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    Annual and zonal mean ice water content (mg m−3) predicted by (a) the modified CAM (simulation ICE) and (b) standard CAM (simulation REF), and that measured by (c) the Aura MLS instrument.

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    Ice water content profiles from observation data at the ARM SGP site, and from the CAM simulations REF and ICE.

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    A comparison of normalized occurrence frequency of RHi in two layers of the upper troposphere (200–300 and 300–500 hPa) obtained from the two model simulations (REF and ICE) and the MOZAIC data. Data were sampled every 3 h in the CAM grid cells (2.5° × 2°) within flight tracks.

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    (a) Grid-averaged ice crystal mass mixing ratio (mg kg−1), (b) grid-averaged number concentrations (g−1), (c) air temperature (°C), and (d) relative humidity (%; RHw for T > 0°C, and RHi for T ≤ 0°C) in January predicted with the modified CAM3 model (simulation ICE).

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    January zonal averaged cloud ice number source terms from (a) ice nucleation, (b) convective detrainment, (c) contact freezing of cloud droplets, and (d) secondary ice production, and the loss terms from (e) precipitation and (f) cloud evaporation.

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    Zonal mean effective radius (μm) of ice crystals in January from (a) simulation ICE and (b) simulation REF. Effective radius is weighted by the cloud ice mixing ratio when averaged zonally to reflect the larger contribution from optically thicker clouds.

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    Annual zonal mean latitude vs pressure cross sections of temperature (K), specific humidity (g kg−1), cloud cover (%), and cloud ice mass mixing ratio (mg kg−3) for simulations REF and ICE, and the difference, ICE – REF.

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    Annual and zonal means of shortwave and longwave cloud forcing from the two simulations REF and ICE as compared to the ERBE data.

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    Annual averaged and vertically integrated ice crystal number concentration (106 m−2) for simulations (a) ICE, (b) SIG-W, and (c) RHI.

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    Annual zonal mean latitude vs pressure cross sections of temperature (K), specific humidity (g kg−1), cloud cover (%), and cloud ice mass mixing ratio (mg kg−3) for simulation ICE and the differences, SIG-W – ICE and RHI – ICE.

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    Annual zonal mean latitude vs pressure cross sections of (a) ice number absolute difference (g−1), (b) specific humidity relative difference (%), and (c) temperature absolute difference (K) between the two simulations RHI and ICE.

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Inclusion of Ice Microphysics in the NCAR Community Atmospheric Model Version 3 (CAM3)

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  • 1 Pacific Northwest National Laboratory, Richland, Washington
  • 2 Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan
  • 3 Pacific Northwest National Laboratory, Richland, Washington
  • 4 Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan
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Abstract

A prognostic equation for ice crystal number concentration together with an ice nucleation scheme are implemented in the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 3 (CAM3) with the aim of studying the indirect effect of aerosols on cold clouds. The effective radius of ice crystals, which is used in the radiation and gravitational settlement calculations, is now calculated from model-predicted mass and number of ice crystals rather than diagnosed as a function of temperature. A water vapor deposition scheme is added to replace the condensation and evaporation (C–E) in the standard CAM3 for ice clouds. The repartitioning of total water into liquid and ice in mixed-phase clouds as a function of temperature is removed, and ice supersaturation is allowed. The predicted ice water content in the modified CAM3 is in better agreement with the Aura Microwave Limb Sounder (MLS) data than that in the standard CAM3. The cirrus cloud fraction near the tropical tropopause, which is underestimated in the standard CAM3 as revealed through comparison with the Stratospheric Aerosol and Gas Experiment II (SAGE II) data, is increased by 20%–30%, and the cold temperature bias there is reduced by 1–2 K. However, an increase in the cloud fraction in polar regions makes the underestimation (by ∼20 W m−2) of downwelling shortwave radiation in the standard CAM3 even worse. A sensitivity test reducing the threshold relative humidity with respect to ice (RHi) for heterogeneous ice nucleation from 120% to 105% (representing nearly perfect ice nuclei) increases the global cloud cover by 1.4%, temperature near the tropical tropopause by 4–5 K, and water vapor in the stratosphere by 50%–80%.

Corresponding author address: Xiaohong Liu, Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, 3200 Q Ave., MSIN K9-24, Richland, WA 99352. Email: Xiaohong.Liu@pnl.gov

Abstract

A prognostic equation for ice crystal number concentration together with an ice nucleation scheme are implemented in the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 3 (CAM3) with the aim of studying the indirect effect of aerosols on cold clouds. The effective radius of ice crystals, which is used in the radiation and gravitational settlement calculations, is now calculated from model-predicted mass and number of ice crystals rather than diagnosed as a function of temperature. A water vapor deposition scheme is added to replace the condensation and evaporation (C–E) in the standard CAM3 for ice clouds. The repartitioning of total water into liquid and ice in mixed-phase clouds as a function of temperature is removed, and ice supersaturation is allowed. The predicted ice water content in the modified CAM3 is in better agreement with the Aura Microwave Limb Sounder (MLS) data than that in the standard CAM3. The cirrus cloud fraction near the tropical tropopause, which is underestimated in the standard CAM3 as revealed through comparison with the Stratospheric Aerosol and Gas Experiment II (SAGE II) data, is increased by 20%–30%, and the cold temperature bias there is reduced by 1–2 K. However, an increase in the cloud fraction in polar regions makes the underestimation (by ∼20 W m−2) of downwelling shortwave radiation in the standard CAM3 even worse. A sensitivity test reducing the threshold relative humidity with respect to ice (RHi) for heterogeneous ice nucleation from 120% to 105% (representing nearly perfect ice nuclei) increases the global cloud cover by 1.4%, temperature near the tropical tropopause by 4–5 K, and water vapor in the stratosphere by 50%–80%.

Corresponding author address: Xiaohong Liu, Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, 3200 Q Ave., MSIN K9-24, Richland, WA 99352. Email: Xiaohong.Liu@pnl.gov

1. Introduction

Cirrus clouds composed of ice crystals have an annual global average frequency of occurrence of about 30% (e.g., Wylie and Menzel 1999; Wang et al. 1996; Rossow and Schiffer 1999). Cirrus clouds scatter shortwave (SW) radiation and absorb and emit longwave (LW) terrestrial radiation, thereby modifying the global radiative balance (Liou 1986; Ramanathan and Collins 1991). Furthermore, because tropospheric air enters the stratosphere predominantly in the Tropics by upward transport, the formation of tropical cirrus layers influences the stratospheric water vapor content (e.g., Danielsen 1993; Potter and Holton 1995). Cirrus clouds have also been invoked as a possible surface for heterogeneous reactions that could impact ozone concentrations in the upper troposphere and lower stratosphere (Borrmann et al. 1996).

Ice formation in cirrus clouds may result from both homogeneous freezing of solution droplets formed on soluble cloud condensation nuclei (CCN) at temperatures less than −37°C and heterogeneous freezing of ice by insoluble or partially insoluble particles. Homogeneous nucleation requires supersaturations with respect to ice in excess of 40% to freeze sulfate haze droplets at upper-tropospheric temperatures (Koop et al. 1998; Bertram et al. 2000). Homogeneous freezing is independent of the chemical nature of the solution and only depends on the water activity of the solution droplets (Koop et al. 2000). Heterogeneous nucleation involves aerosol particles that serve as a substrate that induces ice nucleation. Heterogeneous mechanisms include direct deposition from vapor to ice on a suitable nucleus (deposition nucleation) and freezing of previously condensed supercooled cloud or haze droplets, with the freezing initiated either by contact of a nuclei with the cloud or haze droplet (contact nucleation) or by a nuclei immersed within the cloud or haze droplet (immersion nucleation).

