1
Constraining cloud lifetime effects of aerosols using
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A-Train satellite observations
3
Minghuai Wang1, Steven Ghan1, Xiaohong Liu1, Tristan L’ Ecuyer2, Kai Zhang1,
4
Hugh Morrison3, Mikhail Ovchinnikov1, Richard Easter1, Roger Marchand4, Duli
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Chand1, Yun Qian1, and Joyce E. Penner5
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[1] Atmospheric Science and Global Change Division, Pacific Northwest National
7
Laboratory, Richland, Washington, United States
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[2] Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison,
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Wisconsin, United States
10
[3] Mesoscale and Microscale Meteorology Division, National Center for Atmospheric
11
Research, Boulder, Colorado, United States
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[4] Joint Institute for the Study of the Atmosphere and Ocean, University of Washington,
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Seattle, Washington, United States
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[5] Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann
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Arbor, Michigan, United States
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17
Supplementary Information
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19
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1
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1
Model descriptions
22
Two conventional global aerosol-climate models (NCAR CAM5 and ECHAM5-HAM2) and
23
a multi-scale aerosol-climate model (PNNL-MMF) are used in this study. These models have
24
been documented extensively elsewhere and are only briefly described here.
25
1.1
26
The
27
(http://www.cesm.ucar.edu/models/cesm1.0/cam/) is the latest version of the NCAR
28
Community Atmospheric model, which is the atmospheric component of the Community
29
Earth System Model (CESM1.0). CAM5 includes a range of enhancement and improvements
30
in the representation of physical processes compared to previous versions, which makes it
31
possible to simulate the full aerosol-cloud interactions in stratiform clouds, including aerosol
32
effects on warm, mixed-phase, and cirrus clouds.
33
The moist turbulence scheme is based on Bretherton and Park [Bretherton and Park, 2009],
34
which explicitly simulates stratus-radiation-turbulence interactions. The shallow convection
35
scheme is from Park and Bretherton [2009] and uses a realistic plume dilution equation and
36
closure that accurately simulates the spatial distribution of shallow convective activity. The
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deep convection parameterization is retained from CAM4.0 [Neale et al., 2008]. The two-
38
moment cloud microphysics scheme from Morrison and Gettelman [2008] is used to predict
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both the mass and number mixing ratios for cloud water and cloud ice with a diagnostic
40
formula for rain and snow. Autoconversion of cloud droplets to rain and accretion of cloud
41
droplets by raindrops are based on Khairoutdinov and Kogan [2000] (hereafter KK00).
42
The cloud ice microphysics was further modified to allow supersaturation and aerosol effects
43
on ice clouds [Gettelman et al., 2010]. The cloud macrophysics scheme is documented in Park
NCAR CAM5
Community
Atmospheric
Model
Version
5
(CAM5)
2
44
et al. [2011] for liquid clouds, and was further modified by Gettelman et al. [2010] for ice
45
clouds. The radiative transfer scheme in CAM5 is a broadband k-distribution radiation model
46
known as the Rapid Radiative Transfer Model for GCMs (RRTMG) [Iacono et al., 2003;
47
Iacono et al., 2008; Mlawer et al., 1997].
48
A modal approach is used to treat aerosols in CAM5 [Liu et al., 2011]. Aerosol size
49
distributions are represented by using three or seven log-normal modes. The three-mode
50
version adopted in this study has an Aitken mode, an accumulation mode, and a single coarse
51
mode. Aitken mode species include sulfate, secondary organic aerosol (SOA), and sea salt;
52
accumulation mode species include sulfate, SOA, black carbon (BC), primary organic matter
53
(POM), sea salt, and dust; coarse mode species include sulfate, sea salt, and dust. Species
54
mass and number mixing ratios are predicted for each mode, while mode widths are
55
prescribed. Both aerosols outside the cloud droplets (interstitial) and aerosols in the cloud
56
droplets (cloud-borne) are predicted. Aerosol nucleation (involving H2SO4 vapor),
57
condensation of trace gases (H2SO4 and semi-volatile organics) on existing aerosol particles,
58
and coagulation (Aitken and accumulation modes) are treated. Water uptake and optical
59
properties for each mode are expressed in terms of both relative humidity (accounting for
60
hysteresis) and the hygroscopicities of the mode’s component [Ghan and Zaveri, 2007].
