GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-11-3147-2018The importance of considering sub-grid cloud variability when using
satellite observations to evaluate the cloud and precipitation simulations in
climate modelsImportance of sub-grid cloud variability for model evaluationSongHuaZhangZhibozhibo.zhang@umbc.eduhttps://orcid.org/0000-0001-9491-1654MaPo-Lunhttps://orcid.org/0000-0003-3109-5316GhanStevenWangMinghuaihttps://orcid.org/0000-0002-9179-228XJoint Center for Earth Systems Technology, UMBC, Baltimore, MD, USAPhysics Department, UMBC, Baltimore, MD, USAAtmospheric Sciences and Global Change Division, Pacific Northwest National
Laboratory, Richland, WA, USAInstitute for Climate and Global Change Research & School of Atmospheric
Sciences, Nanjing University, Nanjing, ChinaZhibo Zhang (zhibo.zhang@umbc.edu)3August20181183147315819January201812February201818May201829May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://gmd.copernicus.org/articles/11/3147/2018/gmd-11-3147-2018.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/11/3147/2018/gmd-11-3147-2018.pdf
Satellite cloud observations have become an indispensable tool for evaluating
general circulation models (GCMs). To facilitate the satellite and GCM
comparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project)
Observation Simulator Package (COSP) has been developed and is now
increasingly used in GCM evaluations. Real-world clouds and precipitation can
have significant sub-grid variations, which, however, are often ignored or
oversimplified in the COSP simulation. In this study, we use COSP cloud
simulations from the Super-Parameterized Community Atmosphere Model (SPCAM5)
and satellite observations from the Moderate Resolution Imaging
Spectroradiometer (MODIS) and CloudSat to demonstrate the importance of
considering the sub-grid variability of cloud and precipitation when using
the COSP to evaluate GCM simulations. We carry out two sensitivity tests:
SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-grid
cloud and precipitation properties from the embedded
cloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while in
the SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitation
properties (i.e., no sub-grid variations) are given to the COSP. We find that
the warm rain signatures in the SPCAM5 COSP run agree with the MODIS and
CloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSP
run which ignores the sub-grid cloud variations substantially overestimates
the radar reflectivity and probability of precipitation compared to the
satellite observations, as well as the results from the SPCAM5 COSP run. The
significant differences between the two COSP runs demonstrate that it is
important to take into account the sub-grid variations of cloud and
precipitation when using COSP to evaluate the GCM to avoid confusing and
misleading results.
Introduction
Marine boundary layer (MBL) cloud, as a strong modulator of the radiative
energy budget of the Earth–atmosphere system, is a major source of
uncertainty in future climate change projections of the general circulation
models (GCMs) (Cess et al., 1996; Bony and Dufresne, 2005). Improving MBL
cloud simulations in the GCMs is one of the top priorities of the climate
modeling community. As the cloud parameterization schemes in the GCMs become
increasingly sophisticated, there is a strong need for comprehensive global
satellite cloud observations for model evaluation and improvement. However,
the fundamental definitions of clouds in GCMs differ dramatically from those
used for satellite remote sensing, which hampers the use of satellite
products for model evaluation. In order to overcome this obstacle, the Cloud
Feedback Model Intercomparison Project (CFMIP) community has developed an
integrated satellite simulator, the CFMIP Observation Simulator Package
(COSP) (Zhang et al., 2010; Bodas-Salcedo et al., 2011). COSP has greatly
facilitated and promoted the use of satellite data in the climate modeling
community to expose and diagnose issues in GCM cloud simulations (e.g.,
Marchand et al., 2009; Zhang et al., 2010; Kay et al., 2012, 2016; Pincus et
al., 2012; Song et al., 2018).
Warm rain is a unique and important feature of MBL clouds. It plays an
important role in determining the macro- and micro-physical properties of MBL
clouds, in particular, the cloud water budget (e.g., Stevens et al., 2005;
Wood, 2005; Comstock et al., 2005). Many previous studies have investigated
the warm rain simulation in GCMs using the COSP simulators. These studies
reveal a common problem in the latest generation of GCMs; i.e., the drizzle
in MBL clouds is too frequent in the GCM compared with satellite observations
(e.g., Zhang et al., 2010; Franklin et al., 2013; Suzuki et al., 2015;
Takahashi et al., 2017; Jing et al., 2017; Song et al., 2017; Bodas-Salcedo
et al., 2008, 2011; Stephens et al., 2012; Nam and Quaas, 2012; Franklin et
al., 2013; Jing et al., 2017). One possible reason for the excessive warm
rain production in GCMs could be the model's inaccurate representation of
physical processes, such as auto-conversion and accretion, that govern the
precipitation efficiency in warm MBL clouds. Due to the lack of sub-grid
variability of microphysical quantities in most large-scale models, the
auto-conversion parameterization is overly aggressive, so that the models
tend to produce precipitation too quickly (Lebsock et al., 2013; Song et al.,
2017).