There is still a shortage of information on concentrations and properties of ice nuclei (IN) in the upper troposphere. Measurements during the National Aeronautics and Space Administration (NASA) Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE) confirm that mineral dust appears to be common heterogeneous IN (Sassen et al. 2003; DeMott et al. 2003; Cziczo et al. 2004). Analyses of ice-nucleating aerosols collected in the upper troposphere during the Subsonic Aircraft: Contrail and Cloud Effects Special Study (SUCCESS) showed that carbonaceous and crustal particles dominated the number fraction of IN (Chen et al. 1998). Laboratory studies of cirrus ice formation that relate ice nucleation to aerosol properties revealed that heterogeneous ice nucleation on mineral dust (e.g., Zuberi et al. 2002; Hung et al. 2003; Archuleta et al. 2005; Field et al. 2006; Möhler et al. 2006; Salam et al. 2006), and on soot particles (DeMott 1990; DeMott et al. 1999; Gorbunov et al. 2001; Möhler et al. 2005), requires lower relative humidity over ice (RHi) than homogeneous freezing on sulfate.

The anthropogenic component of sulfate and carbonaceous aerosols has substantially increased the global mean aerosol burden from preindustrial times to the present day. Observations during SUCCESS suggest significant surface sources of upper-tropospheric sulfate as well (Dibbs et al. 1998), raising the possibility of an indirect aerosol effect on cirrus clouds. Aircraft exhaust may influence cloud formation either directly by forming contrails or indirectly by causing an aerosol of black carbon soot, volatile particles, and metallic particles that later impacts the formation and properties of natural ice clouds (Penner et al. 1999; Schumann 2002; Jensen and Toon 1997). In a high-traffic region, cirrus was found to be affected by soot emission from aircraft, causing an approximate doubling of the ice particle number concentration (Ström and Ohlsson 1998). Kristensson et al. (2000) observed that the effective crystal diameter decreased by 10%–30% in cirrus clouds perturbed by aircraft. If cirrus cloud occurrence frequency and microphysical characteristics, such as number density, size, and shape, of the ice crystals are modified by human-related activities, this can have a large influence on the radiative properties of the clouds (Stephens et al. 1990; Smith et al. 1998), and hence the vertical profiles of radiative heating, which can also have big impacts on the large-scale model results. The size of the crystals determines their sedimentation velocity and therefore the cloud lifetime and the humidity of the upper troposphere, and also the humidity of the stratosphere, as this is partly controlled by the dehydration potential of cirrus clouds (Jensen et al. 2001).

While the influence of aerosols on cloud droplet nucleation has received considerable attention from the climate research community, the connection between aerosols and cirrus clouds is considered to be too uncertain to even speculate on whether it would be a positive or negative radiative climate forcing (Penner et al. 2001). The first global models dealing with ice nucleation (e.g., Ghan et al. 1997b) employed a temperature-dependent formulation (e.g., Fletcher 1962) or supersaturation-dependent formulation (e.g., Huffman 1973; Meyers et al. 1992) or a combination of both (Cotton et al. 1986) to determine ice number concentration. These formulations are derived from measurements near the surface, and thus, could overpredict ice crystal number concentrations in cold cirrus clouds where aerosol loadings are much lower. Also, these formulations do not provide links to the aerosol physical and chemical properties and air dynamics driving ice nucleation. Therefore, it is not possible to use these formulations in a global model to study the effects of anthropogenic aerosols such as soot particles from aircraft on cirrus clouds.

Kärcher and Lohmann (2002a, b, 2003) developed physically based parameterizations for the homogeneous freezing of sulfate solution droplets and for heterogeneous immersion freezing in cirrus formation. In contrast to previous parameterizations, their schemes consider the dependence of ice number concentration on temperature and updraft velocity. These parameterizations were implemented in the ECHAM general circulation model (GCM) to simulate the cirrus clouds formed by homogeneous freezing (Lohmann and Kärcher 2002) and to study the effects of heterogeneous freezing on cirrus clouds and climate (Lohmann et al. 2004). Liu and Penner (2005) derived a parameterization based on first principles for the homogeneous nucleation of sulfate and heterogeneous immersion nucleation on soot in cirrus clouds and considered the competition between these two nucleation mechanisms. Kärcher et al. (2006) also developed a parameterization that allows for the competition between heterogeneous nucleation of ice nuclei and homogeneous freezing of liquid aerosol particles. Unlike the Kärcher et al. (2006) scheme, which requires the time integration of water vapor saturation ratio, ice nucleation, and ice crystal growth over the time period associated with the GCM time step (e.g., 20–30 min), the Liu and Penner (2005) scheme explicitly gives the relationship between ice number and sulfate and soot number, updraft velocity, and air temperature, and thus is computationally faster and more practical for multiyear climate simulations. It is noted that although Lohmann and Kärcher (2002), Liu and Penner (2005), and Kärcher et al. (2006) are improvements in parameterizations of ice microphysics, there is still great uncertainty in our physical understanding of these processes and in our representation of them in models.

In this paper, we apply the Liu and Penner (2005) ice nucleation parameterization to the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 3 (CAM3). A prognostic equation for the number concentration of ice crystals is also implemented. To apply the ice nucleation parameterization, the saturation adjustment scheme used in the standard CAM3 is abandoned. We also eliminate the diagnosis of the ice water portion from the predicted total water content as a function of temperature by introducing a treatment of water vapor deposition on ice crystals. The ice crystal effective radius, which is used in the cloud radiation and gravitational settling calculations, is now calculated from the predicted mass and number rather than diagnosed from temperature.

The changes to CAM3 are discussed in section 2. Validation with observations and the effect of these changes on cloud properties and climate are discussed in section 3. Sensitivities of the model results to the subgrid updraft velocity for ice nucleation, and to the heterogeneous ice nucleation threshold are investigated in section 4. Section 5 summarizes the results and concludes this paper.

2. Model description

a. Standard NCAR CAM3

The NCAR CAM3 is the atmospheric component of the Community Climate System Model (CCSM; Collins et al. 2006a, b). CAM3 includes Eulerian spectral and semi-Langrangian dynamics and finite volume (FV) dynamics (Lin and Rood 1996). The finite volume dynamics at 2° × 2.5° horizontal resolution is used in this study. The treatment of cloud condensation and microphysics in CAM3 (Boville et al. 2006) is based on Rasch and Kristjánsson (1998) as updated by Zhang et al. (2003) with separate prognostic equations for the liquid and ice-phase condensate. Condensed water is detrained from both shallow and frontal convection and added to the stratiform cloud water. Advection and sedimentation of cloud droplets and ice particles are included in the equations governing the cloud condensates. The settling velocities for liquid and ice-phase constituents are computed separately as a function of particle size, which is characterized by the effective radius. Cloud fraction is derived from diagnostic relationships for the amounts of low-level marine stratus (Klein and Hartmann 1993), shallow and deep convection (Xu and Krueger 1991), and layered cloud systems (Sundqvist 1988). The layered cloud fraction fs is diagnosed from relative humidity as
i1520-0442-20-18-4526-e1
where the threshold relative humidity (RHmin) is an adjustable constant that depends on pressure, and RH is a hybrid relative humidity that is also used for model physics (e.g., radiation, cloud microphysics, and precipitation evaporation). Relative humidity with respect to water (RHw) is used for T > 0°C, relative humidity with respect to ice (RHi) is used for T < −20°C, and the relative humidity for temperatures between −20° and 0°C is calculated using a vapor pressure that is linearly interpolated between water saturation and ice saturation. Even though each phase of water is transported separately, after advection, convective detrainment, and sedimentation, the liquid and ice are repartitioned according to a temperature-dependent fraction of ice in total water ( fi):
i1520-0442-20-18-4526-e2
where the bounds Tmin and Tmax are adjustable within a narrow range. The values in CAM3 are −40° and −10°C, respectively. By this way the CAM3 avoids the representation of a variety of processes (e.g., the Bergeron–Findeisen process, contact nucleation, splintering, evaporation, energy transitions, etc.) in mixed-phase clouds.