61
1.2
62
The PNNL-MMF is documented in detail in Wang et al. [2011a; 2011b] and is only briefly
63
described here. It is an extension of the Colorado State University (CSU) MMF model[
64
Khairoutdinov et al., 2005; Khairoutdinov et al., 2008; Randall et al., 2003; Tao et al., 2009],
65
first developed by Khairoutdinov and Randall [2001]. The host GCM in the PNNL-MMF is
66
updated to NCAR CAM5. The embedded CRM in each GCM grid column is a two-
67
dimensional version of the System for Atmospheric Modeling (SAM) [Khairoutdinov and
The PNNL-MMF multi-scale aerosol-climate model
3
68
Randall, 2003], which replaces the conventional moist physics, convective cloud, turbulence,
69
and boundary layer parameterizations in CAM5. During each GCM time step (every 10
70
minutes), the CRM is forced by the large-scale temperature and moisture tendencies arising
71
from GCM-scale dynamical processes and feeds the cloud response back to the GCM-scale as
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heating and moistening terms in the large-scale budget equations for heat and moisture. The
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CRM runs continuously using a 20-s time step.
74
The version of the SAM CRM used in this study features a two-moment cloud microphysics
75
scheme [Morrison et al., 2005; 2009], which replaces the simple bulk microphysics used in
76
the original CSU MMF model, and is similar in many respects to the stratiform cloud
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microphysics scheme in CAM5 (e.g., KK00 is used in both CAM5 and MMF for
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autoconverion and accretion) [Gettelman et al., 2008; Morrison and Gettelman, 2008]. The
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new scheme predicts the number and mass mixing ratios of five hydrometeor types (cloud
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droplets, ice crystals, rain droplets, snow particles, and graupel particles). The precipitation
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hydrometeor types (rain, snow, and graupel) are fully prognostic in the CRM model, rather
82
than diagnostic in CAM5 [Morrison and Gettelman, 2008]. Droplet activation is calculated at
83
each CRM grid cell, based on the parameterization of Abdul-Razzak and Ghan [2000].
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Aerosol fields used in droplet activation for the CRM are predicted on the GCM grid cells as
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described next.
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In the PNNL-MMF, the modal aerosol representation in CAM5 [Liu et al., 2011] is retained
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(Sect. 1.1). However, aerosol and trace gas processing by clouds (i.e., aqueous chemistry,
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convective transport, and wet scavenging) in the standard CAM5 is replaced by the explicit-
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cloud-parameterized-pollutant (ECPP) approach in the PNNL-MMF [Gustafson et al., 2008;
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Wang et al., 2011b]. The ECPP approach uses statistics of cloud distribution, vertical velocity,
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and cloud microphysical properties resolved by the CRM to drive aerosol and chemical
4
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processing by clouds on the GCM grid, which allows the MMF to explicitly treat the effects
93
of convective clouds on aerosols in a computationally feasible manner. The ECPP approach
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predicts both interstitial aerosols and cloud-borne aerosols in all clouds, while the
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conventional CAM5 only treats cloud-borne aerosols in stratiform clouds. In addition, by
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integrating the continuity equation for aerosols and trace gases in convective updraft and
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downdraft regions, the ECPP approach treats convective transport, aqueous chemistry, and
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wet scavenging in an integrated, self-consistent way [Wang et al., 2011b].
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The CAM5 radiative transfer calculation (RRTMG) is applied to each CRM column at each
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GCM time step (10 minutes), assuming 1 or 0 cloud fraction at each CRM grid cell based on
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the presense or absence of condensate. Aerosol optical properties are diagnosed on the CRM
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grid from the dry aerosol on the GCM grid, and the aerosol water on the CRM grid that is
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calculated from Kohler theory based on the relative humidity on the CRM grid, accounting for
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hysteresis and the hygroscopicities of each of the modes’ components [Ghan and Zaveri,
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2007].