The radar observations of warm rain from CloudSat and collocated MODIS
(Moderate Resolution Imaging Spectroradiometer) cloud observations are
extremely useful data for assessing and improving the GCM simulations of MBL
clouds and their precipitation process. However, the dramatic spatial
resolution differences between the conventional GCM (∼100 km) and
satellite observations (∼1 km) become a challenging obstacle for the
satellite and GCM comparisons. To overcome this obstacle, the COSP first
divides the grid-level cloud and precipitation properties (e.g., grid-mean
cloud water and rain water) into the so-called “sub-columns” that are
conceptually similar to “pixel” in satellite observation. Then, for each
sub-column the COSP satellite simulators (e.g., COSP-CloudSat and COSP-MODIS)
simulate the satellite measurements (e.g., radar reflectivity) and retrievals
(e.g., MODIS cloud optical depth and effective radius) which become directly
comparable with satellite data. Ideally, the sub-column generation in COSP
should be consistent with the sub-grid cloud parameterization scheme in the
host GCM. However, in practice sub-grid variations of cloud and precipitation
are often ignored or treated crudely in the COSP simulation for a number of
possible reasons. First of all, the COSP is an independent package, and it
takes substantial efforts to implement in the COSP a sub-grid cloud
generation scheme that is consistent with the host GCM. Secondly, a simple
sub-column generation scheme helps alleviate the computational cost
associated with the COSP simulation. Last but certainly not least, the users
of the COSP might not be fully aware of the consequences of ignoring the
sub-grid cloud and precipitation variability in the COSP simulations.
The current version (v1.4) of COSP provides a built-in highly simplified
sub-column generator. It accounts only for the sub-grid variability of the
types of hydrometeors and ignores the variability of mass and microphysics
within each hydrometeor type. The water content and microphysical properties
(i.e., droplet effective radius and optical thickness) of each hydrometeor
are horizontally homogenous among all the sub-columns that are labeled as the
same type (i.e., stratiform or convective). Here we refer to the current
scheme as the “homogenous hydrometeor scheme”. The uncertainties
and potential biases caused by the homogenous hydrometeor scheme can
be significant and should not be overlooked. A simple hypothetical example is
provided in Fig. 1 to illustrate the importance of accounting for the
sub-grid variability of rainwater in simulating the CloudSat radar
reflectivity. To be consistent with the two-moment cloud microphysics scheme
(Morrison and Gettelman, 2008) that is widely used in the GCMs, we assume the
sub-grid distribution of rainwater to follow the exponential distribution. In
this example, the grid-mean rainwater mixing ratio (q¯) is set to be
0.03 g kg-1 (dashed blue line in Fig. 1a). Using the Quickbeam
simulator (Haynes et al., 2007) in COSP, we simulated the corresponding
94 GHz CloudSat radar reflectivity, which is shown in Fig. 1b. The grid-mean
radar reflectivity based on the exponentially distributed rainwater (i.e.,
with sub-grid variance) is about 4 dBZ (solid red line in Fig. 1b). In
contrast, if the sub-grid variation of rainwater is ignored, the radar
reflectivity corresponding to q¯=0.03 g kg-1 is 13 dBZ
(dashed blue line in Fig. 1b). The substantial difference between the two
indicates that ignoring the sub-grid variability of hydrometeors could cause
significant overestimation of grid-mean radar reflectivity simulation, which
in turn could complicate and even mislead the evaluation of GCMs.
(a) PDF of the rainwater mixing ratio for rainwater when
the horizontal variability of rainwater is assumed to follow the exponential
distribution. The vertical dashed blue line indicates the mean value of the
rainwater mixing ratio as 0.03 g kg-1. (b) The corresponding
PDF of the CloudSat radar reflectivity simulated by COSP assuming the
Marshall and Palmer particle size distribution. The dashed blue line
corresponds to the radar reflectivity based on the mean rainwater
0.03 g kg-1, and the solid red line corresponds to the grid-mean radar
reflectivity based on the PDF of the rainwater mixing ratio.
The objective of this study is to investigate and demonstrate to the GCM
modeling community the importance of considering the sub-grid variability of
cloud and precipitation properties when evaluating the GCM simulations using
COSP. Here we employ the Super-parameterized Community Atmosphere Model
Version 5 (SPCAM5, Wang et al., 2015) to provide the sub-grid cloud and
precipitation hydrometeor fields for a comparison study of the simulated
radar reflectivity and warm rain frequencies by COSP. Fundamentally different
from the convective cloud parameterization schemes in GCMs, SPCAM5 consists
of a two-dimensional cloud-resolving model (CRM) embedded into each grid of a
conventional CAM5 (Khairoutdinov and Randall, 2003; Wang et al., 2015). In
SPCAM5, the sub-grid cloud dynamical and microphysical processes are
explicitly resolved at a 4 km resolution using a two-dimensional version of
the System for Atmospheric Modeling (Khairoutdinov and Randall, 2003) with
the two-moment microphysics scheme (Morrison et al., 2005). We carry out two
sensitivity tests: SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5
COSP run, the sub-grid cloud and precipitation properties from the embedded
CRMs of SPCAM5 are used to drive the COSP simulation. In the
SPCAM5-Homogeneous COSP run, the default homogenous hydrometeor scheme of COSP mentioned above is used to generate the sub-grid cloud and
precipitation fields for the COSP simulation. The outputs from the two runs
are compared with the collocated CloudSat and MODIS observations to assess
the potential problems in both runs, and also to understand the impacts of
omitting sub-grid cloud variations in the COSP simulations.
The rest of the paper is organized as follows: Sect. 2 describes the model,
COSP, and satellite data used in this study. Results are represented in
Sect. 3. Finally, Sect. 4 provides general conclusions and remarks.