In the default configuration used here, CAM3 includes the radiative effects of an aerosol climatology in the calculation of shortwave fluxes and heating rates, which is derived from a chemical transport model constrained by assimilation of satellite retrievals of aerosol optical depth for the period of 1995–2000 (Collins et al. 2001; Rasch et al. 2001). The cloud optical properties, for example, the effective radius of cloud droplets and ice particles, the cloud liquid and ice water path, and cloud emissivity, are used in the radiation calculations.

b. Changes to CAM3

Here we describe the changes to the CAM3 model, particularly to the ice-phase microphysics in stratiform clouds.

1) Cloud particle number prediction

To study the effects of anthropogenic aerosols on clouds through their influence on the number and sizes of cloud particles, we have implemented two additional prognostic equations to the NCAR CAM3, one for the number of cloud droplets, Nd, and another for the number of ice crystals, Ni. Since this study focuses on ice microphysics, we turned off the aerosol effect on liquid water clouds by using the droplet effective radius re that is prescribed in the standard CAM3 (i.e., re = 14 μm over oceans, while re depends on T and snow depth over land; for T > 0°C, re is assumed to be 8 μm over land in the absence of snow; Boville et al. 2006). Here, Nd is prescribed as in the standard CAM3 to be 400 cm−3 over land near the surface, 150 cm−3 over the ocean, and 75 cm−3 over sea ice. Ice number concentration, Ni, with its sources and sinks can be predicted from
i1520-0442-20-18-4526-e3
where R(Ni) represents the advective, turbulent, and convective transport of Ni, and qi is the cloud ice mixing ratio. Convective transport refers to the detrainment at the top of cumulus clouds. Here, Jnuc is the nucleation of ice crystals, Jfrz is the freezing of cloud droplets, and Jsec is the secondary production of ice crystals. The other microphysical processes are aggregation of ice crystals to form snow Qagg, accretion of ice crystals by snow Qsaci, and melting of ice crystals Qmlt. The increase in Ni due to detrainment from convective clouds is approximated as 3ρ0Qdlf/(4πρir3iv) following Lohmann (2002), where ρi is the ice crystal density, ρ0 is the air density, riv is the volume mean radius, and Qdlf is the detrainment rate of cloud water mass diagnosed from the convection parameterization. The above volume mean radius of ice crystals riv in the detrainment contribution to ice number is determined from a temperature-dependent lookup table as in the standard CAM3 (Kristjánsson et al. 2000; also Boville et al. 2006), because we intend to study the effect of aerosols on cirrus clouds formed from in situ ice nucleation, not from convective detrainment. The effect of aerosols on ice microphysics in the anvil cirrus is ignored here because how aerosols influence microphysics, convection, precipitation, and detainment in convective systems is currently too uncertain (Nober et al. 2003; Menon and Rotstayn 2006). The ice nucleation term in the equation is given by
i1520-0442-20-18-4526-e4
This equation means that at the present time step if the nucleated ice number (Nnuc) at a given model grid box is higher than that at the last time step, the ice number difference (divided by model time step Δt) will be added to the prognostic equation for Ni; otherwise there will be no new ice nucleation. Vapor conversion to ice mass due to ice nucleation is added to the respective water prognostic equations, which are calculated using the changes in the rate of ice number concentration in Eq. (4) multiplied by the initial mass of an ice crystal mi0 (=10−12 kg; Rutledge and Hobbs 1983). The heating associated with the phase change is also accounted for. Secondary ice production Jsec between −3° and −8°C (Hallett–Mossop process) in mixed-phase clouds is included based on Cotton et al. (1986). Ice number loss by evaporation Eevp is given by
i1520-0442-20-18-4526-e5
assuming that ice particles evaporate completely only when the cloud dissipates as the cloud fraction fcld decreases (Ghan et al. 1997a).

2) Ice nucleation and droplet freezing

We use the Liu and Penner (2005) parameterization to determine the number of ice crystals (Nnuc) formed through homogeneous ice nucleation and heterogeneous immersion nucleation in cirrus clouds (with temperatures less than −35°C). This parameterization method is able to represent the combination of homogeneous and heterogeneous nucleation pathways, and to link the Ni to aerosol properties, and thus allows us to carry out a nucleation simulation considering the effects of different aerosol types (e.g., sulfate and soot). Sulfate aerosol in the upper troposphere is generally soluble especially at the high RH regimes required for homogeneous ice nucleation. Thus, we assume that sulfate particles form ice through homogeneous freezing after deliquescence. The ice nuclei for immersion nucleation is assumed to be soot particles, since soot particles are often internally mixed with soluble species (e.g., sulfate) due to aging in the atmosphere (e.g., Pósfai et al. 1999; Clarke et al. 2004). CAM3 distinguishes between hydrophobic and hydrophilic black carbon (BC), and only hydrophilic BC is used in the ice nucleation calculation. Single particle measurements found that dust particles are generally insoluble and have little soluble compounds associated with them (Cziczo et al. 2004). Thus we assume that they can act as contact IN for cloud droplet freezing (Lohmann 2002), and as deposition/condensation IN (DeMott et al. 2003; Sassen 2005) as described by the Meyers et al. (1992) parameterization (see below). In the Liu and Penner (2005) parameterization, ice number Ni depends on temperature, updraft velocity, sulfate, and immersion IN (e.g., soot) number concentrations and was derived by fitting the results from a large set of cloud parcel model calculations covering different conditions in the upper troposphere. Our cloud parcel model was part of the Cirrus Parcel Model Comparison Project (CPMC) of the Global Energy and Water Experiment (GEWEX) Cloud System Study (GCSS) Working Group on Cirrus Cloud Systems (WG2; Lin et al. 2002). There was qualitative agreement between different parcel models for the homogeneous nucleation simulations. The homogeneous nucleation formulation, haze particle solution concentration, and water vapor uptake rate by ice crystal growth (particularly as controlled by the deposition coefficient) are critical components that lead to differences in the predicted microphysics. The difference in the predicted ice number concentrations can be greatly reduced if the same value for the deposition coefficient is used for the models (Lin et al. 2002).

