringae
(Yankofsky
et al. 1981)
1% of all fungal same as bacteria
immersion
14.82
17.6
0.001
spores
pollen
birch (Diehl et al. immersion
25.16
17.3
1.0
2002)
soot
Möhler et al. (2005)
deposition
28.00
-20.0
0.01
dust
illite (Zimmermann deposition
12.70
-0.621
1.0
et al. 2008)
55
Table 5. Global annual mean cloud cover (CC), liquid water path (LW P ), ice water
path (IW P ), short-wave cloud forcing (SW CF ), long-wave cloud forcing (LW CF ) and net
radiation at the top of the atmosphere (Fnet ) 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).
CTL
CNT
CNTCNTCNTCNThighbact lowdust nosootct nobio
CC [%]
64.9±0.1
64.7±0.1
64.7
64.9
64.6
64.8
∆CC [%]
-0.2±0.1
+0.0±0.1
+0.0
-0.2
-0.3
+0.0
LW P [g m−2 ]
109.3±0.1 103.5±0.3
103.1
108.8
103.6
104.0
∆LW P [g m−2 ]
4.11±0.25 4.69±0.36
4.89
4.64
4.68
5.09
IW P [g m−2 ]
31.0±0.04 29.8±0.04
29.8
30.4
29.8
29.8
−2
∆IW P [g m ]
+0.12±0.08 -0.02±0.07
+0.01
-0.19
-0.05
+0.05
−2
SW CF [W m ]
-57.7±0.02 -56.4±0.08
-56.4
-57.5
-56.5
-56.6
∆SW CF [W m−2 ] -1.30±0.07 -1.68±0.10
-1.75
-1.59
-1.65
-1.80
−2
LW CF [W m ]
32.3±0.05 31.6±0.02
31.5
32.3
31.6
31.6
∆LW CF [W m−2 ] -0.38±0.09 -0.17±0.05
-0.30
-0.37
-0.26
-0.16
Fnet [W m−2 ]
0.13±0.04 0.73±0.11
0.65
0.86
0.70
0.61
−2
∆Fnet [W m ]
-1.68±0.09 -1.76±0.17
-2.03
-1.98
-1.84
-1.91
56
Table 6. 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 + sulfates/salts” and “metal oxides/dust
+ carbonaceous” particles sampled by Prenni et al. (2009a) and “dust” and “dust + 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.
Mineral dust Carbonaceous Soot Biological Other
Phillips et al. (2008),
0.52
0.37 n/a
n/a
0.11
from 6 campaigns
Prenni et al. (2009a),
0.64
0.35 n/a
n/a
0.01
MPACE, Alaska, US
Prenni et al. (2009a),
0.64
0.17 n/a
n/a
0.19
SHEBA/FIRE-ACE, Arctic
Ocean
Prenni et al. (2009b),
0.50
0.47 n/a up to 0.47
0.03
Amazon basin
Pratt et al. (2009),
0.50
0.41 0.04
0.33
0.09
Wyoming, US
Targino et al. (2006),
0.58
0.23 n/a
n/a
0.19
Scandinavia
Kumai (1961),
0.57
0.09 0.08
0.01
0.34
Hokkaido, Japan
Kumai (1961),
0.88
0.04 0.04
0.00
0.08
Honshu, Japan
Kumai (1961),
0.87
0.02 0.02
0.00
0.11
Michigan, US
Kumai and Francis (1962),
0.85
0.00 0.00
n/a
0.15
Greenland
Simulation CTL, global
0.84
0.16 0.16
0.00
0.00
Simulation CNT, global
0.82
0.18 0.18
1e-7
0.00
Simulation CNT, Arctic
0.90
0.10 0.10
1e-7
0.00
Simulation CNT-highbact,
0.82
0.18 0.18
6e-7
0.00
global
Simulation CNT-lowdust,
0.45
0.55 0.55
3e-7
0.00
global
Simulation CNT-nosootct,
0.88
0.12 0.12
1e-7
0.00
global
Simulation
CNT-nobio,
0.82
0.18 0.18
0.00
0.00
global
57
List of Figures
1
Parameterized immersion freezing rates for soot (for a particle radius rN of
40 nm, as in the experiment), montmorillonite dust (750 nm), birch pollen
(12.5 µm) and Pseudomonas syringae bacteria (500 nm). The crosses indicate freezing rates derived from measurements, see Table 4. 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
60
accordingly.
