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7-14-2014
Aerosol Effects on Cirrus Through Ice Nucleation
in the Community Atmosphere Model CAM5
with a Statistical Cirrus Scheme
Minghuai Wang
Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
Xiaohong Liu
University of Wyoming; Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland,
Washington
Kai Zhang
Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
Jennifer M. Comstock
Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
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Publication Information
Wang, Minghuai; Liu, Xiaohong; Zhang, Kai; and Comstock, Jennifer M. (2014). "Aerosol Effects on Cirrus Through Ice Nucleation
in the Community Atmosphere Model CAM5 with a Statistical Cirrus Scheme." Journal of Advances in Modeling Earth Systems , 1-21.
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PUBLICATIONS
Journal of Advances in Modeling Earth Systems
RESEARCH ARTICLE
10.1002/2014MS000339
Key Points:
A statistical cirrus scheme has been
implemented into CAM5
Heterogeneous IN significantly alter
the net TOA radiative fluxes
Aerosol effects on cirrus have a large
atmospheric component of radiative
fluxes
Aerosol effects on cirrus through ice nucleation in the
Community Atmosphere Model CAM5 with a statistical cirrus
scheme
Minghuai Wang1, Xiaohong Liu1,2, Kai Zhang1, and Jennifer M. Comstock1
1
2
Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA,
Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA
Abstract A statistical cirrus scheme that tracks ice saturation ratio in the clear-sky and cloudy portion of
Correspondence to:
M. Wang,
[email protected]
Citation:
Wang, M., X. Liu, K. Zhang, and J. M.
Comstock (2014), Aerosol effects on
cirrus through ice nucleation in the
Community Atmosphere Model CAM5
with a statistical cirrus scheme, J. Adv.
Model. Earth Syst., 06, doi:10.1002/
2014MS000339.
Received 22 APR 2014
Accepted 9 JUL 2014
Accepted article online 14 JUL 2014
a grid box separately has been implemented into the Community Atmosphere Model CAM5 to provide a
consistent treatment of ice nucleation and cloud formation. Simulated ice supersaturation and ice crystal
number concentrations strongly depend on the number concentrations of heterogeneous ice nuclei (IN),
subgrid temperature formulas, and the number concentration of sulfate particles participating in homogeneous freezing, while simulated ice water content is insensitive to these perturbations. Allowing 1–10% of
dust particles to serve as heterogeneous IN is found to produce ice supersaturation in better agreement
with observations. Introducing a subgrid temperature perturbation based on long-term aircraft observations
produces a better hemispheric contrast in ice supersaturation compared to observations. Heterogeneous IN
from dust particles alter the net radiative fluxes at the top of atmosphere (TOA) (20.24 to 21.59 W m22)
with a significant clear-sky longwave component (0.01 to 20.55 W m22). Different cirrus treatments significantly perturb the net TOA anthropogenic aerosol forcing from 21.21 W m22 to 21.54 W m22, with a
standard deviation of 0.10 W m22. Aerosol effects on cirrus exert an even larger impact on the atmospheric
component of the radiative fluxes (2 or 3 times the changes in the TOA radiative fluxes) and therefore
through the fast atmosphere response on the hydrological cycle. This points to the urgent need to quantify
aerosol effects on cirrus through ice nucleation and how these further affect the hydrological cycle.
1. Introduction
Cirrus clouds cover about 17% of the Earth’s area [Sassen et al., 2008] (about 30% when subvisible cirrus is
included [Wang et al., 1996]), and play an important role in the climate system by regulating the energy and
hydrological cycles. They form through homogeneous freezing of liquid solution such as sulfate droplets
[Koop et al., 2000] and/or heterogeneous freezing involving the surfaces of insoluble particles serving as ice
nuclei (IN) [Pruppacher and Klett, 1997; Zobrist et al., 2007]. Though cirrus is important, our understanding of
cirrus processes is still poor, and it is even more challenging to model cirrus in climate models.
This is an open access article under the
terms of the Creative Commons
Attribution-NonCommercial-NoDerivs
License, which permits use and
distribution in any medium, provided
the original work is properly cited, the
use is non-commercial and no
modifications or adaptations are
made.
WANG ET AL.
One challenge is to understand the ability of aerosol particles serving as heterogeneous IN in cirrus [Hoose
and Mohler, 2012]. Laboratory experiments and field observations show that mineral dust particles, soot,
bioaerosols (e.g., bacteria, fungal spores, pollen, and diatoms), solid ammonium sulfate, organic acids, and
humic-like substances are the main groups of atmospherically-relevant IN. Dust particles are shown to be
efficient heterogeneous IN by most studies, though their ice freezing capability may depend on their surface areas [DeMott et al., 2010], mineralogies [Atkinson et al., 2013], and coating from other species [Figure 9
€hler, 2012]. The ice freezing capability of soot particles is more uncertain, and soot particles
in Hoose and Mo
are generally viewed as less efficient ice nuclei than dust particles [Hoose and Mohler, 2012]. Bioaerosol particles can be very active IN at subzero temperatures [Despres et al., 2012], though their role in cirrus formation can be limited since bioaerosol particles are much less abundant compared to dust and soot particles
in the upper troposphere. Crystalline ammonium sulfate and organic solids are shown to be efficient IN in
cirrus conditions [Abbatt et al., 2006; Shilling et al., 2006], while organic acids in a glass state at temperature
below 265 C is also shown to be efficient IN [Murray et al., 2010; Baustian et al., 2013].
Using four aircraft measurement campaigns over North and Central America and nearby ocean, Cziczo et al.
[2013] has shown that mineral dust and metallic particles are dominant sources of ice crystal residual particles in cirrus, while sulfate/organic particles are underrepresented. Cziczo et al. [2013] has further shown
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that elemental carbon and biological materials are essentially absent from the residual particles in cirrus,
which leads to their suggestion of ignoring these particles as IN in climate models. We note that data analyzed in Cziczo et al. [2013] were biased toward anvil cirrus and are limited to low latitudes in the Northern
Hemisphere (NH). For in situ synoptic cirrus in the NH midlatitudes, Zhang et al. [2013] has shown fairly high
ice crystal number concentrations (100–1000 L21) and an increasing trend of ice crystal number concentration with decreasing temperature, which might suggest that ice crystals are primarily formed through
homogeneous freezing for this type of cirrus. For in situ cirrus and subvisible cirrus in the Tropical Troposphere Layer (TTL), it has been shown that dust particles do not appear to be enhanced in ice crystal residual measurements relative to ambient aerosol population and that most of the ice crystal residuals are
internally mixed sulfate and organics [Froyd et al., 2010], which leads to the suggestion that these ice crystals are formed heterogeneously on glassy aerosols or solid ammonia sulfate [Froyd et al., 2010; Jensen et al.,
2010; Jensen et al., 2013].
Another challenge in modeling cirrus in climate models is to represent the competition between homogeneous freezing and heterogeneous freezing. Simulating this competition requires the accurate description
of the time evolution of saturation ratio with respect to ice. However, due to coarse model resolution and
large time steps, climate models are unable to explicitly simulate the evolution of saturation ratio inside cirrus. In early studies, fixed threshold heterogeneous IN number concentrations were used to determine
whether homogeneous freezing or heterogeneous freezing occurs [Hendricks et al., 2005; Lohmann et al.,
2008]. More recently, more sophisticated parameterizations based on the parcel model framework have
been developed to treat the competition between homogeneous freezing and heterogeneous freezing.
These parameterizations can be derived using empirical fitting of parcel model results [Liu and Penner,
2005], analytical formulas based on the particle formation and growth equations [Barahona and Nenes,
2009a, 2009b], or through a direct numerical integration of a simplified parcel model within a large-scale
model [Karcher et al., 2006]. These parameterizations are typically combined with the grid-mean saturation
ratio predicted in climate models to determine when heterogeneous freezing or homogeneous freezing
occurs [Liu et al., 2007; Gettelman et al., 2010; Salzmann et al., 2010; Hendricks et al., 2011; Kuebbeler et al.,
2014].
What makes it even more challenging in modeling cirrus is the poor representation of mesoscale motions
in climate models. Mesoscale motions play a critical role in determining ice formation and growth [Karcher
and Strom, 2003; Haag and Karcher, 2004; Jensen and Pfister, 2004; Hoyle et al., 2005]. As processes that lead
to the mescoscale motions in the upper troposphere (e.g., gravity waves generated by topography and convection) are not well treated in models, the representation of subgrid vertical velocity or temperature perturbation from mesoscale motions is still poor in climate models. For example, the subgrid vertical velocity
for driving ice nucleation is often diagnosed based on the turbulence kinetic energy (TKE) [e.g., Gettleman
et al., 2010], though it is not clear how well the TKE can represent the mesoscale motions in the upper troposphere. Efforts have been taken to improve the representation of the orographic effects on cirrus formation [Dean et al., 2007; Joos et al., 2008], though the effects of gravity wave excited by convection on cirrus
formation are still neglected in climate models.