The threshold relative humidity with respect to water (RHw, %) for homogeneous ice formation was fitted as a function of the temperature at which freezing commences (T, °C) and the updraft velocity (w, m s−1):
i1520-0442-20-18-4526-e6
where A = 6 × 10−4 lnw + 6.6 × 10−3, B = 6 × 10−2 lnw + 1.052, and C = 1.68 ln w + 129.35 (Liu and Penner 2005). It is noted that the updraft velocity effect on the critical RHw is much smaller than the temperature effect in Eq. (6). The RHw threshold in Eq. (6) is well within the range of variations of parameterizations in the literature and is also within 10% of the laboratory data of Koop et al. (1998) in the temperature range from −35° to −60°C (Liu and Penner 2005). For higher T and lower w (the fast-growth regime), the ice crystal number density Ni,a (cm−3) from homogeneous freezing, as a function of T, w, and sulfate aerosol number concentration Ni,a (cm−3), is given by the following power-law function between Ni,a and Na:
i1520-0442-20-18-4526-e7
For lower T and higher w (the slow-growth regime) a formula similar to Eq. (7) is obtained:
i1520-0442-20-18-4526-e8
When we set a1 + b1 T + c1 lnw to be equal for the two regimes, we find that
i1520-0442-20-18-4526-e9
which is used to differentiate the two growth regimes. Thus, when T ≥ 6.07 lnw − 55.0, or when w ≤ exp[(T + 55.0)/6.07], the fast-growth regime is used. Values of the coefficients in Eqs. (7) and (8) are given in Table 1.
The description of immersion ice nucleation in Liu and Penner (2005) is based on classical theory (Pruppacher and Klett 1997; Fletcher 1962). The threshold RHi for immersion nucleation is rather insensitive to the temperature and updraft velocity, and is ∼120% when the compatibility parameter, mis, is 0.5 and ∼130% when mis is 0.1. The compatibility parameter is defined as the cosine of the contact angle between the ice germ and solid substrate (Pruppacher and Klett 1997). The critical soot number concentration Ns,c (cm−3) above which only heterogeneous nucleation occurs is derived as
i1520-0442-20-18-4526-e10
We have compared this equation with the Gierens (2003) formulation in Liu and Penner (2005). The number of ice crystals formed from immersion nucleation of soot (Ni,s, cm−3) in the heterogeneous nucleation regime [determined from Eq. (10)] is given as
i1520-0442-20-18-4526-e11
and values of the coefficients in Eq. (11) are given by a11 = 0.0263, b11 = −0.008, a12 = −0.0185, b12 = −0.0468, a21 = 2.758, b21 = −0.2667, a22 = 1.3221, and b22 = −1.4588. We can see from Eq. (11) that Ni,s is a power-law function of soot number concentration (Ns, cm−3).

We include a subgrid-scale variation of the updraft velocity to account for the small-scale temperature fluctuations caused by high-frequency gravity waves that are currently unresolved in global models, but are very important in ice nucleation (Jensen and Pfister 2004; Hoyle et al. 2005; Haag and Kärcher 2004). This is modeled by adding a Gaussian distribution with a standard deviation of 25 cm s−1 [based on the Interhemispheric Differences in Cirrus Properties from Anthropogenic Emissions (INCA) measurements, from Kärcher and Ström (2003)] to the large-scale updraft velocity predicted by the climate model. A sensitivity test is undertaken in section 5 to study the effect of varying this assumed standard deviation on ice nucleation and global climate. Another method to calculate the cloud-scale updraft velocity is to use a velocity derived from the turbulent kinetic energy (TKE; Lohmann 2002). However the TKE is not available from the CAM3 turbulence scheme.

Currently we assume that deposition/condensation nucleation on mineral dust in the temperature range between −40° and 0°C is represented by the Meyers et al. (1992) formulation:
i1520-0442-20-18-4526-e12
where Ni,d (l−1) is the number of ice crystals predicted due to deposition nucleation, a = −0.639, and b = 0.1296. Future improvement should allow Ni,d to depend on the dust number concentration. Since the Meyers’ formula is based on ground-based measurements, in applying this formula, we assume the decay rate for the median concentrations obtained by Minikin et al. (2003) for tropospheric vertical profiles of nonvolatile (refractory) particle number concentrations in the Northern Hemisphere at midlatitudes during the INCA campaign. This decay rate was about tenfold per 6.7 km from the boundary layer top (∼1 km) to 7 km with little variation above 7 km. The total number of ice crystals Nnuc in Eq. (4) includes the contribution calculated using the Liu and Penner (2005) parameterization, which accounts for the homogeneous freezing on sulfate and heterogeneous immersion freezing on soot (including the partitioning between them), and that from deposition/condensation nucleation on mineral dust [Eq. (12)].
In mixed-phase clouds with temperatures between −40° and −3°C, we also include contact freezing of cloud droplets through Brownian coagulation with insoluble IN (Young 1974). The change in ice number concentration is
i1520-0442-20-18-4526-e13
where rυ is the volume mean droplet radius, Nd is the number concentration of cloud droplets with values prescribed as in the standard CAM3, ρ0 is the air density, Dcnt is the Brownian aerosol diffusivity, and Ncnt is the concentration of contact IN, which is parameterized by
i1520-0442-20-18-4526-e14
with Na0 as the number of active ice nuclei at 269.16 K. Here we assume that all contact IN are mineral dust (Lohmann 2002), and Na0 is then given by the number concentration of dust particles (with four size bins of 0.05–0.5-, 0.5–1.25-, 1.25–2.5-, and 2.5–5.0-μm radius in the CAM3 aerosol fields). The Brownian aerosol diffusivity is given by
i1520-0442-20-18-4526-e15
where kB is the Boltzmann constant, μ is the viscosity of air, rcnt is the aerosol number mean radius, and Cc is the Cunningham correction factor. Following Morrison and Pinto (2005), we neglect the impact of phoretic forces on the grid-scale collection rate because these forces depend on local gradients of temperature and water vapor that are not resolved at the GCM scale.

3) Effective radius of cloud particles

A change in the cloud particle number concentration can affect the radiative properties of clouds by modifying the effective radius of cloud particles. In water clouds, the effective radius (re) can be related to volume mean radius (rυ) by
i1520-0442-20-18-4526-e16
with k equal to 0.67 (over land) and to 0.8 (over ocean; Martin et al. 1994). For ice clouds, this relationship and values of k are more complicated because most ice crystals are nonspherical particles. There is a wide variety of mathematical expressions for the ice size spectrum based on observations. The effective radius, which is a measure of the mean size for use in optical depth calculations, has been interpreted and defined in various ways (McFarquhar and Heymsfield 1998). Many large-scale models do not take into account the varying size of ice particles, or only parameterize the effective radius as a function of ice water content (IWC; McFarlane et al. 1992) or temperature (Ou and Liou 1995). Iacobellis et al. (2003) described how the relationship between ice effective radius as a function of IWC and temperature changes when different definitions of effective radius are used. To facilitate the simulation of the effects of aerosols on ice cloud optical properties, an analytical formulation relating the ice effective radius rie to ice volume mean radius riv is derived. For this formula, we used the ice crystal size distribution (a gamma distribution for length less than 20 μm and a power-law distribution for larger particles) recommended in Wyser (1998). The ice effective radius is defined by Wyser (1998) as
i1520-0442-20-18-4526-e17
which assumes that the mean radius for a randomly oriented ice particle is proportional to (D2L)1/3 and that the ice crystal shape is a hexagonal column with length L and width D. We assume that Eq. (16) holds for ice crystals, and we calculate the value of k for different temperatures and IWC (Y. Chen et al. 2006, personal communication). Then k can be regressed as a function of temperature T(K) and IWC(g m−3):
i1520-0442-20-18-4526-e18
where
i1520-0442-20-18-4526-eq1
The above fitted formulation captures the main dependence of k on T and IWC within the predicted ranges. The mean volume radius of ice crystals can be calculated from the number density and mass mixing ratio of cloud ice. Then, effective radius is derived using Eq. (16) with k values calculated from Eq. (18).