Description of model, COSP, and satellite observationsModel
The model used in this study is SPCAM5, an application of the Multiscale
Modeling Framework (MMF) (Randall et al., 2003; Khairoutdinov et al., 2005,
2008; Tao et al., 2009) to CAM5 (Neale et al., 2010), which uses the finite
volume dynamical core at 1.9∘ latitude × 2.5∘
longitude resolution with 30 vertical levels and a 600 s time step. The
embedded 2-D CRM in each CAM5 grid cell includes 32 columns at 4 km
horizontal grid spacing and 28 vertical layers coinciding with the lowest 28
CAM5 levels. The CRM runs with a 20 s time step. Details of the SPCAM5 can
be found in Wang et al. (2011, 2015). The simulations are run in a
“constrained meteorology” configuration (Ma et al., 2013, 2015) to
facilitate model evaluation against observations, in which the model winds
are nudged toward the Modern Era Reanalysis for Research Applications (MERRA)
reanalysis with a relaxation timescale of 6 h (Zhang et al., 2014). The
SPCAM5 simulations are performed from September 2008 to December 2010
(28 months). The last 24 months' (January 2009–December 2010) outputs of the
simulations are used for analysis.
COSP
We used COSP Version 1.4, which has no scientific difference from the latest
version, COSP2 (Swales et al., 2018). Currently, COSP provides simulations of
ISCCP (International Satellite Cloud Climatology Project), CALIPSO
(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation),
CloudSat, MODIS, and MISR (Multi-angle Imaging SpectroRadiometer) cloud
measurements and/or retrievals (Bodas-Salcedo et al., 2011). In this study,
we will focus on the MODIS and CloudSat simulators (Pincus et al., 2012;
Haynes et al., 2007). COSP has three major parts, each controlling a step of
the pseudo-retrieval process: (1) the sub-column generator of COSP
first distributes the grid-mean cloud and precipitation properties from GCMs
into the so-called sub-columns that are conceptually similar to “pixels” in
satellite remote sensing; (2) the satellite simulators simulate the
direct measurements (e.g., CloudSat radar reflectivity and CALIOP
backscatter) and retrieval products (e.g., MODIS cloud optical thickness and
effective radius) for each sub-column using highly simplified radiative
transfer and retrieval schemes; (3) the aggregation scheme averages
the sub-column simulations back to grid level to obtain temporal–spatial
averages that are comparable with aggregated satellite products (e.g., MODIS
level-3 gridded monthly mean products).
At the single-grid 23∘ N and 150∘ E on
4 December 2010 in the CAM5-Base simulation (Song et al., 2017):
(a) the grid-mean total (stratiform plus convective) and convective
cloud fraction. (b) The grid-mean mixing ratios of cloud and
precipitation hydrometeors (LS_CLIQ: large-scale (i.e., stratiform) cloud
water; LS_CICE: large-scale cloud ice; LS_RAIN: large-scale rain; LS_SNOW:
large-scale snow; LS_GRPL: large-scale graupel; CV_CLIQ: convective cloud
water; CV_CICE: convective cloud ice; CV_RAIN: convective rain; CV_SNOW:
convective snow). (c) The distribution of large-scale (red plus
signs for frac_out=1) and convective (blue plus signs for frac_out=2)
cloud among the sub-columns generated by the SCOPS scheme (i.e., frac_out
from scops.f). (d) The distribution of large-scale (red plus signs
for prec_frac=1), convective (blue plus signs for prec_frac=2), and
mixed (green plus signs for prec_frac=3) precipitation among the
sub-columns generated by the SCOPS-PREC scheme (i.e., prec_frac from
prec_scops.f). (e) The mixing ratio (left panels) and effective
radius (right panels) of three precipitation hydrometeor types among the
sub-columns.
As mentioned in the Introduction, COSP-v1.4 has a highly simplified built-in
sub-column generator based on the homogenous hydrometeor scheme. This scheme
accounts only for the sub-grid variability of the types of hydrometeors and
ignores the variability of mass and microphysics within each hydrometeor
type. An example is provided in Fig. 2 to illustrate how this default
sub-column generator of COSP-v1.4 distributes the grid-mean cloud and
precipitation into the sub-columns. We arbitrarily selected a grid
(23∘ N and 150∘ E) with both cloud and significant
precipitation from our previous CAM5 simulation (CAM5-Base simulation in Song
et al., 2017). Figure 2a shows the vertical profiles of the grid-mean total
(stratiform plus convective) and convective cloud fractions at the selected
grid box. Figure 2b shows the vertical profiles of the grid-mean mixing
ratios of each type of hydrometeor. The sub-column generator of COSP takes
the grid-mean cloud fractions, hydrometeor mixing ratios, and effective
particle sizes (Fig. 2a and b) as inputs to generate the sub-columns for the
later satellite measurement and retrieval simulation.
First, sub-columns (150 sub-columns are generated in our example) are
assigned as either cloudy or clear at each model level by the Subgrid Cloud
Overlap Profile Sampler (SCOPS), which was developed originally as part of
the ISCCP simulator (Klein and Jakob, 1999; Webb et al., 2001). Figure 2c
shows the distributions of cloudy sub-columns among the 150 sub-columns at
each vertical level, indicated by variable frac_out produced in the scops.f
routine. The sub-column at a certain vertical level is stratiform cloudy if
frac_out=1, or connective cloudy if frac_out=2 at that vertical level.