Global climate models have been used to study how heterogeneous IN and/or anthropogenic aerosols
affect cirrus and climate [Hendricks et al., 2005; Lohmann et al., 2008; Liu et al., 2009; Penner et al., 2009;
Wang and Penner, 2010; Hendricks et al., 2011; Gettelman et al., 2012; Liu et al., 2012a; Kuebbeler et al., 2014]
and to further explore how cirrus seeding may cool climate [Mitchell and Finnegan, 2009; Storelvmo et al.,
2013; Muri et al., 2014; Storelvmo and Herger, 2014]. Gettelman et al. [2012] showed that black carbon aerosols have a small and not statistically significant aerosol indirect effects on cirrus when included as ice
nuclei, for nucleation efficiencies within the range of laboratory measurements. Gettelman et al. [2012]
showed that anthropogenic aerosols from increasing sulfur emissions can potentially have larger effects
with a warming of 0.27 W m22 with a standard deviation of 0.1 W m22 from two climate models, though
the details of the warming mechanism are different between the two models.
In this study, we have implemented a statistical cirrus scheme [Wang and Penner, 2010] (hereafter WP10)
into the Community Atmosphere Model, Version 5 (CAM5). This cirrus scheme was originally developed by
Karcher and Burkhardt [2008] (hereafter KB08) and then was extended and implemented in NCAR CAM3 by
WP10. This cirrus scheme is based on separate probability distribution functions (PDF) for total water in the
clear-sky and cloudy portions of each grid. Both subsaturation and supersaturation conditions with respect
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to ice are allowed in cloud-free air and inside cirrus. The paper is organized as follows. Section 2 presents
the model description and experiment designs. Section 3 evaluates the performance of this scheme in
CAM5 against observations. Heterogeneous IN effects on cirrus and climate are examined in section 4, and
anthropogenic aerosol’s effects on cirrus and climate are examined in section 5. The summary is provided
in section 6.
2. Model and Methods
2.1. CAM5
In this study, the Community Atmospheric Model version 5.1 is used. CAM5 includes a range of enhancement and improvements in the representation of physical processes compared to previous versions, which
makes it possible to simulate the full aerosol-cloud interactions in stratiform clouds, including aerosol
effects on warm, mixed-phase, and cirrus clouds.
The moist turbulence scheme is based on Bretherton and Park [2009], which explicitly simulates stratusradiation-turbulence interactions. The shallow convection scheme is from Park and Bretherton [2009] and
uses a realistic plume dilution equation and closure that accurately simulates the spatial distribution of shallow convective activity. The deep convection parameterization is retained from CAM4.0 [Neale et al., 2008].
The two-moment cloud microphysics scheme from Morrison and Gettelman [2008] is used to predict both
the mass and number mixing ratios for cloud water and cloud ice with a diagnostic formula for rain and
snow. The cloud ice microphysics was further modified to allow ice supersaturation and aerosol effects on
ice clouds [Gettelman et al., 2010]. The radiative transfer scheme in CAM5 is a broadband k-distribution radiation model known as the Rapid Radiative Transfer Model for GCMs (RRTMG) [Mlawer et al., 1997; Iacono
et al., 2003; Iacono et al., 2008]. A modal approach is used to treat aerosols in CAM5 [Liu et al., 2012b; Ghan
et al., 2012]. Aerosol size distribution can be represented by using either 3 modes or 7 modes, and the
default 3-mode treatment is used in this study.
2.2. Cirrus Treatment
2.2.1. Cirrus Treatment in CAM5
Cirrus treatment in CAM5.1 is based on Liu et al. [2007] and Gettelman et al. [2010]. Ice nucleation is calculated based on grid-mean relative humidity with respect to ice (RHi) (a scaling factor of 1.2 is used to
account for the subgrid variations in RHi), while ice water is primarily generated through vapor deposition
that is calculated based on grid-mean RHi. Ice cloud fraction is diagnosed using a relative humidity of total
water (water vapor 1 ice water) (RHit) based on the scheme of Slingo [1987]. Cloud fraction increases linearly from 0 to 100% when RHit increases from 80 to 110% in CAM5.1.
As grid-mean RHi is used for ice nucleation, vapor deposition and ice cloud fraction, there are some potential
inconsistency in CAM5 cirrus scheme. For example, when RHit increases from 90 to 95%, ice cloud fraction
increases from 33 to 50%, but ice water sublimates (RHi < 100%) and no ice nucleation occurs. On the other
hand, when RHit decreases from 105 to 102%, cloud decays as cloud fraction decreases, but vapor deposition
and ice nucleation still occurs if RHi is larger than 100%. Moreover, ice nucleation is allowed in cloudy sky
even if there is plenty of preexisting ice crystals, as long as RHi is high enough and the number concentrations
of freshly nucleated ice crystals are larger than the number concentrations of preexisting ice crystals.
2.2.2. A PDF Cirrus Scheme
The PDF cirrus scheme documented in WP10 and KB08 has now been implemented into CAM5.1 to replace its
cirrus macrophysics. It provides a consistent treatment of cloud fraction growth/decay, ice nucleation, and ice
sublimation/vapor deposition. In KB08, the PDF scheme was constructed for nonconvective cirrus formed by
homogeneous freezing of supercooled aerosols and was tested in a box model. This scheme treats cloud growth
and decay based on a subgrid-scale distribution of temperature. It uses separate PDFs of total water in the clearsky and the cloudy sky portions of each GCM grid. In WP10, this scheme was implemented in CAM3 coupled
with an aerosol model [Wang et al., 2009] and was further extended to treat both homogeneous freezing and
heterogeneous freezing and to couple with anvil clouds generated from convective detrainment. Here we only
briefly describe the PDF cirrus scheme, and readers are referred to WP10 and KB08 for more details.
In this PDF cirrus scheme, the specific humidity in both the clear-sky areas and cloudy areas within a grid is
predicted in the model. Cloud growth via ice nucleation (increase in cloud fraction) is treated by using the
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specific humidity in the clear-sky area, while cloud decay (decrease in cloud fraction) is treated by using the
specific humidity in the cloudy area of a grid box that is also used to determine the amount of water vapor
that deposits onto or sublimates from ice crystals in the cloudy area.
The PDF of saturation ratio in the clear-sky portion is determined from the PDF of subgrid temperature in
the clear-sky portion of the grid. The PDF of temperature is assumed to be a constrained normal distribution
with a mean temperature predicted by the GCM and a standard deviation (subgrid temperature perturbation, dT). In WP10, dT is prescribed from Gary [2006, 2008] as a function of altitude, topography, season, and
latitude. In this study, we will also explore an alternative formula based on the subgrid vertical velocity diagnosed from the turbulent kinetic energy in CAM5.1 (section 2.3). The fraction of the portion of the saturation
ratio PDF that exceeds the threshold saturation ratio for ice nucleation is the fraction of the clear-sky part of
the grid that is experiencing ice nucleation, and therefore moves to the cloudy part of the grid box. Ice crystal number concentrations generated from ice nucleation depend on the subgrid vertical velocity (m s21),
which is linked to the subgrid-temperature perturbation through the following formula (KB08):
w½ms21 50:23dT½K:
(1)
In the cloudy part of the grid, specific humidity as well as temperature is assumed to have a uniform distribution. Vapor deposition or sublimation is calculated based on the in-cloud saturation ratio. When the
cloudy area is supersaturated, vapor deposition occurs and the supersaturation is relaxed to the saturation
level with no change in cloud fraction. When the cloudy area is subsaturated, sublimation of ice crystal
occurs and cloud decays. Decrease in cloud fraction due to sublimation is determined by the in-cloud specific humidity, mean in-cloud ice water content, and the PDF of the in-cloud ice water content.
Anvil clouds generated from convective detrainment are an additional source of cirrus in the upper troposphere
and are coupled to the KB08 scheme (WP10). The new clouds generated by convective detrainment are
assumed to be at saturation with respect to ice. In doing this, anvil clouds and in situ cirrus compete for available
water vapor. For example, when anvil clouds are formed due to convective detrainment, it reduces the saturation ratio in the clear-sky portion of a grid, and can potentially reduce the frequency of in situ cirrus formation.
As documented in WP10, both homogeneous and heterogeneous freezing are included, based on the parcel model results of Liu and Penner [2005]. When heterogeneous IN number concentrations (Nin) are larger
than a critical heterogeneous IN number concentration (Nin_cr) that is determined by temperature and subgrid vertical velocity, heterogeneous nucleation occurs and cloud fraction increase from heterogeneous ice
nucleation is determined by the threshold saturation ratio of heterogeneous freezing, while freshly
nucleated ice crystal number concentrations come from both heterogeneous freezing (for the portion of
the saturation ratio PDF that exceeds the saturation ratio of heterogeneous freezing) and homogeneous
freezing (for the portion of the saturation ratio PDF that exceeds the threshold saturation ratio of homogeneous freezing). When Nin is lower than 0.1Nin_cr, ice crystal number concentration is determined by homogeneous freezing, and cloud fraction is determined by the threshold saturation ratio of homogeneous
freezing. For the intermediate Nin (0.1Nin_cr<Nin< Nin_cr), both homogeneous freezing and heterogeneous
freezing occur, and ice crystal number concentrations nucleated gradually decrease from those generated
by homogeneous freezing to those generated by heterogeneous freezing with increasing Nin.