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1.3
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The global aerosol-climate model ECHAM5-HAM, version 2 is the latest version of
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ECHAM5-HAM [Stier et al., 2005; Zhang, et al., 2011]. It features a two-moment stratiform
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cloud scheme, introduced by Lohmann et al. [2007], with further improvements documented
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in Lohmann [2008] and Lohmann and Hoose [2009]. It solves the prognostic equations of
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cloud water mass, cloud droplet number concentration, ice water mass, and ice crystal number
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concentration. A diagnostic formula is used for rain and snow. Aerosol activation in warm
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clouds is parameterized by the semi-empirical scheme of Lin and Leaitch [1997]. KK00 is
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used for the autoconversion and accretion parameterization. Homogeneous freezing is
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assumed for cirrus clouds with a temperature below -38C, while heterogeneous freezing is
ECHAM5-HAM2
5
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assumed for the mixed-phase clouds, with a temperature between 0C to -38C [Lohmann and
117
Hoose, 2009].
118
The microphysical aerosol module HAM was first introduced in Stier et al. [2005], with
119
further improvement documented in Zhang et al. [2011]. HAM also uses a modal approach to
120
represent the aerosol size distribution. Seven log-normal modes are used to represent 6 major
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aerosol types: sulfate, black carbon, primary organic carbon, secondary organic carbon, dust,
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and sea salt.
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2
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For CAM5, a finite-volume dynamical core is chosen, with 30 vertical levels at 1.92.5
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horizontal resolution and 30 minutes time step. The model was integrated for 5 years for each
126
simulation, and the results from the last 4 years are used for the analysis.
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For the PNNL-MMF, the host GCM CAM5 uses the same finite-volume dynamical core, and
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the same horizontal and vertical resolution as those in CAM5. The GCM time step is 10
129
minutes. The embedded CRM includes 32 columns at 4-km horizontal grid spacing and 28
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vertical layers coinciding with the lowest 28 CAM levels. The time step for the embedded
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CRM is 20 seconds. The MMF model was integrated for 36 months. Results from the last 34
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months are used in this study.
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For the ECHAM5-HAM2, model simulations have been performed both at T42 horizontal
134
resolution on 19 vertical levels with a time step of 30 minutes and at T63 horizontal resolution
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on 31 vertical levels with a time step of 12 minutes. The model was integrated for 5 years, and
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the results from the last four years are used.
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Climatological sea surface temperature and sea ice are prescribed, and greenhouse gases are
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fixed at the present day level in all model simulations. Both the MMF and CAM5 use the
Set-up of simulations
6
139
same aerosol and precursor emissions as described in Liu et al. [2011]. Anthropogenic SO2,
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BC, and primary organic carbon emissions are from the IPCC AR5 emission data set
141
[Lamarque et al., 2010]. The years 2000 and 1850 are chosen to represent the present day
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(PD) and the pre-industrial (PI) time, respectively.
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For the ECHAM5-HAM2, anthropogenic SO2, BC, and primary organic carbon emissions are
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from the IPCC AR5 emission data set, the same as those used in the MMF and CAM5. The
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emissions of sea salt and dust are computed interactively. Natural emissions of dimethyl
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sulfide (DMS) from the marine biosphere are calculated online according to DMS seawater
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concentrations [Kettle and Andreae, 2000] and the model calculated air-sea exchange rate
148
following Nightingale et al. [2000]. DMS from terrestrial sources are prescribed according to
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Pham et al. [1995].
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A pair of simulations are performed for each model experiment: one with the PD aerosol and
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precursor emissions, and the other with the PI aerosol and precursor emissions.