As illustrated in Fig. 2c, the SCOPS assigns cloud to the sub-columns in a
manner consistent with the model's grid box average stratiform and convective
cloud amounts (Fig. 2a) and its cloud overlap assumption, i.e.,
maximum-random overlap in this case. The next step is to determine which of
the sub-columns generated by SCOPS contain precipitation hydrometeors, e.g.,
rain and snow. This step is necessary and critical for the COSP CloudSat
radar simulator (Bodas-Salcedo et al., 2011) because radar reflectivity is
highly sensitive to the precipitation hydrometeors due to their large
particle size (L'Ecuyer and Stephens, 2002; Tanelli et al., 2008). The
current sub-grid precipitation distribution scheme, “SCOPS-PREC”, is
developed and described in Zhang et al. (2010). Figure 2d shows the masking
of precipitation among the 150 sub-columns generated by SCOPS-PREC for the
example grid. After the cloud and precipitation are masked, the last step is
to specify the mass (i.e., mixing ratio) and effective radius of hydrometeors
for all the sub-columns occupied by clouds and/or precipitation. The current
scheme for this step is highly simplified. As shown in Fig. 2e, it assumes
the mass and the microphysics of each type of hydrometeor to be horizontally
homogeneous among all the sub-columns that are occupied by this type of
hydrometeor at a given model level. In other words, at each model level the
only difference among sub-columns is that they may be occupied by different
types of hydrometeors (Zhang et al., 2010).
In this study, we have carried out two COSP simulations using the 2-year
SPCAM5 CRM outputs to investigate the importance of considering the sub-grid
variations of cloud and precipitation properties when evaluating the GCM
simulations using COSP. The two COSP simulations are marked as SPCAM5 COSP
and SPCAM5-Homogeneous COSP, respectively. For the SPCAM5 COSP simulation, we
treat the sub-grid cloud and precipitation fields from the CRM of SPCAM5
outputs as sub-columns of COSP without using the COSP sub-column generator.
For the SPCAM5-Homogeneous COSP simulation, we first average the sub-grid
cloud and precipitation fields (including both clear and cloudy sub-grids)
from the CRM of SPCAM5 to each CAM5 grid, and then input these grid-mean
cloud and precipitation fields to the default COSP-v1.4 sub-column simulator
described above to generate the sub-column fields. All the other processes of
two COSP simulations are exactly the same. The COSP simulator outputs are
produced from 6-hourly calculations and the number of sub-columns used here
is 32. To derive the probability of precipitation, we made some simple
in-house modifications in COSP v1.4 to write out the MODIS and CloudSat
simulations for every sub-column. This allows us to derive the joint
statistics of COSP-MODIS and COSP-CloudSat simulations and compare them with
those derived from collocated MODIS and CloudSat level-2 products.
Satellite data
The cloud measurements from the A-Train satellite sensors, namely MODIS and
CloudSat, are used for model-to-observation comparison. The newly released
collection 6 (C6) Aqua-MODIS cloud products (Platnick et al., 2017) are used
to evaluate cloud fraction, cloud optical thickness, and cloud droplet
effective radius. For MBL cloud studies, CloudSat provides valuable
information on the warm rain process that cannot be achieved by a passive
sensor like MODIS. The direct measurement of CloudSat is the vertical profile
of 94 GHz radar reflectivity by cloud and hydrometer particles (i.e.,
2B-GEOPROF product), from which other information such as vertical
distribution of clouds and precipitation can be derived. The CloudSat
2B-GEOPROF product (Marchand et al., 2008) is used for cloud vertical
structure, radar reflectivity, and identification of precipitation in MBL
clouds. To prepare for the comparison of joint statistics, we collocated 5
years (2006–2010) of pixel-level (i.e., level-2) MODIS and CloudSat
observations using the collocation scheme developed in Cho et al. (2008). Due
to the low sampling rate of CloudSat, we used 5 years (2006–2010) of
observations, in comparison with the 2-year model simulation (2009–2010), to
obtain enough statistics. A sensitivity study indicates that the inter-annual
variability of MBL clouds is much smaller than the model-to-observation
differences.
In this study, we focus on the tropical and subtropical regions between
45∘ S and 45∘ N (loosely referred to as “tropical and
subtropical region”), where most stratocumulus and cumulus regimes are
found. We avoid high latitudes because satellite observations, namely MODIS,
may have large uncertainties at low solar zenith angles there (Kato and
Marshak, 2009; Grosvenor and Wood, 2014; Cho et al., 2015).
Sensitivity study: SPCAM5 COSP vs. SPCAM5-Homogeneous COSP
Tropical-averaged radar reflectivity–height histogram in the
CloudSat observation (a), the SPCAM5 CloudSat simulation
(b), and the
SPCAM5-Homogeneous CloudSat simulation (c).
First, we compare the Contoured Frequency by Altitude Diagram (CFAD) of
tropical clouds derived based on SPCAM5 COSP and SPCAM5-Homogeneous COSP
simulations with that derived from the CloudSat 2B-GEOPROF product in Fig. 3.
The CFAD-based CloudSat observations display a typical boomerang-type shape
that has been reported in many previous studies (Bodas-Salcedo et al., 2011;
Zhang et al., 2010; Marchand et al., 2009). Focusing on the low clouds below
3 km, we observe a rather broad distribution of radar reflectivity with a
maximum occurrence frequency around -30 to -20 dBZ followed by a long
tail extending to about 10 dBZ. As pointed out in previous studies, the peak
around -30 to -20 dBZ is due to non-precipitating MBL clouds and the
precipitating clouds with increasing rain rate give rise to the long tail.