2.3. Model Experiments
Here we perform several model experiments (Table 1) to evaluate this new scheme and to study aerosol
effects on cirrus through ice nucleation. CAM5.1 is the simulation with the default CAM5.1 configuration
[Gettelman et al., 2010], but with a change in how cloud droplet number concentrations are updated from
freshly activated particles. In the default CAM5.1 (CAM5.1_default), cloud liquid condensate is updated from
large-scale condensation/evaporation first, and cloud droplet number concentration is updated next, in parallel with the other microphysics processes. This mismatch between liquid condensate and cloud droplet
number concentrations can cause excessive depletion of cloud water through autoconversion/accretion
processes due to lower droplet number concentrations used for microphysical processes. In the updated
version (CAM5.1), both cloud condensate and cloud droplet number concentration are updated before the
calculations of other microphysics process rates, which can partly remedy the issue of excessive depletion
of cloud water (H. Morrison, personal communication, 2014). This change leads to larger droplet number
concentrations and larger liquid water path (LWP) (see the discussion in section 3.4).
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Table 1. Model Experiments
Case Name
Heterogeneous INa
Sulfate Particlesb
dT/wc
Cirrus Schemed
Cloud Droplete
CAM5.1_default
CAM5.1
wGary_dust0.01
wGary_dust0.05
wGary_dust0.10
wGary_dust1.00
wGary_HOM
wCAM5_dust0.01
wCAM5_fdust0.05
wCAM5_dust0.10
wCAM5_dust1.0
wCAM5_HOM
wCAM5_aCAM5
wCAM5_aCAM5_HOM
Coarse mode dust
Coarse mode dust
fdust50.01
fdust50.05
fdust50.10
fdust51.00
fdust50.0
fdust50.01
fdust50.05
fdust50.10
fdust51.00
fdust50.00
Coarse mode dust
fdust50.0
>100 nm
>100 nm
All
All
All
All
All
All
All
All
All
All
> 100 nm
> 100 nm
CAM5
CAM5
Gary
Gary
Gary
Gary
Gary
CAM5
CAM5
CAM5
CAM5
CAM5
CAM5
CAM5
CAM5
CAM5
WP10
WP10
WP10
WP10
WP10
WP10
WP10
WP10
WP10
WP10
WP10
WP10
CAM5 default
Updated
Updated
Updated
Updated
Updated
Updated
Updated
Updated
Updated
Updated
Updated
Updated
Updated
a
Aerosol population that serves as heterogeneous IN: coarse mode dust, all dust particles in the coarse mode; fdust, the fraction of
accumulation and coarse mode dust particles.
b
The population of Aitken mode sulfate particles that participate in homogeneous freezing: All, all Aitken model sulfate
particles; > 100 nm, only those particles with diameter larger than 100 nm.
c
The subgrid vertical velocity/temperature formula used for the WP10 cirrus cloud scheme: CAM5, the one used in the default CAM5;
Gary, the one based on Gary [2006, 2008].
d
The cirrus cloud scheme used in the simulations: WP10, the WP10 cirrus cloud scheme documented in section 2.2.2; CAM5, the cirrus cloud scheme used in the default CAM5, briefly described in section 2.2.1.
e
How cloud droplet number concentration is calculated: See the discussion in the first paragraph of section 2.3.
In CAM5.1, sulfate solution particles used for homogeneous freezing are limited to sulfate particles in the
Aitken mode with diameter larger than 100 nm, and heterogeneous IN includes only dust particles in the
coarse mode, as documented in Gettelman et al. [2010]. Soot particles are not allowed to act as heterogeneous
IN, as soot particles are generally thought to be inefficient IN [Hoose and Mohler, 2012; Cziczo et al., 2013]. Subgrid vertical velocity used for ice nucleation is diagnosed from the turbulence kinetic energy predicted by the
turbulence scheme in CAM5 and an upper limit of 0.20 m s21 is applied [Gettelman et al., 2010].
In the first set of experiments (wGary), the PDF cirrus scheme described above replaces cirrus scheme in
CAM5.1 for temperature below 240 C and the subgrid temperature perturbation is based on Gary [2006,
2008]. The Gary formula is based on an analysis of more than 4000 aircraft flight hours taken by the Microwave Temperature Profiler in the altitude range 7–22 km and with a variety of underlying topography, spanning the latitude range 70 S–80 N. It accounts for the seasonal, latitude, topographic, and altitude
dependence of the temperature perturbation. The altitude dependence in the Gary formula is consistent
with gravity wave theory [Fritts and Alexander, 2003].
Following Liu et al. [2012a], the population of sulfate solution particles for homogeneous freezing includes
all sulfate particles in the Aitken mode, and dust particles in both accumulation and coarse modes can serve
as heterogeneous IN. Large uncertainties remain in the fraction of dust particles (fdust) that can serve as efficient IN. A fdust of 1.0 is used in Liu et al. [2012a], while Zhang et al. [2013] showed that a lower maximum
freezing ratio for dust aerosols (0.05–0.1) produces results in better agreement with observations based on
the ice nucleation parameterization of Barahona and Nenes [2009b]. Kuebbeler et al. [2014] used a fdust of
0.05 for coated dust particles as immersion freezing IN and a separate fdust for pure dust serving as deposition freezing IN as a function of saturation ratio. Here we perform four different experiments with fdust50.01,
0.05, 0.10, and 1.0, respectively. To further examine the effects of heterogeneous IN on cirrus and climate,
pure homogeneous freezing experiments with no heterogeneous IN are performed.
Since subgrid temperature perturbation and vertical velocity play a critical role in determining ice nucleation and ice crystal number concentrations (e.g., KB08; WP10), in another set of experiments (wCAM5), the
subgrid vertical velocity of Gary [2006, 2008] is replaced with the subgrid vertical velocity used in the standard CAM5.1 [Gettelman et al., 2012]. Equation (1) is used to convert the subgrid vertical velocity to the subgrid temperature perturbation used in the WP10 cirrus scheme.
In the third set of experiments (wCAM5_aCAM5), the same aerosol population (dust and sulfate) and the
same subgrid vertical velocity as CAM5.1 are applied. Here coarse mode dust particles serve as
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SH MID 100−200hPa ANN
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Figure 1. Frequency of occurrence of clear-sky RHi in the SH midlatitudes (60 S–30 S) (SH MID, left plot), in the tropics (30 S–30 N) (TROPIC, middle plot), and the NH midlatitudes
(30 N–60 N) (NH MID, right plot) at 100–200 hPa (top plot) and at 200–300 hPa (bottom plot) from five simulations with the Gary subgrid temperature formula (listed in Table 1). Observations from MOZAIC data in the tropics and in the NH midlatitudes are also shown.
heterogeneous IN with fdust51.0, and only sulfate particles with diameter larger than 100 nm participate in
homogeneous freezing. A homogeneous freezing only experiment is also performed.
Aerosol and precursor emissions used in our experiments are described in Liu et al. [2012b]. Anthropogenic
SO2, BC, and POM emissions are from the Lamarque et al. [2010] IPCC AR5 emission data set. The years 2000
and 1850 are chosen to represent the present day (PD) and the preindustrial (PI) time, respectively. Greenhouse
gases, sea surface temperature (SST), and sea ice are prescribed as climatological values. The model runs with
the finite volume dynamical core at 1.9 3 2.5 horizontal resolution with 30 vertical levels and a time step of
30 min. Each experiment was integrated for 6 years, and results from the last 5 years are analyzed here.
3. Evaluation With Observations in the PD Simulations
3.1. Ice Supersaturation
As saturation adjustment is removed for cirrus condensation and subgrid-scale supersaturation is used to
determine ice cloud formation and ice nucleation along with subgrid vertical velocity and heterogeneous
IN in the new scheme, simulated subgrid supersaturation is therefore important for model evaluation and
for understanding how different nucleation mechanisms compete with each other. Figures 1 and 2 show
the PDF of clear-sky RHi within two layers (100–200 hPa, and 200–300 hPa) and in three different regions
(the Southern Hemisphere (SH) midlatitudes: 60 S–30 S; the tropics: 30 S–30 N; and the Northern Hemisphere (NH) midlatitudes: 30 N–60 N), with Figure 1 from the Gary formula and Figure 2 from the CAM5 formula. The PDF of clear-sky RHi is calculated based on the clear-sky mean RHi and the PDF of temperatures
used in the model, weighed by the clear-sky fraction. Also shown in Figures 1 and 2 is the PDF of RHi from
the Measurement of Ozone and Water Vapor by Airbus In-service Aircraft (MOZAIC) campaign [Gierens et al.,
1999] (see a similar comparison in WP10, Figure 6).
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10
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10
0
50
100
RHi(%)
150
−2
10
−3
10
−4
10
0
50
100
RHi(%)
150
Figure 2. The same as Figure 1, but for seven simulations with the CAM5 subgrid temperature formula (listed in Table 1).
Figures 1 and 2 show that the PDF of RHi strongly depends on freezing modes, the number concentration
of heterogeneous IN, and the formulas of subgrid temperature perturbation. For the pure homogeneous
freezing cases (wGary_HOM, wCAM5_HOM, and wCAM5_aCAM5_HOM), RHi extends to as high as 150%,
and the PDF shapes are close to each other for all three cases. When heterogeneous IN are added, the frequency of RHi that exceeds the threshold RHi of heterogeneous freezing (around 120%) gradually decreases
with increasing fdust. At fdust51, homogeneous freezing rarely occurs in any cases as the frequency of RHi
that exceeds the threshold RHi of homogeneous freezing (around 150%) is extremely low. As the subgrid
temperature perturbation and vertical velocity from the Gary and CAM5 formulas have different altitudedependence (Figure 3), the PDF of RHi from the two formulas shows different altitude-dependence as well.
