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  • Yankofsky, S. A., , Z. Levin, , T. Bertold, , and N. Sandlerman, 1981: Some basic characteristics of bacterial freezing nuclei. J. Appl. Meteor., 20 , 10131019.

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  • Young, K. C., 1974: The role of contact nucleation in ice phase initiation in clouds. J. Atmos. Sci., 31 , 768776.

  • Zimmermann, F., , S. Weinbruch, , L. Schütz, , H. Hofmann, , M. Ebert, , K. Kandler, , and A. Worringen, 2008: Ice nucleation properties of the most abundant mineral dust phases. J. Geophys. Res., 113 , D23204. doi:10.1029/2008JD010655.

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    Parameterized immersion freezing rates for soot (for rN = 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 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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    Isolines of Jdep for illite dust (rN = 2.5 μm) 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 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).

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    Zonal annual mean particle number concentrations in simulation CNT. Note the two different color bars for the top and bottom rows.

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    Zonal annual mean immersion freezing rates (ΔNi,immt) in simulation CNT.

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    Zonal annual mean deposition nucleation rates (ΔNi,dept) in simulation CNT.

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    Zonal annual mean contact freezing rates (ΔNi,contactt) in simulation CNT.

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    Global annual mean vertically integrated nucleation rates in simulation CNT.

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    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.

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    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.

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    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 μm, for (a) all T and (b) T ≤ −20°C. The dashed lines are power-law fit to observations (DeMott et al. 2006; Georgii and Kleinjung 1967); d > 0.6 μm, T = −21°C.

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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

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  • 1 Department of Geosciences, University of Oslo, Oslo, Norway
  • | 2 Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
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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 0° and −38°C. 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 −20°C 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 10−5% biological particles) lies in the range of observed ice nuclei and ice crystal residue compositions.

* Current affiliation: Institute for Meteorology and Climate Research (IMK-AAF), Karlsruhe Institute of Technology, Karlsruhe, Germany

+ 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. Email: corinna.hoose@kit.edu

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 0° and −38°C. 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 −20°C 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 10−5% biological particles) lies in the range of observed ice nuclei and ice crystal residue compositions.

* Current affiliation: Institute for Meteorology and Climate Research (IMK-AAF), Karlsruhe Institute of Technology, Karlsruhe, Germany

+ 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. Email: corinna.hoose@kit.edu

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° 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 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 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; 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 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 −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 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 −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 approximately −15°C.

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.

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 μm for bacteria (Burrows et al. 2009), 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), 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 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 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 (2.8125° × 2.8125°) with 26 vertical levels. The simulations are integrated for 5 yr after 4 months of spinup, for both 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). 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.

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.

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
i1520-0469-67-8-2483-e1
where A′ 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, Δg# is the activation energy (aerosol dependent and with different values for immersion and deposition nucleation), Δg°g 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
i1520-0469-67-8-2483-e2
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
i1520-0469-67-8-2483-e3
The parameters contained here (see also Tables 2 and 3) are the volume of a water molecule υw, 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:
i1520-0469-67-8-2483-e4
In Eq. (1), which includes parts of the Zeldovich factor and the molecule flux toward the ice germ, A′ can be parameterized from ambient parameters:
i1520-0469-67-8-2483-e5
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:
i1520-0469-67-8-2483-e6
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 = fl,fungi = fl,pollen = 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 Δt, we obtain
i1520-0469-67-8-2483-e7
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 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 ≲ 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, , and Aimm as specified in Eqs. (3)(5). Also included in Fig. 1 are the measurements used to derive θ and Δg#. The freezing onset, defined as Jimm > 10−5 s−1, is approximately −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 by analogy to immersion freezing. For a detailed derivation, see Chen et al. (2008). The critical germ size rg,dep, , and Adep are functions of the temperature and of the water vapor pressure e (equivalent: the supersaturation over ice Si = e/esi):
i1520-0469-67-8-2483-e8
i1520-0469-67-8-2483-e9
i1520-0469-67-8-2483-e10
Here σi/υ is the surface tension between ice and water vapor, mw is the mass of a water molecule, and νs 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 TSi 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 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:
i1520-0469-67-8-2483-e11
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 Δt, we obtain
i1520-0469-67-8-2483-e12
The fraction of particles acting as deposition nuclei per model time step of 40 min 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 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 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
i1520-0469-67-8-2483-e13

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. We evaluate f for f (θdep, rg,imm).

The contact nucleation rate is given by the collision rate between droplets and aerosols that contain at least one contact nucleation germ. As in the case of deposition nucleation, only uncoated, nonactivated 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 x. As the biological particles are assumed to be fully activated to cloud droplets, they do not contribute to contact nucleation.
i1520-0469-67-8-2483-e14

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 Δt:
i1520-0469-67-8-2483-e15
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 10−3 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 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 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 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. 2010). 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

Figures 4, 5, and 6 display the zonal annual mean freezing rates [i.e., ΔNit 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 −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 (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), 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 μm 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 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 [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/Δt]. 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 −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 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 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 show 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 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 μm. 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 < −20°C (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 μm) 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 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 0°C 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 66°N), 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 10−7%, respectively. The biological IN fraction is increased to 5 in 107 particles in the CNT-highbact 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 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-the-atmosphere net radiation Fnet is −1.68 ± 0.09 W m−2 (5-yr 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) stem from model updates in the warm-phase physics. In simulation CNT, ΔFnet is slightly less negative (−1.55 ± 0.17 W m−2) 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 (−1.89 ± 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 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 ΔSWCF 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 CNT-highbact (and 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 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 heterogeneous nucleation is initiated by mineral dust particles and 23% 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 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 −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 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 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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