The CFAD based on two COSP simulations exhibits some characteristics similar
to the CloudSat observations, but also many noticeable differences. In
particular, the two COSP simulations both produce a much narrower range of
radar reflectivity for low clouds, with occurrence frequency clustered mostly
around -25 dBZ in SPCAM5 COSP and around 0 dBZ in SPCAM5-Homogeneous
COSP. These results show that using the oversimplified COSP sub-column
generator (e.g., the homogeneous hydrometeor scheme) has non-negligible
influences on the simulated radar reflectivity and produces artificially high
occurrences of large radar reflectivity. Consistent with many previous
studies (e.g., Bodas-Salcedo et al., 2008; Stephens et al., 2012; Nam and
Quaas, 2012; Franklin et al., 2013; Jing et al., 2017), our results also
reveal that GCMs tend to produce much larger radar reflectivity more
frequently through the COSP simulator compared to the satellite observation.
The systematic biases in simulated radar reflectivity by the COSP homogeneous
hydrometeor scheme might lead to the unjustified and biased evaluation of the
warm rain production in GCMs, since cloud column maximum radar reflectivity
(Zmax) is often used to distinguish precipitating from
non-precipitating MBL clouds (Kubar and Hartmann, 2009; Lebsock and Su, 2014;
Haynes et al., 2009).
Next we compare the simulated and observed probability density functions (PDFs) of
Zmax for all the sub-columns that are marked as warm liquid clouds in
the domain between 45∘ S and 45∘ N. The warm liquid clouds
are defined by the cloud phase and cloud top pressure derived from the MODIS
simulator by the criteria that cloud phase is liquid and cloud top pressure
is between 900 and 500 hPa. Big differences in the PDFs of Zmax
between the SPCAM5-Homogeneous COSP and the A-Train observations, and between
SPCAM5-Homogeneous COSP and SPCAM5 COSP, are shown in Fig. 4. First, in the
A-Train observations, about 46 % of warm liquid clouds detected by MODIS
are not observed by CloudSat. These clouds are either too thin and therefore
their radar reflectivity is too weak to be detected by CloudSat, or they are
too low and therefore suffer from the surface clutter issue (Marchand et al.,
2008). For those warm liquid clouds detected by both MODIS and CloudSat, the
PDF of Zmax peaks around -25 dBZ. Second, in both COSP simulations,
almost all warm liquid clouds derived by the MODIS simulator have a valid
CloudSat radar reflectivity larger than -40 dBZ. The PDFs of Zmax in
SPCAM5 reasonably resemble those in the A-Train observations. However,
significantly different from the other two, the distribution of Zmax in
SPCAM5-Homogeneous shifts to the large dBZ values and peaks around 0 dBZ. In
previous studies (e.g., Takahashi et al., 2017), warm liquid clouds are
categorized into three different modes by Zmax: non-precipitating mode
(Zmax<-15 dBZ), drizzle mode (-15 dBZ <Zmax<0 dBZ),
and rain mode (Zmax>0 dBZ). The simulated and observed PDFs of
Zmax demonstrate that a large portion of warm liquid clouds is
non-precipitating in the observations and SPCAM5 COSP, while most warm liquid
clouds are precipitating (drizzle or rain) clouds in the SPCAM5-Homogeneous
COSP. The use of the COSP homogeneous hydrometeor scheme gives us a
dramatically different assessment of the warm rain production of MBL clouds
in the SPCAM5 model; i.e., if we consider the sub-column variability of cloud
and precipitation in the COSP simulation, we find that the SPCAM5 model can
reproduce the observed warm rain production quite well. However, if we ignore
the CRM sub-grid variability and use the homogeneous hydrometeor scheme, we
may make the biased conclusion that the SPCAM5 model performs badly in the
simulation of warm rain production.
The histograms of column maximum radar reflectivity for liquid
clouds over oceanic regions from 45∘ S to 45∘ N in A-Train
satellite observations, SPCAM5 COSP, and SPCAM5-Homogeneous COSP
simulations.
More significant differences between the SPCAM5 COSP and SPCAM5-Homogeneous
COSP simulations can be found from the spatial distributions of the
probability of precipitation (POP) in MBL warm clouds (Fig. 5). Here, the POP
for a given grid box is defined as the fraction of liquid-phase cloud
identified by MODIS observations with Zmax larger than a certain
threshold (i.e., -15 dBZ for drizzle or rain, 0 dBZ for rain, and 10 dBZ
for heavy rain, respectively) according to the collocated CloudSat
observations with respect to the total population liquid-phase clouds with
the cloud top pressure between 500 and 900 hPa in the grid. Observations in
Fig. 5 suggest that roughly a third of MBL clouds observed by MODIS in the
tropical and subtropical region are likely precipitating (drizzle or rain),
with a domain-averaged POP around 33 %. The POP of drizzle plus rain has a
distinct pattern: smaller (∼15 %) in the coastal Sc regions and
increasing to ∼50 % in the Cu cloud regions. The observed POPs of rain
and heavy rain show similar spatial patterns to those of drizzle plus rain,
with much smaller domain-averaged POP being about 12.5 % and 3.3 %,
respectively.
Probability of precipitation (POP) of liquid clouds between 500 and
900 hPa levels in the satellite observations (a, d, g), the SPCAM5
COSP simulation (b, e, h), and the SPCAM5-Homogeneous COSP
simulation (c, f, i). Three categories of precipitation: drizzle
plus rain (column Zmax>-15 dBZ, a, b, c), rain (column Zmax>0 dBZ, d, e, f), and strong rain only (column Zmax>10 dBZ, g, h, i).
Unit of POP is %.