The above two-moment (mass and number) scheme for cloud ice allows us to predict the effective radius of ice particles and allows aerosol–cloud radiation interactions by using the predicted mass and number of ice particles. Thus, the prescribed temperature dependence of the radius for ice clouds (Kristjánsson et al. 2000) used in the standard CAM3 radiation and gravitational settling calculations can be replaced with these predicted values. The aerosol effects on radiation, cloud microphysics, and water vapor can be accounted for by changing the number density, size, and gravitational settling of ice particles.

4) Ice supersaturation and ice depositional growth

As mentioned above, in the standard CAM3 a hybrid RH is used for model physics (i.e., using RHw for T > 0°C and RHi for T < −20°C). Like most current global climate models, CAM3 removes supersaturation through a saturation adjustment scheme. Therefore, the CAM3 predicted RHi never exceeds 100% for ice clouds in the upper troposphere at temperatures below −20°C. Also in the standard CAM3 the liquid and ice are repartitioned according to a temperature-dependent fraction of ice in total water (linear temperature interpolation between −40° and −10°C) after advection. To describe the ice formation process more explicitly, we modified the ice microphysics scheme in CAM3 as follows: the cloud condensation and evaporation (C–E) scheme of Rasch and Kristjánsson (1998), which removes ice supersaturation for ice clouds in the standard CAM3, is now used only for liquid water. Therefore, in diagnosing C–E for liquid clouds, RHw and a liquid cloud fraction (based on RHw) are used. A separate (total) cloud fraction is diagnosed based on RHi when T < 0°C and RHw when T ≥ 0°C, and this cloud fraction is used in the cloud microphysics and radiation schemes. To represent vapor deposition on ice crystals we use the Rotstayn et al. (2000) scheme, which accounts for the dependence of vapor deposition on crystal size and reduces time truncation errors. The total amount of cloud ice water that is converted from water vapor in a time step Δt in the grid box is given by
i1520-0442-20-18-4526-e19
where qi0 = max(mi0Ni/ρ0, qi) and qi is the grid average cloud-ice mixing ratio prior to the vapor deposition calculation, and mi0 is the initial mass of ice crystals (=10−12 kg). The rate constant csvd in (19) is given by
i1520-0442-20-18-4526-e20
where
i1520-0442-20-18-4526-e21
and B″ = RυT/χesi are terms representing heat conduction and vapor diffusion, respectively. Here χ is the diffusivity of water vapor in air, which varies inversely with pressure p as χ = 2.21/p in SI units, Ls is the latent heat of sublimation of water, Rυ is the specific gas constant for water vapor, Ka is the thermal conductivity of air, esi is the saturation vapor pressure with respect to ice, and ρi is the ice crystal density. The water vapor deposition on ice crystals is allowed only when the temperature is less than 0°C and when there is cloud ice existing in the grid cell. We use the C–E scheme for the condensational growth and evaporation of liquid water. When there is cloud ice (e.g., from the contact freezing of cloud liquid in mixed-phase clouds), vapor deposition on ice will start (Zhang et al. 2005). We may underestimate cloud freezing in mixed-phase clouds since we have not included immersion freezing of cloud droplets. However it has been suggested that contact nucleation is more efficient than immersion freezing in mixed-phase clouds (Lohmann and Diehl 2006). The vapor deposition scheme, combined with a treatment of riming of droplets on ice crystals, permits explicit treatment of the Bergeron–Findeisen process (in which crystals grow at the expense of evaporating or collected cloud droplets), rather than simply diagnosing the condensate phase from temperature. The Rasch and Kristjánsson (1998) parameterization of the conversion from liquid and ice to rain and snow is left unchanged. We also added the liquid mass conversion to ice due to the contact freezing of cloud droplets calculated from multiplying the change in ice crystal number concentration [Eq. (13)] by the volume mean mass for a cloud droplet. Droplet number Nd is from the standard CAM3. The heating associated with the phase change is also accounted for. Table 2 summarizes the changes to the CAM3.

3. Model results

a. Ice water content in the upper troposphere and lower stratosphere

The simulations with the standard CAM3 (simulation REF), with our modified ice microphysics changes to CAM3 (simulation ICE), and for the sensitivity studies (see section 4) were conducted at 2° × 2.5° horizontal resolution with 26 vertical levels over a period of 3 yr after an initial spinup of 4 months using climatological sea surface temperatures from 1950 to 2000. Results from the last 3 yr are presented below.

The NASA Earth Observing System’s (EOS’s) Microwave Limb Sounder (MLS) (Waters et al. 1999) onboard the Aura satellite (launched on 15 July 2004) has five radiometers measuring microwave emissions from the earth’s atmosphere to retrieve chemical composition, water vapor, temperature, and cloud ice. The MLS ice water content consists of data from the upper troposphere and lower stratosphere (from 316 to 46 hPa) and has a near-global (82°S–82°N) coverage. The estimated uncertainty in the IWC measurements is approximately 0.4, 1.0, and 4 mg m−3 at 100, 147, and 215 hPa, respectively (Livesey et al. 2005). These values account for combined instrument plus algorithm uncertainties associated with a single observation. The IWC data have a vertical resolution of ∼3.5 km and a horizontal resolution of ∼160 km for a single MLS measurement along an orbital track (Wu et al. 2006). The data used here for comparison are monthly mean IWCs from September 2004 to August 2005 averaged with 4° (latitude) × 8° (longitude) horizontal resolution, and thus have an uncertainty of 0.3 and 2 mg m−3 at 147 and 215 hPa, respectively, due to time and spatial averages (Su et al. 2006). For comparison, we regridded the CAM3 data to the same MLS horizontal and vertical resolution.

Figure 1 shows the annual and zonal mean IWC predicted by the modified CAM (simulation ICE) and standard CAM (simulation REF) as compared with the MLS data. The predicted IWC from the modified CAM is more consistent with the MLS Aura data in the upper troposphere than the standard CAM prediction. Although the standard CAM generally reproduces the horizontal IWC distribution patterns with maxima in the western Pacific, the Indian Ocean, and over central Africa and South America, it predicts too little IWC by as much as a factor of 4 in tropical regions (Li et al. 2005). We note that our modified CAM may still underestimate IWC at levels above ∼200 hPa in the Tropics by a factor of 2–3, since the MLS IWC is distributed to higher altitudes there. Moreover, the MLS IWC has a more rapid reduction from the Tropics toward the two poles than does the model (i.e., lower MLS IWCs at polar regions).

b. IWC at the ARM SGP site

One of the difficulties in using radar/lidar/radiometer observations to validate models is that these observations are often single-point measurements, and may not be representative of cloud properties predicted from the global climate models with larger grid sizes (100–500 km). Seo and Liu (2006) combined surface remote sensing and satellite measurements to derive three-dimensional IWC distributions in a 10° × 10° area (31.6°–41.6°N, 92.5°–102.5°W) near the Atmospheric Radiation Measurement (ARM) Southern Great Plain (SGP) site. The estimated uncertainty in the IWC is about 20% on average, which was estimated by considering both the instrument and radiative transfer model uncertainties (Seo and Liu 2006). Currently only the ice water retrievals for the March 2000 SGP Intensive Operation Period are available. We averaged the ARM IWC data temporally and horizontally to produce the monthly mean vertical profile of IWC. To compare with measured data, we also averaged the model grid mean IWC horizontally over the same area (10° × 10°). Figure 2 shows the IWC profiles from ARM data, and from the CAM simulations REF and ICE. Both CAM simulations broadly capture the IWC maximum between 3 and 8 km. However, they underpredict the data by a factor of 10. The simulation ICE improves the comparison, but the increased IWC is still much smaller than the observations. Model results agree with the data better at 9–12-km altitudes (see also Fig. 1 in the Northern midlatitudes from ∼300 to 200 hPa). Above 12 km, modeled IWCs decrease with altitude much faster than the observation. However, the value observed above 12 km is so small (∼0.1 mg m−3) that it is below the sensitivity of the minimum ice amount that may be retrieved (G. Liu 2006, personal communication).