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3
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In addition to the model experiment with the default CAM5 configuration, 16 CAM5
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sensitivity experiments are performed by varying the autoconversion formulas, as described in
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Table S1. The default KK00 autoconversion scheme used in the CAM5 has the following
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formula: Aqc2.47Nc-1.79, where qc is cloud water content in kg kg-1, and Nc is cloud droplet
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number concentration in cm-3. A is a constant with a unit of cm0.186 sec-1, which is 1350 in
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KK00, but it can vary in CAM5 depending on the subgrid distribution of cloud water content
159
assumed in the model [Morrison and Gettelman, 2008].
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These sensitivity experiments can be grouped into the following categories:
CAM5 sensitivity experiments
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i) Sensitivity to the minimum cloud droplet number concentrations in the autoconversion
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formula (experiments A, B, C, and D). The minimum droplet number
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concentration has been shown to affect aerosol indirect forcing significantly [Ghan
164
et al., 2001; Hoose et al., 2009; Wang and Penner, 2009]. Here we focus on the
165
autoconverion formula, and examine how the minimum droplet number
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concentration affects cloud lifetime effects by varying the minimum droplet
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number concentration from 0 in the default CAM5 to 1, 5, and 20 cm-3. 20 cm-3 is
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the default minimum droplet number concentration used in ECHAM5-HAM2.
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ii) Sensitivity to different autoconversion schemes (experiments L-Q). Here we test two
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Kessler-type autoconversion schemes. One is from Chen and Cotton [Chen and
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Cotton, 1987] (experiments L-O), which is the default autoconversion scheme used
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in NCAR CAM3 [Rasch and Kristjansson, 1998]. The critical cloud droplet radius
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is the volume-weighted mean radius. Several different critical cloud droplet radii
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are tested. A constant collection efficiency of 0.0285 is used, following Baker
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[1993]. The second Kessler-type autoconversion scheme is from Liu and Daum
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[2004], which is similar to the Chen and Cotton scheme but accounts for the
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dispersion effect and uses a collection efficiency that depends on droplet number
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concentration (experiments P and Q). The modified Liu and Daum formula
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suggested by Wood [2005] is used, which predicts autoconversion rates that are
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about a factor of 10 smaller than the original formula and is found to agree better
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with field observations [Wood, 2005]. Two different forms of threshold functions
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H(R, Rcrit) are tested, where R is droplet radius, and Rcrit is the critical radius. One
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is H(R, Rcrit)=(R+0.01*Rcrit)/(Rcrit+R), which is the default in CAM3 and produces
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a very smooth transition when R increases from below Rcrit to above Rcrit. The
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second one is H(R, Rcrit)=1/(1+exp(-40.(R/Rcrit-1))), which produces a much
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sharper transition from 0 to 1 when R increases from below Rcrit to above Rcrit. For
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the Chen and Cotton scheme, the second threshold function is used.
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iii) Sensitivity to the dependence of the autoconversion rate on droplet number
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concentrations (Nc). Using the autoconversion scheme in the form of KK00, we
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vary the power dependence of autoconversion rate from Nc-0.00 to Nc-3.30
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(experiments G-K). In all these sensitivity tests, the autoconversion rate is scaled
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to produce the same rate as that from the default KK00 scheme at a cloud droplet
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number concentration of 100 cm-3. The autoconversion rate in Case K is further
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scaled by 0.1 as the original one produces too low LWP. The autoconversion rate
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in Case H is further scaled by 10.0 to test how the magnitude of the autoconversion
196
rate affects Spop and dlnLWP/dlnCCN. The Chen and Cotton scheme has a
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dependence of Nc-0.33 (L-O), and the Liu and Daum scheme has a dependence of
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Nc-1.00 (P-Q).
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iv) Sensitivity to the magnitude of the autoconversion rate. These sensitivity tests include
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several pairs of simulations that differ only in the magnitude of the autoconversion
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rate or accretion rate by a factor of 5 or 10. In Case E, the autoconversion rate in
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the CAM5 default is scaled by 0.1, while in case F, the accretion rate is further
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scaled by 10.0. Compared with case G, the autoconversion rate in case H is scaled
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by a factor of 10. Compared with case N, the autoconversion rate in case O is
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scaled by a factor of 5.