For the CAM5 formula, as the subgrid temperature perturbation generally decreases with increasing altitude
over both midlatitudes and tropics (Figure 3), even fdust50.01 strongly suppresses homogeneous freezing
at 100–200 hPa (subgrid vertical velocity is low), while fdust51.0 allows some homogeneous freezing to
occur at 200–300 hPa (subgrid vertical velocity is high). In contrast, for the Gary formula, as subgrid temperature perturbation increases with increasing altitude according to the gravity wave theory [Gary, 2006],
even fdust51.0 allows some homogeneous freezing to occur at 100–200 hPa (subgrid vertical velocity is
high), especially over the SH midlatitudes and tropics.
The PDF of RHi also shows different hemispherical contrasts from the two subgrid vertical velocity formulas.
With the Gary formula, the PDF of RHi in the SH is insensitive to fdust for fdust ranging from 0.01 to 0.10, and
is close to that of the pure homogeneous freezing case, while with the CAM5 formula, the PDF of RHi is similar in the two hemispheres.
Compared with MOSAIC, our results suggest that the RHi PDF from a small fdust ranging from 0.01 to 0.10
agrees better with observations than that from fdust51.0. fdust51.0 predicts too little occurrence of high
supersaturaiton and leads to a heterogeneous-freezing-dominated regime. These results are consistent with
Zhang et al. [2013] who shows that results from a small fdust (0.05-0.10) agree better with observations. Our
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SH MID ANN
0
−1
10
−1
−1
−2
−3
10
−4
10
10
Distribution function
Distribution function
10
10
−2
10
−3
10
−4
10
0
0.2
0.4
0.6
sigmaT (K)
0.8
1
NH MID ANN
0
10
10
Distribution function
TROPIC ANN
0
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0.2
0.4
0.6
sigmaT (K)
0.8
−3
10
−4
10
wGary 100−200 hPa
wCAM5 100−200 hPa
wGary 200−300 hPa
wCAM5 200−300 hPa
0
−2
10
1
0
0.2
0.4
0.6
sigmaT (K)
0.8
1
Figure 3. The frequency distribution of the subgrid temperature perturbations from the Gary (red) and CAM5 (blue) formulas in the SH midlatitudes (60 S–30 S) (SH MID, left plot), in
the tropics (30 S–30 N) (TROPIC, middle plot), and the NH midlatitudes (30 N–60 N) (NH MID, right plot) at 100–200 hPa (solid lines) and at 200–300 hPa (dash lines).
results are also consistent with Kuebbeler et al. [2014], who uses a fdust50.05 for immersion freezing. The
hemispherical contrast in the RHi PDF for a fdust ranging from 0.01 to 0.10 with the Gary formula agrees best
with the observed hemispherical contrast from the INCA field campaign [Haag et al., 2003], which showed
that RHi extends to higher value in the SH midlatitudes than in the NH midlatitudes. The hemispherical contrast in the RHi PDF with the CAM5 formula is much smaller.
3.2. Ice Crystal Number Concentrations
Figures 4a–4c and 5a–5c compare the frequency distribution of simulated ice crystal number concentrations with those measured in Kramer et al. [2009]. FSSP (Forward Scattering Spectrometer Probe) 100 and
300 instruments were used to measure the ice crystal number concentration. The FSSP 100 and FSSP 300
sample particles in the size range of 1.5–15 and 2–20 lm diameter, respectively. As shown in Kramer et al.
Figure 4. The frequency distribution of ice crystal number concentrations at different temperature ranges from five simulations with the Gary subgrid temperature formula (listed in
Table 1). (top) Results over the regions where observations in Kramer et al. [2009] were collected: (a) 185–195 K; (b) 195–205 K; (c) 205–215 K, and (bottom) Results over the ARM SGP site
during the SPARTICUS field campaign [Zhang et al., 2013]: (d) 205–215 K; (e) 215–225 K; (f) 225–235 K.
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Figure 5. The same as Figure 4, but for seven simulations with the CAM5 subgrid temperature formula.
[2009], at least 80%, but typically 90% of total ice crystal number concentrations are within the FSSP size
range. Ice crystal number concentrations from 28 flights in tropical, midlatitude, and Arctic field experiments are examined in three temperature ranges: 185–195 K, 195–205 K, and 205–215 K. Data collected at
warmer temperatures may be contaminated by shattering effects [Kramer et al., 2009] and are not used in
this study. Model results are sampled over the six regions reported in Kramer et al. [2009] (see Table 3 in
their paper for the flight information).
At temperatures below 205 K, observations show low ice crystal number concentrations, with most ice crystal number concentrations below 100 L21 and ice crystal number concentrations below 10 L21 are also frequently observed. The low ice crystal number concentrations at the low temperatures have led to the
suggestion that ice crystals are primarily formed through heterogeneous freezing in the tropical tropopause
layer [Kramer et al., 2009; Froyd et al., 2010; Jensen et al., 2010; Jensen et al., 2013]. With the Gary subgrid
temperature formula (Figure 4), simulated ice crystal number concentrations are generally higher than
observations, especially at 195–205 K. At fdust50.01, 0.05, and 0.10, heterogeneous IN has little effects on
simulated ice crystal number concentrations. However, fdust51.0 reduces ice crystal number concentrations
and shifts the distribution to the lower ice crystal number concentrations.
With the CAM5 subgrid temperature formula (Figure 5), the pure homogeneous freezing cases (wCAM5_HOM and wCAM5_aCAM5_HOM) produce lower ice crystal number concentrations compared to the Gary
formula. This can be partly explained by the low subgrid temperature and therefore the low vertical velocity
from the CAM5 formula (Figure 3). It is interesting to note that the case of wCAM5_HOM produces double
peaks in the simulated frequency distribution of ice crystal number concentration at 185–195 K, which may
result from the subgrid vertical velocity distribution. When 1% of dust particles serve as heterogeneous IN
(wCAM5_dust0.01), the first peak at about 10 L21 disappears and shifts to lower ice crystal number concentrations (1–2 L21), while the second peak at the higher ice crystal number concentrations remains. Further
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Figure 6. The frequency distribution of ice water content at three temperature ranges over the regions where observations in Luebke et al. [2013] were collected: (a) < 205 K, (b) 205–
227 K, and (c) > 227 K, from the five simulations with the Gary formula (listed in Table 1).
increasing fdust gradually shifts the first peak to higher ice crystal number concentration while the second
peak shifts to lower ice crystal number concentrations. When fdust51 (wCAM5_dust1.00), the frequency distribution of ice crystal number concentrations become single mode. When only sulfate particles larger than
100 nm in diameter are used for homogeneous freezing, the pure homogeneous freezing case (wCAM5_aCAM5_HOM) produces substantially lower ice crystal number concentrations with a peak between 10 and
100 L21, typical ice crystal number concentrations from heterogeneous freezing. Adding 100% of coarse
mode dust particles as heterogeneous IN only slightly shift the distribution to lower ice crystal number concentrations (wCAM5_aCAM5).
At temperatures between 205 K and 215 K, observed ice crystal number concentrations are substantially
higher, with a peak between 100 and 500 L21, and model results agree better with observations. The results
from the Gary and CAM5 formulas are closer to each other than at lower temperatures, though heterogeneous IN have larger impact with the CAM5 formula, which can be explained by the smaller subgrid vertical
velocities from the CAM5 formula at these temperature ranges. The results with the CAM5 formula show
that using sulfate particles with diameter larger than 100 nm for homogeneous freezing leads to too few
ice crystals at 205–215K.
Simulated ice crystal number concentrations are further compared with those observed during the SPARTICUS field campaign [Zhang et al., 2013] in Figures 4 and 5. The SPARTICUS field campaign took place from
January 2010 to June 2010 over the Southern Great Plain (SGP) site of the DOE Atmospheric Radiation Measurement (ARM) Program. Ice crystal number concentrations and size distributions were measured by the 2D-S probes with improved probe tips designed to reduce the shattering effects of larger ice particles [Lawson, 2011]. The observational data have a frequency of 1 Hz, while the instantaneous model output was
sampled every 6 h. Observational data shows increasing ice crystal number concentrations with decreasing
temperatures, with peak ice number concentrations of 20–50 L21 at 225–235 K, 100–200 L21 at 215–225 K,
and 1000 L21 at 205–215 K. The model results are in reasonable agreement with observations at 215–225 K,
while the model overestimates ice crystal number concentrations at the warmer temperatures (225–235 K)
and underestimates ice crystal number concentrations at the colder temperatures (205–215 K). It is interesting to note that, the overestimation of ice crystal number concentrations at 225–235 K is persistent among
all experiments. Heterogeneous freezing, more heterogeneous IN or different subgrid vertical velocity formulas help little in this regard. The overestimation of ice crystal number at this temperature range is also
evident for CAM5 simulations with two different ice nucleation parameterizations in Zhang et al. [2013]. We
note that ice crystal number concentrations observed in this temperature range are much higher from the
NASA Mid-latitude Airborne Cirrus Properties Experiment (MACPEX), with a peak at around 100 L21 and
dominated by anvil clouds [Zhang et al., 2013].