c. Frequency of occurrence of relative humidity

Gierens et al. (1999) analyzed three years of Measurement of Ozone on Airbus In-Service Aircraft (MOZAIC) measurements and derived a distribution law for the relative humidity in the upper troposphere and lower stratosphere. The MOZAIC data for RHi were found to decrease exponentially with increasing relative humidity above 100% in two layers centered around 200 and 250 hPa for cloud-free conditions. We also extracted RHi from one year of CAM data for cloud-free grids. Data were sampled every 3 h at the CAM grid cells (2.5° × 2°) within the MOZAIC flight tracks. Figure 3 compares the model-predicted RHi with the MOZAIC data in two upper-troposphere layers (200–300 and 300–500 hPa). Unlike the standard CAM (simulation REF), our modified ice microphysics (simulation ICE) allows ice supersaturation in the upper troposphere. However, although we averaged the MOZIAC data within the CAM grid cell over each 3-h time interval, the occurrence frequency of ice supersaturation from the modified CAM is still lower than that from the MOZAIC data. As in the MOZAIC data, the probability of RHi occurrence from the ICE simulation decreases exponentially with relative humidity above 100% in these two layers, but it decreases faster than the MOZAIC data. The model-calculated maximum RHi in the two layers is ∼135% within the MOZAIC flight tracks. Considering the CAM calculated RHi represents the average within one 2.5° × 2° grid, while the MOZAIC RHi data represent the local averages over 60 s, equivalent to a horizontal length scale of 15 km (Gierens et al. 1999), the lower frequency of CAM calculated ice supersaturation is reasonable. This comparison points out the importance of including the subgrid-scale variation in RHi in the ice microphysics processes (e.g., ice nucleation; see section 4). We note that Tompkins et al. (2007) obtained a much higher frequency of moderate supersaturation with respect to ice by allowing a higher grid-mean humidity limit (rather than ice saturation) in their condensation and evaporation scheme for pure ice clouds (T < 250 K) in the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Subgrid RH variability was considered by assuming ice saturation within the cloudy portion of the grid and then diagnosing the clear-sky portion of the humidity. All humidity exceeding the saturation value was converted to ice instantaneously once the nucleation threshold was attained.

d. Comparisons between the standard and the modified CAM3

Figure 4 shows the zonal mean mass mixing ratio and number concentration of ice crystals in January predicted from the ICE simulation. Ice nucleation in the tropical upper troposphere and over the Arctic regions produces maxima in the number concentrations of ice crystals (with radius of ∼10 μm) due to the extremely low temperatures (less than −70°C) and high relative humidity, while the ice mass maxima appear at lower altitudes. Maximum number concentrations of ice crystals over the Arctic regions are a result of ice nucleation on aerosols, which are transported from midlatitudes by the general circulation (e.g., Easter et al. 2004; Liu et al. 2005). The simulated in-cloud ice crystal number concentrations are mostly within the range of 0.01–1 cm−3. Dowling and Radke (1990) summarized the microphysical data of cirrus from a wide number of projects before 1990 and found crystal number concentrations ranging from 10−7 to 10 cm−3, which were widely scattered. Recent measurements of ice number concentrations are in the range from 0.01 to 10 cm−3. However there are still questions about whether these high ice number concentrations are predominantly artifacts of shattering in the instrument inlets (E. Jensen 2006, personal communication).

The source and sink terms for ice number concentration in January are shown in Fig. 5. Ice nucleation from aerosols [see Eq. (4)] is the dominant source for ice number in the tropical tropopause region and in the regions over the Arctic. Convective detrainment becomes more important in the tropical upper troposphere between 200 and 400 hPa and also at midlatitudes. The ice number source rates in mixed-phase clouds due to contact freezing of droplets and secondary ice production are much smaller than the ice formation rates due to nucleation and detrainment in the upper troposphere. Precipitation reduction of ice number generally dominates in the upper troposphere, while cloud evaporation is a more important sink near the tropopause.

Figure 6 shows the zonal mean (weighted by the cloud ice mixing ratio) effective radius of ice crystals in January from simulations ICE and REF. Simulation ICE predicts low effective radius (less than 25 μm) for ice crystals in the upper troposphere due to the low temperatures and high RHi, which favors ice nucleation and produces large numbers of small ice crystals. This small ice effective radius in the upper troposphere is also present in simulation REF. At lower levels, however, the distribution patterns between the two simulations are different: the maximum effective radius (150–200 μm) from simulation ICE generally coincides with the regions with higher ice mass mixing ratios, whereas the effective radius from simulation REF increases toward lower altitudes with increased temperatures. These differences in ice effective radius will influence the radiative properties of ice crystals and ice gravitational settling velocity, which will then influence modeled cloud radiative forcing and IWC, as discussed below and shown in Figs. 7 and 8.

Figure 7 shows the annual average zonal mean latitude versus pressure cross sections of temperature, specific humidity, cloud cover, and ice water mixing ratio for simulations REF and ICE. Specific humidity in the ICE simulation is reduced in the tropical convective regions in the lower and middle troposphere and in some regions of the midlatitudes but is increased elsewhere. The largest relative increase occurs in the tropical tropopause and lower stratosphere regions (by ∼50%) above 200 hPa (figure not shown). Correspondingly cloud cover is increased near the tropical tropopause (∼100 hPa) and over polar regions. The higher cloud cover (by 20%–30%) near the tropical tropopause is due to the formation of subvisible cirrus clouds from ice nucleation, while cloud cover there in the standard CAM3 is underestimated (Boville et al. 2006). Wang et al. (1996) found that subvisible cirrus clouds occurred >45% of the time just below the tropical tropopause in the Stratospheric Aerosol and Gas Experiment II profiles. Subvisible cirrus can be either generated in situ from large-scale vertical motions or they can be remnants of anvils produced by deep convection. McFarquhar et al. (2000) determined that 28% of the subvisible tropopause tropical cirrus occurred in the vicinity of deep convection. These subvisible cirrus clouds contribute to a temperature increase due to radiation heating, which reduces the cold tropopause bias in the standard CAM3 by 1–2 K. The standard CAM3 simulates excessive clouds over polar regions, causing too little all-sky surface insolation there (Collins et al. 2006a). This model bias has been amplified in the ICE simulation with its even greater cloud cover. The atmosphere is warmer by 2–3 K near the surface in the Arctic compared to the REF simulation. Since the purpose of this study is to document the effects of introducing an ice nucleation scheme in CAM3, we did not change the values of the adjustable parameters in CAM3 [e.g., the threshold relative humidity RHmin in Eq. (1)]. The zonal mean maxima of cloud ice mixing ratio are increased significantly in the ICE simulation, in better agreement with the MLS data (see Fig. 1). Above these maxima, the cloud ice mixing ratio is slightly decreased, which also agrees better with the MLS data since the cloud ice mixing ratio is reduced faster toward the poles.