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4
Surface rain rate vs. radar reflectivity
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For the satellite observations, attenuation-corrected near-surface (about 750 meter above the
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surface) radar reflectivity is used to define a rain event. Two rain categories are used in this
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study. One is the ‘rain certain’ category in which radar reflectivity is in excess of 0 dBZ, and
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the other is the ‘drizzle’ category in which radar reflectivity is in excess of -15 dBZ. Although
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some radar instrument simulators have been applied to global climate models to simulate
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radar reflectivity [Bodas-Salcedo et al., 2011], uncertainties remain regarding how to
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horizontally and vertically distribute precipitating hydrometeors in conventional global
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climate models with fractional cloudiness [Zhang et al., 2010]. Instead, we choose surface
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rain rate to define a rain event in models and used the QuickBeam radar simulation package,
216
which
217
(http://cfmip.metoffice.com/COSP.html) [Bodas-Salcedo et al., 2011], to simulate the radar
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reflectivity at each CRM column in the MMF model [Haynes et al., 2007; Marchand et al.,
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2009] in order to provide guidance for the choice of surface rain rate threshold that
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corresponds to these two ‘rain’ categories. The same hydrometer size distributions as those in
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the MMF model are used in the QuickBeam radar simulator. The attenuation of radar
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reflectivity from hydrometers and water vapor are turned off in the simulator.
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Figure S1 shows the scatter plot of surface rain rate and the radar reflectivity in the first model
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layer (Fig. S1a) and the fifth model layer (about 750 meter above the surface, comparable to
225
the height of the lowest bin of CloudSat observations, Fig. S1b) based on the daily model
226
output at 1:30pm local time for warm and marine clouds over the 34-month MMF simulation
227
period. It shows that radar reflectivity in either the first model layer or the fifth model layer
228
can indicate the strength of surface rain rate, especially at radar reflectivity larger than 0 dBZ.
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Also shown in Figure S1 are the radar reflectivity at 10, 20, 50, 80, and 90 percentiles at each
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surface rain rate bin (red lines). Based on the middle line in Fig. S1b (the median radar
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reflectivity), surface precipitation rates that corresponds to 0 dBZ and -15 dBZ in the fifth
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model layer (about 750 meter above the surface) are 0.60 mm/day and 0.12 mm/day,
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respectively. So these two values are chosen as the thresholds of surface rain rate to define
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‘rain certain’ and ‘drizzle’ category in models. Spop calculated based on two definitions in the
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MMF model with the CRM-scale output are also comparable. For the ‘rain certain’ category,
is
part
of
the
CFMIP
Observation
Simulator
Package
(COSP)
10


236
Spop is 0.35 using the radar reflectivity and 0.38 using surface rain rate. For the ‘drizzle’
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category, Spop is 0.28 using the radar reflectivity and 0.33 using surface rain rate.
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5
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Similar to the construct of cloud susceptibility [Platnick and Twomey, 1994], precipitation
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susceptibility has been employed by Feingold and Siebert [2009] and Sorooshian et al. [2009]
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to examine the dependence of rain rate on cloud droplet number concentration or a proxy of
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cloud droplet number concentration. Sorooshian et al. [2010] further explored this concept by
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breaking up the precipitation susceptibility construct into two components: an aerosol-cloud
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interaction component and a cloud-precipitation component. The rain susceptibility construct
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has also been used in regional modeling studies to examine aerosol effects on precipitation
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rate [Bangert et al., 2011; Jiang et al., 2010; Seifert et al., 2011]. Here we calculate rain
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susceptibility in the MMF model to examine whether this can be a useful tool to evaluate
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cloud lifetime effects.