At 205–215 K, simulated ice crystal number concentrations with the pure homogeneous freezing simulation
agrees better with observations, which may suggest that homogeneous freezing dominates ice formation
at this temperature range over this location, consistent with Zhang et al. [2013]. We note that ice crystal
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Figure 7. The same as Figure 6, but for seven simulations with the CAM5 formula (listed in Table 1).
number concentrations at this temperature range observed from the SPARTICUS field observations are substantially larger than those from Kramer et al. [2009]. Different cloud regimes and/or different geographical
locations might explain the differences.
3.3. Ice Water Content
Figures 6 and 7 compare the PDF of ice water content (IWC) at three temperature ranges (<205 K, 205–
227 K, and > 227 K) with IWC climatology documented in Luebke et al. [2013]. Luebke et al. [2013] compiled IWC climatology from 13 field campaign observations that cover the latitude range from 22 S to
68 N, the altitude range from 5 to 20 km, and the temperature range 182–249 K. It represents 38.2 h of
cloud sampling (See Table 1 in Luebke et al. [2013] for details of each field campaign). Two different total
water instruments are included: one is the University of Colorado Closed-path Laser Hygrometer (CLH) for
four field campaigns, while the other is the Fast In Situ Stratospheric Hygrometer (FISH) for nine field campaigns. To derive IWC, ambient water vapor measurements from the Jet Propulsion Laboratory (JPL) tunable diode laser (TDL) hygrometer (JLH) (for CLH), and the Lyman-a Stratospheric Hygrometer (FLASH) of
the Central Aerological Observatory and the open-path tunable diode laser spectrometer (OJSTER) (for
FISH) were used. The model results are sampled over the 13 regions reported in Luebke et al. [2013]. The
peak IWC is 20, and 40 ppmv for temperature ranges of 205–227 K, and > 227 K, respectively. At temperatures below 205 K, observations show a bimodal distribution with two peaks located at 0.1 and 1 ppmv,
respectively.
Simulated IWC PDFs are close to each other, with little dependence on the subgrid temperature perturbations, nucleation modes, or the number concentrations of heterogeneous IN. Model results are in reasonable agreement with observations, though observations show a broader distribution. This is expected, as
observations are point observations, while model values represent model output over the cloudy part of a
GCM grid box. The cut-off IWC of 0.1 ppmv in model simulations comes from the low limit used in CAM5.1,
though observations contain a large amount of data with IWC below 0.1 ppmv at temperature below 205 K.
This may suggest that a lower limit of IWC is desirable at lower temperature in the model.
Simulated IWC are overestimated for temperatures below 205 K and heterogeneous IN slightly shifts the
PDF of IWC to lower IWC below 205 K, which may come from larger sedimentation due to lower ice crystal
number concentrations from heterogeneous freezing.
3.4. Global Results
Table 2 lists the global, annual mean model fields in CAM5 with the WP10 cirrus scheme, along with results
from CAM5.1 and observations. Results from the new scheme are shown for three cases that include both
homogeneous and heterogeneous freezing (wGary_dust0.10, wCAM5_dust0.10, and wCAM5_aCAM5).
wGary_dust0.10 and wCAM5_dust0.10 are chosen since fdust50.10 produces better RHi PDFs and is also
close to those used in other studies [Zhang et al., 2013; Kuebbeler et al., 2014]. For wCAM5_aCAM5, the
same coarse mode dust particle as those in CAM5.1 is used as heterogeneous IN. The effects of heterogeneous IN on simulated cloud properties and radiative fluxes will be further discussed in section 4.
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Table 2. Annual Global Mean Cloud and Radiation Parameters from CAM5 Present-Day Simulations and Observationsa
CLDTOT (%)
CLDLOW (%)
CLDHGH (%)
SWCF (W m22)
LWCF (W m22)
LWP (g m22)
IWP (g m22)
TIWP (g m
22
)
Nd (1010 m22)
Ni (1010 m22)
PRECT (mm day21)
Wmv (kg m22)
AOD (-)
wGary_dust0.10
wCAM5_dust0.10
wCAM5_aCAM5
CAM5.1
CAM5.1_default
Observations
64.2
45.2
34.6
254.26
24.84
53.20
(37.72)e
19.27
(12.22)
64.05
66.8
45.6
38.6
257.70
29.31
54.33
(38.96)
21.92
(15.60)
66.89
64.1
45.1
35.4
253.63
24.38
52.76
(37.13)
19.85
(12.60)
63.79
65.64
45.28
38.91
254.78
25.40
53.23
(37.30)
18.82
(12.15)
66.93
64.08
43.61
38.10
252.16
24.04
44.74
(29.09)
17.80
(11.04)
66.05
65–75b
#
21–33c
246 to 253d
27–31d
50–87f
1.77
0.011
2.979
25.67
0.14
1.81
0.017
2.885
26.38
0.14
1.75
0.0078
2.998
25.47
0.14
1.75
0.010
2.973
25.75
0.14
1.38
0.0093
2.966
25.609
0.12
#
10–65g
75630h
#
#
2.61i
24.6j
0.15k
a
Total cloud fraction (CLDTOT), low cloud fraction (CLDLOW), high cloud fraction (CLDHGH), shortwave cloud forcing (SWCF), longwave cloud forcing (LWCF), column-integrated grid-mean hydrometeor water path (LWP, liquid water path; IWP, ice water path; TIWP,
total ice water path (TIWP5IWP1snow water path)), column-integrated grid-mean hydrometeor number concentrations (Nd, cloud
droplets; Ni, ice crystals), precipitation rate (PRECT), column-integrated water vapor (Wmv), and aerosol optical depth (AOD).
b
Total cloud fraction observations are obtained from ISCCP for the years 1983–2001 [Rossow and Schiffer, 1999], MODIS data for the
years 2001–2004 [Platnick, 2003] and HIRS data for the years 1979–2001 [Wylie et al., 2005].
c
High cloud fraction observations are obtained from ISCCP data for the years 1983–2001 and HIRS for the years 1979–2001.
d
SWCF, LWCF are taken from ERBE for the years 1985–1989 [Kiehl and Trenberth, 1997] and CERES for the years 2000–2005 as listed in
Table 4 of Loeb et al. [2009].
e
Numbers in parenthesis are from large-scale clouds.
f
Liquid water path is derived from SSM/I [for the years 1987–1994, Ferraro et al., 1996; for August 1993 and January 1994, Weng and
Grody, 1994; and for August 1987 and February 1988, Greenwald et al., 1993] and ISCCP for the year 1987 [Han et al., 1994]. SSM/I data
are restricted to oceans.
g
Total ice water path from NOAA NESDIS, ISCCP, MODIS [Figure 18 in Waliser et al., 2009].
h
Total ice water path from CloudSat [Austin et al., 2009].
i
Precipitation rate is taken from the Global Precipitation Climatology Project (GPCP) for the years 1979–2003 [Adler et al., 2003]
(http://www.gewex.org/gpcpdata).
j
Precipitable water is from the NASA Water Vapor Project (NVAP) for the years 1988–1999 [Randel et al., 1996]. (http://eosweb.larc.
nasa.gov/PRODOCS/nvap/table_nvap.html).
k
AOD is from a satellite retrieval composite [Kinne et al., 2006].
Column-integrated droplet number concentrations (Nd) from the WP10 scheme are close to each other
(ranging from1.75 to 1.81 3 1010 m22) and to that in CAM5.1 (1.75 3 1010 m22), but are larger than that in
CAM5.1_default (1.38 3 1010 m22). The larger column droplet number concentration in our version of the
model is caused by a change in how freshly activated droplets are updated, as discussed in section 2.3. The
larger column-integrated droplet number concentrations agree better with results from some other conventional global model results (e.g., 4–19 3 1010 m22 in Lohmann et al. [2007]; around 2.3 3 1010 m22 from
WP10) and from a MMF model (2.3 3 1010 m22 from Wang et al. [2011]). This change in how freshly activated droplets are updated leads to larger LWP. LWP ranges from 52.8 to 54.3 g m22 in CAM5 with WP10,
close to that in CAM5.1 (53.23 g m22) and is larger than that in CAM5.1_default (44.74 g m22) and agrees
better with observations. Increases in droplet number concentration and LWP further affect aerosol optical
depth, as conversion rate of liquid water to precipitation, which is used for wet scavenging of aerosols, is
smaller. Aerosol optical depth (AOD) increases to about 0.137 in CAM5 with the WP10 scheme and in
CAM5.1, compared to 0.121 in CAM5.1_default.