The comparison of the annual zonal mean shortwave and longwave cloud forcing with Earth Radiation Budget Experiment (ERBE) data is shown in Fig. 8. The difference in the shortwave and longwave cloud forcing from the REF and ICE simulations are within 2 W m−2 in the Tropics, while the ICE shortwave (longwave) cloud forcing is decreased (increased) significantly (by ∼10 W m−2) in the extratropics due to the increases in cloud cover. The global mean shortwave and longwave cloud forcing is decreased and increased by −4.5 and 1.3 W m−2, respectively (Table 3). Conversely the higher water vapor content in the upper troposphere in simulation ICE causes a decrease in the clear-sky outgoing longwave radiation by 1.3 W m−2. The global mean liquid water and ice water paths are increased, by 19.5 and 6.2 g m−2, respectively. Most noticeably, total cloud cover and especially high cloud cover has increased significantly in simulation ICE as compared to REF. The total cloud cover increases by 19.3% in the global mean with a larger increase in high cloud cover (24.6%; Table 3).

4. Sensitivity studies

Sensitivity tests were undertaken to investigate the effects of ice nucleation parameters on modeled water vapor, cloud properties, and climate. In the SIG-W simulation, we reduced the standard deviation of the subgrid-scale updraft velocity (σw) by about a factor of 2 from 25 cm s−1 (from INCA measurements; Kärcher and Ström 2003) to 12 cm s−1, reflecting a change in the cooling rates (with the mean values of 1–10 K h−1) due to small-scale temperature fluctuations related to gravity waves (Haag and Kärcher 2004), as inferred from the SUCCESS measurements (Hoyle et al. 2005) and from tropical observations (Jensen and Pfister 2004).

In a second sensitivity study, we examined the importance of the threshold RHi for heterogeneous ice nucleation (simulation RHI). As mentioned above, we compared the model grid averaged RHi from the ICE simulation with the MOZAIC data. The frequency of occurrence of ice supersaturation from the model is lower than that from the measurements, which is not unrealistic considering the large CAM grid size (2.5° × 2°). This indicates the importance of considering subgrid-scale RHi variability in the ice nucleation (i.e., more frequent ice nucleation due to higher subgrid-scale RHi). To account for this subgrid-scale RHi variability, Tompkins et al. (2007) applied a factor of 0.8 to their homogeneous ice nucleation RHi threshold. In our study, as a first step, we reduced the heterogeneous ice nucleation RHi threshold in the Liu and Penner (2005) scheme from 120% (simulation ICE) to 105% (simulation RHI) to allow more frequent occurrence of ice nucleation events, but other aspects of the Liu and Penner (2005) parameterization were retained [i.e., there is no change in the partitioning between homogeneous and heterogeneous nucleation; see Eq. (10)]. We also note that there are still large variations in the threshold RHi for heterogeneous ice nucleation, which can vary from <110% to 150%–160% at cirrus temperature ranges (e.g., Archuleta et al. 2005; Möhler et al. 2006; Field et al. 2006). The above sensitivity simulations were run over a period of 3 yr after an initial spinup of 4 months. The last three years’ results are presented here.

Figure 9 shows the annual mean vertically integrated ice crystal number concentration for the ICE simulation and the two sensitivity simulations SIG-W and RHI. When we reduce σw from 25 to 12 m s−1, the ice crystal number is reduced substantially in polar regions, somewhat in the Tropics and in storm-track regions. There are no obvious changes elsewhere. Temperatures are reduced by less than 1 K at lower levels in the Arctic along with an increase in cloud cover by 4%–6% (see Fig. 10 for the differences of temperature, specific humidity, cloud cover, and cloud ice mixing ratio between the simulations ICE, SIG-W, and RHI). Changes in specific humidity and cloud ice mass mixing ratio are generally small (<10%). The global mean liquid and ice water path, cloud forcing, and cloud cover are almost unchanged (see Table 3).

When we reduce the threshold RHi for heterogeneous ice nucleation from ∼120% (simulation ICE) to 105% (simulation RHI), ice number increases almost everywhere due to the more efficient ice nucleation (see Fig. 9). As a result, cloud cover in the upper troposphere is increased, especially in the tropical tropopause and in polar regions (by up to 17%). Below the levels with these increases, cloud cover is slightly reduced by several percent. Global mean high and total cloud cover are increased by 1.5% and 1.4%, respectively, compared to simulation ICE (see Table 3). Increased ice number in simulation RHI reduces the effective radius of ice crystals. Ice sediments less readily and the air is dried less efficiently than when there is a lower number of larger ice crystals (simulation ICE). Shortwave and longwave cloud forcing is increased by 3.9 and 4.3 W m−2, respectively, due to the smaller ice effective radius and larger cloud cover. There is also a strong warming in the tropical tropopause due to the radiative heating from more cirrus clouds (Boville et al. 2006) and a cooling in the stratosphere by 4–5 K (see Figs. 10 and 11). Specific humidity generally increases except in the Arctic. Although the maximum absolute increase in the specific humidity is in the lower to middle atmosphere in the Tropics and subtropics, the relative increase is greatest in the tropical tropopause and in the stratosphere (see Fig. 11). The large increase in the stratospheric water vapor (by 50%–80%) not only results from the evaporation of smaller ice crystals transported from the troposphere, but also from the higher temperatures at the tropical tropopause, which increases the water vapor saturation pressure, and allows more water vapor to be transported to the stratosphere. Recent indications that stratospheric water vapor concentrations are increasing (Rosenlof et al. 2001; Oltmans et al. 2000) have intensified the urgency of understanding the processes controlling water vapor in the tropical tropopause layer, where the thin cirrus formed in situ plays a role in limiting the water vapor mixing ratio entering the stratosphere. Possible correlations between aerosol characteristics and upper-tropospheric water vapor and flux into the stratosphere have been proposed (Sherwood 2002a, b; Notholt et al. 2005).

We also ran a simulation ICE-K with the Kärcher et al. (2006) ice nucleation scheme. As mentioned above, the Kärcher et al. (2006) scheme tracks the changes in the water vapor saturation ratio, ice nucleation from aerosols, and growth of ice crystals with different sizes over the period of a GCM time step (e.g., 30 min) in each grid cell. The sub–time step in the scheme is adjustable to ensure that the relative change of the saturation ratio within each sub–time step is less than 0.001. As an air parcel rises for 30 min in the grid cell, the saturation ratio increases, and if the maximum saturation ratio in the parcel RHmaxi reaches the threshold RHi, ice nucleation will commence; otherwise ice nucleation in the grid does not take place. While the scheme is accurate and flexible in terms of treating different aerosol parameters, it is also very time demanding, and the simulation ICE-K increases the computational cost by a factor of ∼15 over the standard CAM simulation [as compared to a 20% increase in the ICE simulation with the Liu and Penner (2005) scheme]. We ran the simulation ICE-K for 12 months after 4 month of spinup time. We used RHi = 100% as the starting RH of the parcel. Simulation ICE-K produces an even higher zonal mean and vertically integrated ice number concentration than the simulation RHI. The effects on cloud properties and radiation (e.g., cloud radiative forcing and cloud fraction) are even more dramatic. The global annual average shortwave and longwave cloud forcing is enhanced by 13.7 and 11.7 W m−2, respectively, and total cloud fraction by 22.8% as compared to the standard CAM3 simulation REF (see Table 3).