2
Isolines of constant parameterized deposition nucleation rate (Jdep ) for illite
dust (particles radius of 2.5 µm) and soot (90 nm). The red crosses indicate the
onset of nucleation in measurements, see Table 4. Black lines: parameterized
deposition nucleation. Red line: simulated isoline of the deposition nucleation
rate Jdep which corresponds to the observed data. Blue lines: isolines of
constant contact nucleation Jcnt , assuming a collision rate of 10−3 s−1 . The
blue lines correspond to nucleation rates of 10−6 , 10−3 and 10−2 s−1 , and
the black lines to nucleation rates of 10−6 , 10−3 , 10−2 , 10−1 and 1 s−1 (from
61
bottom to top).
3
Zonal annual mean particle number concentrations in simulation CNT. Note
the two different colorbars for the upper and lower figures.
62
4
1
Zonal annual mean immersion freezing rates ( ∆t
∆Ni,imm ) in simulation CNT.
63
5
1
Zonal annual mean deposition nucleation rates ( ∆t
∆Ni,dep ) in simulation CNT. 64
6
1
Zonal annual mean contact freezing rates ( ∆t
∆Ni,contact ) in simulation CNT.
58
65
7
Global annual mean vertically integrated nucleation rates in simulation CNT.
8
Ice nuclei concentrations (calculated as 10s-integrals over the freezing rate),
66
sampled at all global gridpoints at an arbitrary timestep of simulation CNT
(small black dots). The colored symbols represent CFDC IN measurements
at various locations.
9
67
Ice nuclei concentrations for specified temperatures (calculated as 10s-integrals
over the freezing rate), sampled at the gridpoints closest to the measurement
locations, at 10 arbitrary timesteps 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
68
represent CFDC IN measurements.
10
Ice nuclei concentrations (calculated as 10s-integrals over the freezing rate)
in simulation CNT, displayed as a function of the number concentrations of
aerosol particles with d > 0.5µm. (a) all temperatures, (b) T ≤ −20◦ C.
Dashed lines: power-law fit to observations (DeMott et al. 2006; Georgii and
Kleinjung 1967, (d > 0.6µm, T= −21◦ C)).
59
69
Fig. 1. Parameterized immersion freezing rates for soot (for a particle radius rN of 40 nm, as
in the experiment), montmorillonite dust (750 nm), birch pollen (12.5 µm) and Pseudomonas
syringae bacteria (500 nm). The crosses indicate freezing rates derived from measurements,
see Table 4. 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.
60
Fig. 2. Isolines of constant parameterized deposition nucleation rate (Jdep ) for illite dust
(particles radius of 2.5 µm) and soot (90 nm). The red crosses indicate the onset of nucleation
in measurements, see Table 4. Black lines: parameterized deposition nucleation. Red line:
simulated isoline of the deposition nucleation rate Jdep which corresponds to the observed
data. Blue lines: isolines of constant contact nucleation Jcnt , assuming a collision rate of
10−3 s−1 . The blue lines correspond to nucleation rates of 10−6 , 10−3 and 10−2 s−1 , and the
black lines to nucleation rates of 10−6 , 10−3 , 10−2 , 10−1 and 1 s−1 (from bottom to top).
61
Fig. 3. Zonal annual mean particle number concentrations in simulation CNT. Note the
two different colorbars for the upper and lower figures.
62
1
Fig. 4. Zonal annual mean immersion freezing rates ( ∆t
∆Ni,imm ) in simulation CNT.
63
1
Fig. 5. Zonal annual mean deposition nucleation rates ( ∆t
∆Ni,dep ) in simulation CNT.
64
1
Fig. 6. Zonal annual mean contact freezing rates ( ∆t
∆Ni,contact ) in simulation CNT.
65
Fig. 7. Global annual mean vertically integrated nucleation rates in simulation CNT.
66
Fig. 8. Ice nuclei concentrations (calculated as 10s-integrals over the freezing rate), sampled
at all global gridpoints at an arbitrary timestep of simulation CNT (small black dots). The
colored symbols represent CFDC IN measurements at various locations.
67
Fig. 9. Ice nuclei concentrations for specified temperatures (calculated as 10s-integrals
over the freezing rate), sampled at the gridpoints closest to the measurement locations, at
10 arbitrary timesteps 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.
68
Fig. 10. Ice nuclei concentrations (calculated as 10s-integrals over the freezing rate) in
simulation CNT, displayed as a function of the number concentrations of aerosol particles
with d > 0.5µm. (a) all temperatures, (b) T ≤ −20◦ C. Dashed lines: power-law fit to
observations (DeMott et al. 2006; Georgii and Kleinjung 1967, (d > 0.6µm, T= −21◦ C)).
69