Column-integrated ice crystal number concentrations (Ni) range from 0.0078 3 1010 m22 to 0.017 3 1010
m22 with the WP10 scheme, while it is 0.010 3 1010 m22 in CAM5.1. When the same subgrid vertical velocity formula and the same aerosol population as CAM5.1 are used for ice nucleation in CAM5 with the WP10
scheme (wCAM5_aCAM5), column-integrated ice crystal number concentration is lower than that in
CAM5.1 (0.0078 3 1010 m22 versus 0.010 3 1010 m22). This may partly come from the fact that in WP10, ice
nucleation only occurs in the clear-sky part that have relative humidity exceeding the threshold relative
humidity for ice nucleation, while in CAM5.1, ice nucleation can happen in a grid whenever the grid-mean
relative humidity is larger than a certain threshold, independent of the preexisting ice crystal number concentrations. Lower ice crystal number concentrations are simulated in the upper troposphere above 200
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Figure 8. Annual-averaged zonal-mean in-cloud ice crystal number concentrations (unit: L21) from 4 present day simulations listed in
Table 2: (a) wGary_dust0.10; (b) wCAM5_dust0.10; (c) wCAM5_aCAM5; (d) CAM5.1. CAM5 uses a hybrid vertical coordinate and the pressure at the kth model level is given by p(k) 5 A(k) p0 1 B(k) ps, where ps is surface pressure, p0 is a specified constant pressure (1000 hPa),
and A and B are coefficients. Data are plotted as a function of this hybrid vertical coordinate times 1000, and labeled as ‘‘Approximate
Pressure.’’
hPa over the tropics in wCAM5_aCAM5 than in CAM5.1, while higher ice crystal number concentrations are
simulated in the midlatitude upper troposphere (Figure 8).
When all sulfate particles in the Aitken mode instead of sulfate particles with diameter larger than 100 nm
are allowed to form ice crystals through homogeneous freezing (wCAM5_dust0.10), column-integrated ice
crystal number concentration increases substantially from 0.0078 3 1010 m22 to 0.017 3 1010 m22. Simulated ice crystal number concentrations in the upper troposphere are higher over all latitudes in wCAM5_dust0.10 compared to wCAM5_aCAM5 (Figure 8). With the Gary subgrid vertical velocity formula, columnintegrated ice crystal number concentration decreases from 0.017 3 1010 m22 to 0.011 3 1010 m22, close
to that in CAM5.1. This may come from the fact that the subgrid vertical velocity from the Gary formula is
lower than that from the CAM5 formula at low altitudes. Simulated high ice crystal number concentrations
(larger than 200 L21) extend to high altitudes with the Gary formula (Figure 8), as the subgrid temperatures
increase with altitude in the Gary formula while they decrease with altitude in the CAM5 formula (Figure 3).
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Table 3. Heterogeneous IN Effects on Cloud Properties and Radiative Fluxesa
wGary dust0.01
wGary_ dust0.05
wGary_dust0.10
wGary_dust1.0
wCAM5_dust0.01
wCAM5_dust0.05
wCAM5_dust0.10
wCAM5_dust1.0
wCAM5_aCAM5
0.53
20.76
20.23
0.01
20.23
20.001
20.13
0.005
0.016
0.015
20.60
1.17
21.66
20.49
20.03
20.58
20.003
20.31
0.007
0.045
0.040
21.34
1.40
22.11
20.71
20.11
20.81
20.005
20.38
0.011
0.052
0.051
21.73
1.72
22.02
20.31
20.35
20.71
20.005
20.44
0.024
0.073
0.059
21.96
2.21
22.51
20.30
20.31
20.68
20.010
20.51
0.018
0.058
0.059
21.98
3.35
23.76
20.41
20.44
20.93
20.013
20.84
0.014
0.074
0.077
22.84
4.10
24.61
20.50
20.48
20.92
20.015
20.95
0.011
0.091
0.096
23.57
5.21
26.14
20.93
20.55
21.59
20.018
21.21
20.002
0.151
0.147
24.99
0.84
21.16
20.32
20.15
20.48
20.002
20.21
0.033
0.021
0.027
21.01
SWCF
LWCF
CFb
FLNTCc
FTOAd
Ni
Wmv
CLDGHG
PRECCe
PRECT
FATMf
a
These are calculated as the differences between simulations with heterogeneous IN and the corresponding pure homogeneous freezing simulations. For example, wCAM5_aCAM5
shows the difference between wCAM5_aCAM5 and wCAM5_aCAM5_HOM (Table 1).
b
Change in the net cloud forcing (shortwave1longwave) (W m22).
c
Clear-sky longwave radiative fluxes at the top of the atmosphere (W m22).
d
Change in the net TOA radiative fluxes (shortwave1longwave) (W m22).
e
Change in convective precipitation (mm day21).
f
Change in the net atmosphere absorption (shortwave1longwave) (W m22).
We note that the column-integrated ice crystal number concentrations in this study and that in CAM5.1 are
low compared with results from some other global climate models. For example, column-integrated ice
crystal number concentrations ranges 0.1–0.7 3 1010 m22 in Lohmann et al. [2008], while it ranges 0.02–
0.09 3 1010 m22 in Wang and Penner [2010]. The differences between this study and other studies can
come from many factors, such as how ice crystal number concentrations in mixed-phase clouds are calculated and how heterogeneous freezing is treated in cirrus.
Simulated ice water path (IWP) with the WP10 scheme ranges from 19.3 to 21.9 g m22, slightly larger than
that in CAM5.1 (18.8 g m22). Simulated total ice water path (TIWP, ice water path 1 snow water path) with
the WP10 scheme ranges from 63.8 to 66.9 g m22, comparable to that in CAM5.1 (66.9 g m22). This is
smaller than that retrieved from CloudSAT1CALIPSO products (75 to 120 g m22, Li et al. [2012]) and close
to that retrieved from MODIS (60 g m22), but is much larger than estimates from ISCCP (around 35 g m22)
and NOAA NESDIS (around 10 g m22) [IWP from MODIS, ISCCP, and NESDIS can be found in Figure 18 in
Waliser et al., 2009].
Simulated shortwave cloud forcing (SWCF) and longwave cloud forcing (LWCF) in wGary_dust0.10 and
wCAM5_aCAM5 are close to those in CAM5.1, and in reasonable agreement with observations (Table 2
and Figure 9). SWCF and LWCF in wCAM5_dust0.10 are larger than those in CAM5.1 (Table 2 and Figure
9). This is mainly caused by the larger ice crystal number concentrations in wCAM5_dust0.10 than in
CAM5.1.
4. The Effects of Heterogeneous IN in the PD Simulations
In this section, we examine how heterogeneous IN affect cloud properties and radative fluxes. Table 3 summarizes changes in simulated cloud properties and radiative fluxes from heterogeneous IN with the WP10
scheme by comparing model results that include both heterogeneous freezing and homogeneous freezing
to the corresponding pure homogeneous freezing cases. Our results show that the effects of IN on cirrus
strongly depend on the treatment of subgrid vertical velocity, the population of heterogeneous IN, and the
population of sulfate particles participating in homogeneous freezing. Our results further show that
changes in clear-sky longwave radiative fluxes can contribute significantly to changes in the total radiative
fluxes because of changes in water vapor, depending on the subgrid vertical velocity formulas in the upper
troposphere.
With the Gary formula (wGary), heterogeneous IN change shortwave cloud forcing by 0.53 to 1.72 W m22
and change longwave cloud forcing by 20.76 to 22.02 W m22. Changes in net cloud forcing (shortwave 1 longwave) are much smaller, ranging from 20.23 to 20.71 W m22, as changes in longwave and shortwave cloud forcing mostly compensate each other. Changes in longwave clear-sky radiative fluxes are
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generally small with the Gary
formula (less than 0.11 W m22),
except at fdust51.0 (wGary_dust1.00). For the case of wGary_dust1.00, the change in the
clear-sky longwave flux is similar to the change in net cloud
forcing (20.35 W m22 versus
20.31 W m22). This can be
explained by the changes in
the RHi PDF in the upper troposphere (Figure 1). When fdust
is small (0.01, 0.05, and 0.10),
the RHi PDF in the upper troposphere is close to that of the
pure homogeneous freezing
case. However, at fdust51.0,
heterogeneous IN strongly
suppress the occurrence of
homogeneous freezing,
reduces the relative humidity,
and water vapor in the upper
troposphere, which decreases
22
in the clear-sky longwave
Figure 9. Annual average zonal-mean (a) shortwave cloud forcing (SWCF, W m ), and (b)
longwave cloud forcing (LWCF, W m22). Model results are from those listed in Table 2, while
fluxes. The large contribution
observations are from CERES data [Loeb et al., 2009].
from the clear-sky longwave
fluxes in this case is consistent
with what is found in WP10. The amplification in the longwave fluxes from the water vapor increase due to
cirrus cloud seeding has also been noted in Storelvmo et al. [2013].
With the CAM5 subgrid temperature formula, changes in cloud forcing by heterogeneous IN are larger.
Changes in shortwave and longwave cloud forcing by heterogeneous IN range from 2.21 to 5.21 W m22,
and from 22.51 to 26.13 W m22, respectively. Changes in net cloud forcing range from 20.30 to 20.94 W
m22. Changes in clear-sky radiative fluxes are an important component of changes in total radiative fluxes
with the CAM5 formula. For example, at fdust50.01 (wCAM5_dust0.01), the change in net cloud forcing is
0.30 W m22, while the change in clear-sky longwave flux is comparable and is 0.31 W m22 (Table 3). This
can again be explained by the changes in the RHi PDF in the upper troposphere (Figure 2). Unlike the simulations with the Gary formula, heterogeneous IN significantly change the RHi PDF in the upper troposphere
in the simulations with the CAM5 formula, even with fdust50.01. As discussed in Section 3.1, the subgrid vertical velocity from the CAM5 formula is generally lower in the upper troposphere, and therefore even a
small amount of dust particles serving as IN can significantly change ice crystal number concentrations and
water vapor in the upper troposphere.