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The rain susceptibility is defined as
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SN  
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where P is surface precipitation rate and N is cloud droplet number concentration (cloud-top
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droplet number concentration Nctop or column-mean droplet number concentration Nccol) or a
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CCN proxy (AI). Similarly, we can also calculate how P changes with cloud droplet radius, R:
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SR 
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where R can be cloud-top droplet effective radius (retop), cloud-top droplet volume radius
256
(rvtop), or column-mean droplet volume radius (rvcol). SR is called the cloud-precipitation
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component in Sorooshian et al. [2010]. The minus sign is applied in Eq. (1) but not Eq. (2) so
Precipitation susceptibility
d ln( P)
,
d ln( N)
d ln( P)
,
d ln( R)
(1)
(2)
11
258
that a positive value of S (either SN or SR) reflects the conventional wisdom that cloud droplet
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number concentration and precipitation rate are negatively correlated for warm-rain clouds
260
while cloud droplet radius and precipitation rate are positively correlated. The aerosol-cloud-
261
interaction (ACI) component is defined as
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ACIR / N  
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The rain susceptibility can be separated into the cloud-precipitation component and ACI
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component as
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SN  SR  ACIR / N .
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To isolate aerosol effects and cloud microphysics on surface precipitation rate, LWP and
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LTSS are again used to stratify data. Only single-layer warm clouds are chosen, as well as
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clouds with a depth that is within 15% of the mean value within a given LWP bin to further
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reduce the influence of the dynamical variations. Only results that are statistically significant
270
with 95% confidence (1-tailed t-distribution) are reported. Following the study of Sorooshian
271
et al. [2010], only clouds with the LTSS less than 15 K and surface rain rate ranging 2.4-120
272
mm/day are sampled, to facilitate the direct comparison between the MMF results and
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Sorooshian et al. [2010] satellite study.
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Figure S3 shows SN and SR in tropical (20S to 20N) marine clouds simulated by the MMF.
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Sretopis around 0.5, which is near the lower end of the range derived from the satellite
276
observation (Fig. 4 in Sorooshian et al. [2010]). Replacing cloud-top droplet effective radius
 277
with cloud-top droplet volume-mean radius has little effect on simulated SR, reducing it very
278
slightly (compare S rvtopand S retop). SAI is quite small, ranging from 0.08 to 0.24 for LWP less
279
than 700 g m-2, which is slightly smaller than that derived from the satellite data (0.17-0.25
280
for LWP500-700g m-2, Fig. 5 in Sorooshian et al. [2010]). However, at larger LWP bins


d ln( R)
.
d ln( N)
(3)
(4)
12
281
model predicted SR goes slightly negative. In general, AI and surface precipitation rate are
282
weakly correlated for tropical marine clouds in the model (the correlations are smaller than
283
0.1 for all LWP bins, not shown).
284
Replacing cloud-top droplet volume-mean radius with column-mean droplet volume-mean
285
radius leads to negative Srvcol (i.e., negative correlation between surface rain rate and column-
286
mean droplet effective radius), except at the smallest LWP bins. The negative Srvcol is
287
consistent withthe negative S Nctop and SNccol (i.e., surface rain rate and cloud droplet number
288

concentration are positively correlated). These results are surprising, as one might expect a
289

positive correlation
between surface
rain rate and column-mean droplet radius, and a negative
290
correlation between surface rain rate and column-mean droplet number concentration, in
291
warm clouds. This suggests that other factors may play an important role in determining the
292
correlation between surface rain rate and cloud droplet number concentrations and droplet
293
effective radius. For example, dynamical factors, such as updraft velocity, can affect both
294
surface rain rate and cloud droplet number concentration in a way that may distort the
295
relationship between surface rain rate and cloud droplet properties expected from solely
296
microphysical arguments. Although cloud droplet concentration and size exert a major
297
influence on the autoconversion rate, the autoconversion itself plays only a quite minor role in
298
rain production and therefore in regulating the surface rain rate in MMF (Fig. 3b). This
299
suggests that the rain susceptibility is not a good measure of the dependence of
300
autoconversion rate on droplet number concentration.
301
13
302
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