In the same way as we define POP for observations, we define the POP for two
COSP simulations as the ratio of sub-columns that have COSP-CloudSat
simulated Zmax larger than a certain threshold with respect to the
total number of liquid-phase clouds identified by COSP-MODIS. As shown in
Fig. 5, two COSP simulations show dramatically different spatial
distributions of POPs. The SPCAM5 COSP produces the similar POP patterns to
those in the observations, with the domain-averaged POPs for drizzle or rain,
rain, and heavy rain being about 43 %, 16 %, and 4.5 %, respectively.
However, the POPs in the SPCAM5-Homogeneous COSP are substantially
overestimated, with the domain-averaged POPs for drizzle or rain, rain, and
heavy rain being about 75 %, 36 %, and 7 %, respectively. Using the
COSP homogeneous hydrometeor scheme will lead to the conclusion that the
drizzle or rain is triggered too frequently (more than double the
observations) in the SPCAM5 model, which obviously is not a fair assessment.
POP (drizzle or rain) of liquid clouds at each LWP and liquid cloud
effective radius in the satellite observations (a), the SPCAM5 COSP
simulation (b), and the SPCAM5-Homogeneous COSP simulation
(c). The white solid contours are joint PDF of LWP and liquid cloud
effective radius. Units of POP and PDF are %.
Previous studies find that the warm rain production in MBL clouds is tightly
related to the in-cloud microphysical properties of MBL clouds (e.g., Stevens
et al., 2005; Wood, 2005; Comstock et al., 2005). Next, we check the
dependence of POP on in-cloud properties' liquid water path (LWP) and on
liquid cloud effective radius (re) in both observations and two
COSP simulations. Figure 6 shows the POPs of drizzle or rain (i.e., Zmax>-15 dBZ) as a function of in-cloud LWP and re overlaid by the joint
PDF of LWP and re (white contours) in the satellite observations
and two COSP simulations. The observed POPs of warm liquid clouds increase
monotonically with increasing in-cloud LWP and re, with high POPs
concentrating on the domain with large values of LWP and re
(i.e., LWP >200 g m-2 and re>15µm).
However, in the two COSP simulations, especially the SPCAM5-Homogeneous COSP,
at each joint bin the POPs are much larger than those in the A-Train
observations. When in-cloud LWP (re) is larger than
150 g m-2 (17 µm), the dependence of POPs on in-cloud
re (LWP) is small. The joint PDFs of in-cloud LWP and
re in the observations and two COSP simulations are also quite
different. There are more occurrences with large LWP and re in
the MODIS observations than the two COSP simulations. The SPCAM5 COSP
simulations have two peaks of the joint PDFs, which are converted to one
occurrence peak in the SPCAM5-Homogeneous COSP simulation by using the COSP
homogeneous hydrometeor scheme.
Based on the above comparisons, we can see that the oversimplified COSP
sub-column generator contributes to not only the narrow distribution of MBL
cloud radar reflectivity, but also to unrealistically high POPs in the
SPCAM5 model. Besides, it also changes the distribution of in-cloud
microphysical properties, and the relationship between POPs and cloud
microphysical properties as well.
Summary and discussion
This study presents a satellite-based evaluation of the warm rain production
of MBL cloud in the SPCAM5 model using two COSP simulations (SPCAM5 COSP and
SPCAM5-Homogeneous COSP), with the objective of demonstrating the importance
of considering the sub-grid variability of cloud and precipitation when using
COSP to evaluate GCM simulations. Through the SPCAM5 COSP simulations, in
which the sub-column variability of cloud and precipitation is considered, we
find that the SPCAM5 model can reproduce the observed warm rain production
quite well. However, in the SPCAM5-Homogeneous COSP simulation, in which we
ignore the CRM sub-grid variability and use the COSP homogeneous hydrometeor
scheme, the simulated radar reflectivity and POPs in the SPCAM5 are
significantly overestimated compared to the observations. Therefore, use of
the COSP homogeneous hydrometeor scheme gives us a significantly different
assessment of warm rain production of MBL clouds in the SPCAM5 model. Our
results also indicate that the sub-grid variability of mass and microphysics
of each hydrometeor type is key to the realistic simulation of radar
reflectivity.
The systematic and significant biases due to the limitation of the current
homogeneous hydrometeor scheme can mislead the evaluation of GCMs and should
not be overlooked. In this regard, an improved sub-column generator needs to
be developed for COSP to account for the sub-grid variances of cloud and/or
hydrometer mass and microphysics. A recent study of Hillman et al. (2018)
investigated the sensitivities of simulated satellite retrievals to
subgrid-scale overlap and condensate heterogeneity, and demonstrated the
systematic biases in the simulated MODIS cloud fraction and CloudSat radar
reflectivity due to the oversimplified COSP sub-column generator. Their study
also proposed a new scheme to replace the COSP current sub-column generator,
and showed that the new scheme can produce much better satellite retrievals.
Implementing their sub-column heterogeneous hydrometeor scheme in COSP may
improve the GCM COSP simulations and give a better-justified assessment of
the GCM performance in simulating warm rain processes and cloud microphysical
properties.
On the other hand, since the assumptions of sub-grid variability of cloud and
hydrometeors in different GCMs may be quite different, one universal
sub-column hydrometeor scheme may be not applicable to all models. Based on
this consideration, the latest version, COSP version 2, enhances flexibility
by allowing for model-specific representation of sub-grid-scale cloudiness
and hydrometeor condensates and encourages the users to implement the same
sub-grid scheme as the host GCM for consistency (Swales et al., 2018).
Nevertheless, our study also suggests that any evaluation study of warm rain
production in GCMs by using COSP simulators should take this issue into
account.