Increasing heterogeneous IN gradually increases high cloud amount (CLDHGH) with the Gary formula. With the
CAM5 formula, however, this is only true when fdust increases from 0.0 to 0.01, and high cloud amount decreases
when IN increases further. On the one hand, increasing heterogeneous IN makes cloud formation easier due to the
reduced threshold RHi for cirrus formation, leading to more cirrus. On the other hand, increasing heterogeneous IN
decreases ice crystal number concentrations, which increases sedimentation and decreases ice cloud amount. The
first factor wins with the Gary formula, while the second factor wins in most cases with the CAM5 formula.
Changes in surface precipitation (PRECT) are mostly from convective precipitation (PRECC), and changes in
stratiform precipitation are small (not shown). As SST and sea ice are prescribed from the climatological
data in our simulations, changes in precipitation are from the fast atmospheric response [Andrews et al.,
2010]. The fast atmospheric response refers to the adjustment of the stratosphere, troposphere, and the
land surface before any changes in global- and annual-mean surface temperature occurs and typically has a
timescale of weeks to months [Gregory and Webb, 2008; Bala et al., 2010; Lohmann et al., 2010]. As shown in
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Andrews et al. [2010], the fast
response in precipitation is strongly
correlated with the atmospheric component of changes in radiative fluxes,
which is defined as changes in the
atmospheric absorption and is calculated as the difference between
changes in net TOA radiative fluxes
and changes in net surface radiative
fluxes. This strong correlation can be
understood from the energy transfer
point of view [e.g., Allen and Ingram,
2002]. Due to a small heat capacity,
the atmosphere cannot store heat,
and radiative cooling of the atmosphere is balanced by latent heating
(precipitation) and sensible heating.
Since Bowen’s ratio (ratio of sensible
to latent heat fluxes) is about 0.2, it is
Figure 10. The scatter plot of the percentage change in surface precipitation (%)
likely that the latent heat response
versus the changes in the atmospheric absorption (FATM, W m22) from different
will dominate over sensible heat
model experiments: changes from heterogeneous IN with the Gary formula (wGary
IN effects) and with the CAM5 formula (wCAM5 IN effects) and with the CAM5 forfluxes. Therefore, any perturbation to
mula 1 CAM5 aerosols for ice nucleation (wCAM5_aCAM5 IN effects) (listed in Table
the atmospheric radiative cooling is
3); and changes from anthropogenic aerosols with the Gary formula (wGary anth.
balanced primarily by a change in
aerosol effects) and with the CAM5 formula (wCAM5 anth. aerosol effects) (listed in
Table 4).
precipitation. This can also be understood from changes in atmospheric
stability, which is caused by fast changes in troposphere temperatures above an unchanged surface [Dong
et al., 2009].
Figure 10 shows the scatter plot of changes in surface precipitation and changes in the atmospheric absorption (FATM in Table 3) from heterogeneous IN. The percent changes in surface precipitation are proportional
to the changes in the atmospheric component of the radiative forcing, consistent with Andrews et al. [2010]
(their Figure 2e). Changes in precipitation are less related to either changes in TOA net radiative fluxes or
changes in surface radiative fluxes (Table 3), which is also consistent with Andrews et al. [2010]. These results
suggest that the fast response in precipitation from aerosol effects on cirrus is consistent with the fast
response in precipitation from other forcing agencies examined in Andrews et al. [2010], such as black carbon and CO2. As the atmospheric components of aerosol effects on ice clouds are large (2 to 4 times the
changes in the TOA radiative fluxes, see Table 3) and are proportional to the fast response in surface precipitation, our results suggest that it is important to examine the atmospheric component of the radiative forcing and its impact on the hydrological cycle for studying aerosol effects on cirrus.
The change in precipitation through the fast atmosphere response helps to explain increases in surface precipitation from cirrus cloud seeding in CAM5 simulations with prescribed SST [Storelvmo and Herger, 2014].
This also helps to explain why the global annual mean surface precipitation has nearly no change from cirrus cloud seeding in coupled simulations [Muri et al., 2014]. In the coupled simulations, precipitation
increase through the fast response due to the reduced atmospheric absorption compensates precipitation
reduction through the slow response due to surface cooling from cirrus cloud seeding, which leads to negligible change in the global annual mean surface precipitation. This may make cirrus cloud seeding an attractive alternative compared to the geoengineering approach of solar radiation management (SRM) which has
the side effect of reducing surface precipitation [Bala et al., 2008].
5. Changes in Anthropogenic Aerosol Forcing From Different Cirrus Treatments
In this section, we examine how the effects of anthropogenic aerosols on the radiative fluxes may depend
on different cirrus treatments discussed above, namely, different ice nucleation modes, different heterogeneous IN number concentrations, and different subgrid vertical velocity formulas. To examine this, for each
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Table 4. Anthropogenic Aerosol Effects on Cloud Properties and Radiative Fluxes, Calculated as the Difference Between the PD and PI
Simulations (PD-PI) (See Table 1 for the List of Experiments).
CAM5.1
wGary_HOM
wGary_dust0.01
wGary_dust0.05
wGary_dust0.10
wGary_dust1.0
wCAM5_HOM
wCAM5_dust0.01
wCAM5_dust0.05
wCAM5_dust0.10
wCAM5_dust1.0
wCAM5_aCAM5_HOM
wCAM5_aCAM5
SWCF
LWCF
FLNTC
FTOA
FATM
Nd
Ni
PRECT
21.91
21.69
21.70
21.52
21.44
21.25
22.99
22.70
22.24
22.01
21.47
21.39
21.24
0.60
0.60
0.48
0.31
0.21
0.02
1.66
1.32
1.03
0.81
0.24
0.22
0.03
0.27
0.09
0.20
0.29
0.28
0.16
0.21
0.31
0.26
0.17
0.07
0.25
0.25
21.44
21.36
21.36
21.33
21.30
21.52
21.41
21.54
21.31
21.21
21.52
21.34
21.33
1.06
0.96
0.94
0.85
0.78
0.64
1.64
1.54
1.43
1.18
0.75
0.83
0.69
0.44
0.43
0.43
0.43
0.42
0.42
0.49
0.48
0.46
0.44
0.44
0.42
0.41
9.0e-4
0.0012
0.0009
0.0006
0.0004
0.0000
0.0053
0.0030
0.0021
0.0015
0.0003
0.0003
0.0000
0.0255
20.022
20.023
20.017
20.015
20.016
20.040
20.036
20.038
20.032
20.016
20.019
20.016
simulation discussed in sections 3 and 4, a corresponding simulation with preindustrial (PI) aerosol and precursor emissions (the year of 1850 is chosen) was performed. Anthropogenic aerosol effects in clouds and
climate are calculated as the difference between PD and PI simulations and are shown in Table 4. Our
results show that different cirrus treatments can make significant differences in simulated anthropogenic
aerosol forcing.
With the Gary formula (wGary), anthropogenic aerosol effects on shortwave cloud forcing (SWCF) range
from 21.25 W m22 to 21.69 W m22, while anthropogenic aerosol effects on longwave cloud forcing
(LWCF) range from 0.02 W m22 to 0.60 W m22. When 100% dust particles act as heterogeneous IN (wGary_dust1.00), anthropogenic aerosol effects on longwave cloud forcing are small, close to zero. As discussed in
section 3.1, heterogeneous freezing dominates in this case. As heterogeneous IN are from dust particles
that rarely change from PI to PD (slightly decreases), anthropogenic aerosols have little effect on ice crystal
number concentrations and therefore on longwave cloud forcing. With the CAM5 subgrid vertical velocity
formula, anthropogenic aerosol effects on longwave and shortwave cloud forcing are generally larger.
Anthropogenic aerosol effects on shortwave cloud forcing range from 21.24 to 22.90 W m22, while the
effects on longwave cloud forcing range from 0.03 to 1.64 W m22.