5. Summary and conclusions

In this study, a prognostic equation for ice crystal number concentration and an ice nucleation scheme (Liu and Penner 2005) are implemented in the NCAR CAM3. The effective radius of ice crystals is calculated from the model-predicted mass and number concentrations of ice crystals rather than diagnosed as a function of temperature. The calculated distribution of ice crystal effective radius now follows closely that of the ice mass mixing ratio (having maxima within the −10° and −40°C isotherm). The Liu and Penner (2005) ice nucleation scheme relates the number of ice crystals nucleated to the aerosol number, air temperature, and updraft velocity and considers both homogeneous and heterogeneous nucleation. Since ice nucleation requires supersaturation, the saturation adjustment in the CAM3 model through the condensation and evaporation (C–E) scheme (Rasch and Kristjánsson 1998; Zhang et al. 2003) is only used for liquid water. In both mixed-phase and ice clouds a scheme for vapor deposition on ice crystals (Rotstayn et al. 2000) is introduced. The repartitioning for the ice fraction of the total water ( fi) in mixed-phase clouds as a function of temperatures is removed. With these modifications, ice supersaturation is allowed in the upper troposphere. We compared the statistics of the CAM predicted RHi with the MOZAIC data within flight tracks. The occurrence frequency of ice supersaturation is underestimated. However, this result is not unrealistic considering the large grid size (2.5° × 2°) of CAM3.

The model-calculated IWC is generally increased, in better agreement with the Aura MLS data for the upper troposphere. The modeled IWC above 200 hPa may still be too low, however. It is noted that the deposition of water vapor on ice crystals might be underestimated. When we apply the Rotstayn et al. (2000) scheme, water vapor deposition on ice crystals takes place only when the grid-averaged RHi exceeds 100%. If we had considered the subgrid-scale variability of RHi, there would be regions with ice supersaturation. Future work should take into account the effect of subgrid RHi variability on vapor deposition.

Cirrus cloud fraction near the tropical tropopause, which is underestimated in the standard CAM3, is increased in the new cloud ice scheme. Correspondingly the cold temperature bias is reduced by 1–2 K. Cloud fraction in polar regions is also increased, which increases the underestimation of downwelling shortwave radiation in CAM3. Shortwave and longwave cloud forcing with our modifications is enhanced by 4.5 and 1.3 W m−2, respectively. Clear sky outgoing longwave radiation is reduced by 1.3 W m−2 due to stronger water vapor absorption in the atmosphere. Both liquid and ice water path are increased by 19.5 and 6.2 g m−2, respectively.

One purpose of this study is to enhance model capability for studying the indirect effect of aerosols on the properties of cirrus clouds. As a first step in this direction, a sensitivity test was performed in which we reduced the threshold RHi for heterogeneous ice nucleation from 120% to 105% (representing nearly perfect IN). Global cloud cover increases by 1.4% with regional changes of 12% near the tropical tropopause. Temperature near the tropical tropopause increases by 4–5 K due to the increased radiative heating. More water vapor is transported into the stratosphere (by 50%–80%). Shortwave and longwave cloud cloud forcing increase by 3.9 and 4.3 W m−2, respectively. It is noted that in this study only the aerosol effect on in situ cirrus clouds through changing the effective radius is considered. Aerosol effects on anvil cirrus through changing the cloud microphysics and precipitation in convective systems remain very uncertain (Nober et al. 2003; Menon and Rotstayn 2006). In the future we expect to examine the effects of both present-day and preindustrial aerosols on homogeneous and heterogeneous nucleation in cirrus clouds. The radiative forcing resulting from aerosol effects (anthropogenic sulfate, soot emitted from aircraft as compared to that from the earth’s surface sources, and mineral dust) on cirrus clouds will be estimated.

Acknowledgments

The authors thank Dr. J. H. Jiang of the NASA Jet Propulsion Laboratory for proving the Aura MLS data. The authors acknowledge the support from the National Science Foundation under Grant ATM-0333016. Partial support from the NASA Interdisciplinary Science Program and the Department of Energy (DOE) Environmental Science Division Atmospheric Radiation Measurement (ARM) program is also gratefully acknowledged. The Pacific Northwest National Laboratory is operated for the DOE by the Battelle Memorial Institute under Contract DE-AC06-76RLO 1830.

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Fig. 1.
Fig. 1.

Annual and zonal mean ice water content (mg m−3) predicted by (a) the modified CAM (simulation ICE) and (b) standard CAM (simulation REF), and that measured by (c) the Aura MLS instrument.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 2.
Fig. 2.

Ice water content profiles from observation data at the ARM SGP site, and from the CAM simulations REF and ICE.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 3.
Fig. 3.

A comparison of normalized occurrence frequency of RHi in two layers of the upper troposphere (200–300 and 300–500 hPa) obtained from the two model simulations (REF and ICE) and the MOZAIC data. Data were sampled every 3 h in the CAM grid cells (2.5° × 2°) within flight tracks.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 4.
Fig. 4.

(a) Grid-averaged ice crystal mass mixing ratio (mg kg−1), (b) grid-averaged number concentrations (g−1), (c) air temperature (°C), and (d) relative humidity (%; RHw for T > 0°C, and RHi for T ≤ 0°C) in January predicted with the modified CAM3 model (simulation ICE).

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 5.
Fig. 5.

January zonal averaged cloud ice number source terms from (a) ice nucleation, (b) convective detrainment, (c) contact freezing of cloud droplets, and (d) secondary ice production, and the loss terms from (e) precipitation and (f) cloud evaporation.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 6.
Fig. 6.

Zonal mean effective radius (μm) of ice crystals in January from (a) simulation ICE and (b) simulation REF. Effective radius is weighted by the cloud ice mixing ratio when averaged zonally to reflect the larger contribution from optically thicker clouds.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 7.
Fig. 7.

Annual zonal mean latitude vs pressure cross sections of temperature (K), specific humidity (g kg−1), cloud cover (%), and cloud ice mass mixing ratio (mg kg−3) for simulations REF and ICE, and the difference, ICE – REF.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 8.
Fig. 8.

Annual and zonal means of shortwave and longwave cloud forcing from the two simulations REF and ICE as compared to the ERBE data.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 9.
Fig. 9.

Annual averaged and vertically integrated ice crystal number concentration (106 m−2) for simulations (a) ICE, (b) SIG-W, and (c) RHI.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 10.
Fig. 10.

Annual zonal mean latitude vs pressure cross sections of temperature (K), specific humidity (g kg−1), cloud cover (%), and cloud ice mass mixing ratio (mg kg−3) for simulation ICE and the differences, SIG-W – ICE and RHI – ICE.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Fig. 11.
Fig. 11.

Annual zonal mean latitude vs pressure cross sections of (a) ice number absolute difference (g−1), (b) specific humidity relative difference (%), and (c) temperature absolute difference (K) between the two simulations RHI and ICE.

Citation: Journal of Climate 20, 18; 10.1175/JCLI4264.1

Table 1.

Coefficients in the homogeneous nucleation parameterization [Eqs. (7) and (8)].

Table 1.
Table 2.

Changes to the CAM3 cloud microphysics processes.

Table 2.
Table 3.

Global annual mean liquid water path (LWP), ice water path (IWP), shortwave and longwave cloud forcing (SWCF and LWCF), clear-sky outgoing longwave radiation (FLNTC), and total and high cloud cover (CLDTOT and CLDHGH) for simulations REF, ICE, and W-SIG, and RHI, together with observations of the cloud forcings from the ERBE, CLDTOT, and CLDTHGH from the International Satellite Cloud Climatology Project (ISCCP).

Table 3.
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