Details of SPCAM5 can be found in Wang et al. (2011,
2015). The host GCM in SPCAM5 is the Community Atmospheric Model, version 5
(see details on the CESM website at
http://www.cesm.ucar.edu/models/cesm1.1/cam/, last access: 19 July
2018). SPCAM5 has recently been merged with CESM1.1.1 and released to the
public (Randall et al., 2013;
https://svn-ccsm-release.cgd.ucar.edu/model_development_releases/spcam2_0-cesm1_1_1,
last access: 19 July 2018, registration required). Codes of COSP V1.4 can be
found on the website at https://github.com/CFMIP/COSPv1 (last access:
19 July 2018). We used the collection 6 (C6) Aqua-MODIS cloud products
(Platnick et al., 2017), which can be downloaded from the NASA website at
https://lance3.modaps.eosdis.nasa.gov/data_products/ (last access: 19
July 2018). The CloudSat data are distributed by the CloudSat Data Processing
Center. The CloudSat 2B-GEOPROF product we used is downloaded from the
website at
http://www.cloudsat.cira.colostate.edu/data-products/level-2b/2b-geoprof?term=42
(last access: 19 July 2018).
MW provided the source code of SPCAM5 and wrote the Fortran code to run COSP
simulation with the sub-grid cloud and precipitation properties from the
embedded CRMs of SPCAM5. PLM and SG provided the results of CAM5 simulations
and helped us to find the excessive drizzle production problem in CAM5, which
is partially due to the COSP's over-simplified sub-column generator. HS and
ZZ carried out the SPCAM5 simulation, drafted the text and made the figures.
All authors contributed to the editing of the
manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research is supported by the U.S. Department of Energy (DOE), Office of
Science, Biological and Environmental Research, Regional and Global Climate
Mode Analysis Program (grant no. DE-SC0014641). The Pacific Northwest National Laboratory is operated for the DOE
by Battelle Memorial Institute under contract DE-AC05-76RLO 1830. Minghuai
Wang was supported by the Minister of Science and Technology of China
(2017YFA0604001). The computations in this study were performed at the UMBC
High Performance Computing Facility (HPCF). The facility is supported by the
U.S. National Science Foundation through the MRI program (grant nos.
CNS-0821258 and CNS-1228778) and the SCREMS program (grant no. DMS-0821311),
with substantial support from UMBC. The MODIS cloud products used in this
study are downloaded from the NASA Level-1 and Atmosphere Archive and
Distribution System from https://ladsweb.modaps.eosdis.nasa.gov/ (last
access: 19 July 2018). The CloudSat products are provided by the CloudSat
Data Processing Center from http://www.cloudsat.cira.colostate.edu/
(last access: 19 July 2018).Edited by: Klaus Gierens
Reviewed by: two anonymous referees
ReferencesBodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., Williams, K.
D., Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met
Office global forecast model using simulated CloudSat radar
reflectivities, J. Geophys. Res., 113, D00A13, 10.1029/2007JD009620,
2008.Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: Satellite simulation software
for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043,
10.1175/2011BAMS2856.1, 2011.Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the
heart of tropical cloud feedback uncertainties in climate models, Geophys.
Res. Lett., 32, L20806, 10.1029/2005GL023851, 2005.Cess, R., Zhang, M. H., Ingram, W. J., Potter, G. L., Alekseev, V., Barker, H. W., Cohen-Solal, E., Colman, R. A., Dazlich, D. A., Del Genio, A. D., Dix, M. R., Dymnikov, V., Esch, M., Fowler, L. D., Fraser, J. R., Galin, V., Gates, W. L., Hack, J. J., Kiehl, J. T., Le Treut, H., Lo, K. K.-W., McAvaney, B. J., Meleshko, V. P., Morcrette, J.-J., Randall, D. A., Roeckner, E., Royer, J.-F., Schlesinger, M. E., Sporyshev, P. V., Timbal, B., Volodin, E. M., Taylor, K. E., Wang, W., and Wetherald, R. T.: Cloud feedback in atmospheric general
circulation models: An update, J. Geophys. Res.-Atmos., 101, 12791–12794,
1996.Cho, H. M., Yang, P., Kattawar, G. W., Nasiri, S. L., Hu, Y., Minnis, P.,
Trepte, C., and Winker, D.: Depolarization ratio and attenuated backscatter
for nine cloud types: Analyses based on collocated CALIPSO lidar and MODIS
measurements, Opt. Express, 16, 3931–3948, 2008.Cho, H. M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., Di Girolano, L., C.-Labonnote, L., Cornet, C., Riedi, J., and Holz, R. E.: Frequency and causes of failed MODIS cloud
property retrievals for liquid phase clouds over global oceans, J. Geophys.
Res.-Atmos., 120, 2015JD023161, 10.1002/2015JD023161, 2015.Comstock, K. K., Bretherton, C. S., and Yuter, S. E.: Mesoscale
variability and drizzle in southeast Pacific stratocumulus, J. Atmos. Sci.,
62, 3792–3807, 10.1175/JAS3567.1, 2005.Franklin, C. N., Sun, Z., Bi, D., Dix, M., Yan, H., and Bodas-Salcedo, A.:
Evaluation of clouds in access using the satellite simulator package COSP:
regime-sorted tropical cloud properties, J. Geophys. Res.-Atmos., 118,
6663–6679, 10.1002/jgrd.50496, 2013.Grosvenor, D. P. and Wood, R.: The effect of solar zenith angle on MODIS
cloud optical and microphysical retrievals within marine liquid water clouds,
Atmos. Chem. Phys., 14, 7291–7321, 10.5194/acp-14-7291-2014,
2014.Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and
Stephens, G. L.: A multi-purpose radar simulation package: QuickBeam, B. Am.