It is interesting to note that the largest anthropogenic aerosol effects on longwave cloud forcing are from
the pure homogeneous freezing cases for both the Gary and CAM5 formulas. Anthropogenic aerosol effects
on longwave cloud forcing are 0.60 W m22, 1.66 W m22 and 0.22 W m22 for wGary_HOM, wCAM5_HOM,
and wCAM5_aCAM5_HOM, respectively. More sulfate particles participating in homogeneous freezing
(wCAM5_HOM versus wCAM5_aCAM5_HOM) and higher subgrid temperature perturbation (wCAM5_HOM
versus wGary_HOM) lead to larger changes in longwave cloud forcing. In our case, ice crystal number concentrations from homogenous freezing increase moderately with increasing sulfate number concentrations
from LP05 parameterization. This is in contrast to Karcher and Lohmann [2002] who showed that the number of ice crystals formed through homogeneous freezing is rather insensitive to the number of solution
aerosol particles. However, this is under the assumption that the timescale of depositional growth of the
pristine ice particles is faster compared to the timescale of the freezing event. Kay and Wood [2008] showed
that ice crystal concentrations from homogeneous freezing can be quite sensitive to aerosol concentration
when ice deposition coefficient is much less than 0.1 (a deposition coefficient of 0.5 is used in Karcher and
Lohmann [2002]). Using a cloud-system-resolving model with two-moment cloud microphysics scheme,
Muhlbauer et al. [2014] also showed that sulfate aerosol number concentrations available for homogeneous
freezing have virtually no effect on the microphysical properties and radiative impact of midlatitude and
subtropical cirrus and have only minor effect on tropical anvil cirrus. For midlatitude and subtropical cirrus,
the insensitivity of cirrus cloud properties to sulfate aerosol number concentrations is attributed to the fact
that cirrus formation in these clouds is dominated by heterogeneous freezing in their simulations. With a
time step of 10 s, it is not clear how well the supersaturation is simulated for ice freezing events and further
for the effects of sulfate aerosol number concentrations on simulated cirrus cloud properties in Muhlbauer
et al. [2014]. The discrepancy between this study and some other studies is now being further investigated
with cloud parcel models under a variety of conditions such as sulfate aerosol size distribution, updraft
velocity, numerics, etc. (Xiaohong Liu et al., manuscript in preparation, 2014).
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For anthropogenic aerosol effects on the net cloud forcing and on the net TOA fluxes, perturbations from
different cirrus treatments are much smaller, as the changes in longwave and shortwave cloud forcing
mostly compensate each other. Anthropogenic aerosol effects on the net cloud forcing range from 21.09
to 21.38 W m22, while the effects on the net TOA fluxes range from 21.21 to 21.54 W m22. The perturbations in the TOA anthropogenic aerosol radiative forcing from different cirrus treatments is similar to what
is found in Gettelman et al. [2012], with a standard deviation of 0.10 W m22 for both studies. This variation
can account for a significant portion of the total anthropogenic aerosol radiative forcing, and suggests the
need to further understand and quantify how aerosols affect cirrus.
In terms of the atmospheric component of the radiative forcing (atmospheric absorption, FATM in Table 4),
it ranges from 0.64 to 1.64 W m22 with a standard deviation of 0.34 W m22, which is much larger than the
perturbation in the TOA radiative forcing from cirrus treatment (a standard deviation of 0.10 W m22). This
suggests that different cirrus treatments explored here have even larger effects on the atmospheric component of the anthropogenic aerosol forcing than on the TOA radiative forcing. As the atmospheric component of the radiative forcing is directly related to the fast response in the hydrology cycle (Figure 10 and the
discussion in section 4), this suggests that there are even more urgent needs to improve our understanding
of how aerosols affect cirrus and further the hydrological cycle.
6. Summary
In this study, we have implemented a statistical cirrus scheme based on Karcher and Burkhardt [2008] and
Wang and Penner [2010] in CAM5 (version 5.1). This scheme tracks ice saturation ratio in the clear-sky and
cloudy portion of a grid box separately, and uses the subgrid saturation ratio in the clear-sky part to treat
ice nucleation and cloud growth. This scheme therefore provides a consistent treatment of ice nucleation
and cloud formation. In contrast, the default cirrus cloud scheme in CAM5 uses grid-mean saturation ratio
for ice nucleation, vapor deposition/sublimation, and ice cloud fraction [Gettelman et al., 2010], which can
cause some potential inconsistencies in the treatment of these processes. Sensitivity experiments are performed to evaluate the model performance and to further examine aerosol effects on cirrus through ice
nucleation.
Simulated ice supersaturation and ice crystal number concentrations strongly depend on the number concentrations of heterogeneous ice nuclei (IN), subgrid temperature formulas, and the number concentration
of sulfate particles participating in homogeneous freezing, while simulated ice water content is insensitive
to these perturbations.
Our results show that a fdust (the fraction of dust particles that can serve as heterogeneous IN) ranging from
0.01 to 0.10 produces the PDF of RHi in better agreement with observations (Figures 1 and 2). fdust51.00
produces too little homogeneous freezing, and leads to cirrus dominated by heterogeneous freezing. The
subgrid vertical velocity from the Gary formula [Gary, 2006, 2008] produces a heterogeneous-freezingdominated regime at warm temperatures and a homogeneous-freezing-dominated regime at cold temperatures. This can be partly explained by the fact that the subgrid temperature perturbation increases with
altitude in the Gary formula. As for the CAM5 subgrid temperature formula, the model predicts substantial
heterogeneous freezing in the upper troposphere (100–200 hPa) even at fdust50.01, due to the low subgrid
temperature perturbation predicted by the CAM5 formula. This points to the need of quantifying not only
the magnitude of subgrid temperature perturbation but also its altitude dependence.
We note that the PDFs of RHi simulated in CAM5 by its default cirrus scheme shows a strong spike at RHi
around 100% (Figure 10 in Gettelman et al. [2010]; Figure 6 in Liu et al. [2012a]; Figure 6 in Zhang et al.
[2013]), while no such spike exists in our simulations with the new statistical cirrus scheme (Figures 1 and
2). As grid-mean RHi is used to treat ice nucleation, ice cloud fraction, and vapor deposition/ice water sublimation in the CAM5’s default cirrus scheme, vapor deposition and ice water sublimation work to bring RHi
back to 100%, which leads to the RHi spike at around 100% in CAM5 simulations. In contrast, as the new
scheme uses clear-sky subgrid RHi to treat ice nucleation and cloud growth, and uses RHi in the cloudy part
to treat vapor deposition and ice sublimation, this helps to avoid the spike at RHi5100%.
Our results show that dust particles serving as heterogeneous IN can significantly change shortwave and
longwave cloud forcing. Depending on the subgrid vertical velocity formulas, and the population of dust
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particles and sulfate particles that can participate in ice nucleation, changes in shortwave cloud forcing can
range from 0.52 to 5.20 W m22, while changes in longwave cloud forcing can range from 20.76 to 26.10 W
m22. Different subgrid temperature formulas can lead to substantially different effects of heterogeneous IN.
The effects of heterogeneous IN on the net cloud forcing and the net TOA radiative fluxes are much more
moderate. Changes in the net cloud forcing ranges from 20.20 to 20.90 W m22, while changes in the net
TOA radiative fluxes has a significant clear-sky longwave component, and therefore a large variation, ranging from 20.23 to 21.59 W m22. The clear-sky longwave component strongly depends on the subgrid temperature formulas, and is generally smaller with the Gary formula than with the CAM5 formula.
Different cirrus treatments also have significant effects on simulated net anthropogenic aerosol forcing. The
net TOA anthropogenic forcing ranges from 21.21 W m22 to 21.54 W m22, with a standard deviation of
0.10 W m22, similar to that in Gettelman et al. [2012]. The warming effects on cirrus from anthropogenic
aerosols are mainly due to the effects of anthropogenic sulfate on homogeneous freezing. These warming
effects decrease with increasing heterogeneous IN, as homogeneous freezing is gradually replaced with
heterogeneous freezing. Further studies are ongoing to understand why homogeneous freezing is sensitive
to the number concentration of sulfate particles.
Our results show that aerosol effects on cirrus not only change the TOA radiative fluxes by a significant
amount, but also exert an even larger impact on the atmospheric component of the radiative fluxes (2 or 3
times the changes in TOA radiative fluxes) and therefore have a significant impact on the hydrology cycle
through the fast atmosphere response [Andrews et al., 2010]. The effects of heterogeneous IN on the atmospheric component of the radiative fluxes ranges from 20.60 W m22 to 26.00 W m22 (Table 3) while the
atmospheric component of anthropogenic aerosol forcing ranges from 0.64 W m22 to 1.64 W m22 due to
different cirrus treatments (Table 4). The percentage change in surface precipitation is proportional to
changes in the atmospheric component of the radiative fluxes, consistent with the results from other forcing agencies, such as BC, sulfate, and CO2 examined in Andrews et al. [2010]. Our results suggest that perturbation to cirrus through ice nucleation caused by natural aerosols (e.g., dust), anthropogenic sulfate, or
cloud seeding through geoengineering [Storelvmo et al., 2013] may cause significant perturbation to the
hydrology cycle, and warrants further examination.
The change in precipitation through the fast atmosphere response helps to explain why the global annual
mean surface precipitation has nearly no change from cirrus cloud seeding in coupled simulations [Muri
et al., 2014], as precipitation increase through the fast response due to the reduced atmospheric absorption
compensates precipitation reduction through the slow response due to surface cooling from cirrus cloud
seeding. This may make cirrus cloud seeding an attractive alternative compared to the geoengineering
approaches of solar radiation management (SRM) which have the side effect of reducing surface precipitation [Bala et al., 2008].
Acknowledgments
This study was supported by the DOE
Atmospheric System Research (ASR)
Program. The Pacific Northwest
National Laboratory is operated for
DOE by Battelle Memorial Institute
under contract DE-AC06-76RLO 1830.
We are grateful to Anna Luebke for
providing IWC observational data used
in Figures 6 and 7. We are also grateful
to Larry Berg and Heng Xiao for their
constructive comments. All model
output is stored on a local linux cluster
at the Pacific Northwest National
Laboratory and is available upon
request.
WANG ET AL.
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