Meteorol. Soc., 88, 1723–1727, 2007.Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C.,
Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with
spaceborne W-band radar, J. Geophys. Res.-Atmos., 114, D00A22,
10.1029/2008JD009973, 2009.Hillman, B. R., Marchand, R. T., and Ackerman, T. P.: Sensitivities of
simulated satellite views of clouds to subgrid-scale overlap and condensate
heterogeneity, J. Geophys. Res.-Atmos.,10.1029/2017jd027680,
accepted, 2018.Jing, X., Suzuki, K., Guo, H., Goto, D., Ogura, T., Koshiro, T., and
Mülmenstädt, J.: A multimodel study on warm precipitation biases in
global models compared to satellite observations, J. Geophys. Res.-Atmos.,
122, 11806–11824, 10.1002/2017JD027310, 2017.Kato, S. and Marshak, A.: Solar zenith and viewing geometry dependent
errors in satellite retrieved cloud optical thickness: Marine Sccase, J.
Geophys. Res.-Atmos., 114, D01202, 10.1029/2008JD010579, 2009.Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, T. P.: Exposing global cloud biases in the
community atmosphere model (CAM) using satellite observations and their
corresponding instrument simulators, J. Climate, 25, 5190–5207,
10.1175/JCLI-D-11-00469.1, 2012.Kay, J. E., L'Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A.,
and Cesana, G.: Recent Advances in Arctic Cloud and Climate Research,
Current Climate Change Reports, 2, 159–169, 2016.Khairoutdinov, M. F. and Randall, D. A.: Cloud resolving modeling of
the ARM summer 1997 IOP: Model formulation, results, uncertainties, and
sensitivities, J. Atmos. Sci., 60, 607–625, 2003.Khairoutdinov, M., Randall, D., and DeMott, C.: Simulations of the
atmospheric general circulation using a cloud-resolving model as a
superparameterization of physical processes, J. Atmos. Sci., 62, 2136–2154,
2005.Klein, S. A. and Jakob, C.: Validation and sensitivities of frontal
clouds simulated by the ECWMF model, Mon. Weather Rev., 127, 2514–2531,
10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2, 1999.Kubar, T. L. and Hartmann, D. L.: Understanding the importance of
microphysics and macrophysics for warm rain in marine low clouds. Part I:
Satellite observations, J. Atmos. Sci., 66, 2953–2972,
10.1175/2009JAS3071.1, 2009.Lebsock, M. and Su, H.: Application of active spaceborne remote
sensing for understanding biases between passive cloud water path retrievals,
J. Geophys. Res.-Atmos., 119, 8962–8979, 10.1002/2014JD021568, 2014.Lebsock, M., Morrison, H., and Gettelman, A.: Microphysical implications
of cloud-precipitation covariance derived from satellite remote sensing, J.
Geophys. Res.-Atmos., 118, 6521–6533, 10.1002/jgrd.50347,
2013.L'Ecuyer, T. S. and Stephens, G. L.: An estimation-based
precipitation retrieval algorithm for attenuating radars, J. Appl.
Meteorol., 41, 272–285, 2002.Ma, P.-L., Rasch, P. J., Wang, H., Zhang, K., and Easter, R. C.: The role of circulation features on black
carbon transport into the Arctic in the Community Atmosphere Model version 5
(CAM5), J. Geophys. Res.-Atmos., 118, 4657–4669, 10.1002/jgrd.50411,
2013.Ma, P.-L., Rasch, P. J., Wang, M., Wang, H., Ghan, S. J., Easter, R. C., Gustafson Jr., W. I., Liu, X., Zhang, Y., and Ma, H.-Y.: How does increasing horizontal resolution in
a global climate model improve the simulation of aerosol-cloud interactions?,
Geophys. Res. Lett., 42, 5058–5065, 10.1002/2015GL064183, 2015.Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
detection using Cloudsat – An earth-orbiting 94-GHz cloud radar, J. Atmos.
Ocean. Technol., 25, 519–533, 10.1175/2007JTECHA1006.1, 2008.Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A
comparison of simulated cloud radar output from the multiscale modeling
framework global climate model with CloudSat cloud radar observations, J.
Geophys. Res.-Atmos., 114, D00A20, 10.1029/2008JD009790, 2009.Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud
microphysics scheme in the community atmosphere model, version 3 (CAM3). Part
I: Description and numerical tests, J. Climate, 21, 3642–3659, 2008.Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A new
double-moment microphysics parameterization for application in cloud and
climate models. Part I: Description, J. Atmos. Sci., 62, 1665–1677, 2005.Nam, C. and Quaas, J.: Evaluation of clouds and precipitation in the
ECHAM5 general circulation model using CALIPSO and CloudSat satellite data,
J. Climate., 25, 4975–4992, 10.1175/JCLI-D-11-00347.1, 2012.Neale, R. B., Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L., Kiehl, J. T., and Briegleb, B.: Description of the NCAR community
atmosphere model (CAM 5.0), Tech. Rep. TN–486+STR, 268 pp., Natl.
Cent. for Atmos. Res., Boulder, Colo., 2010.Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and
Hofmann, P.: Reconciling simulated and observed views of clouds: MODIS,
ISCCP, and the limits of instrument simulators, J. Climate, 25,
120220120058001, 10.1175/JCLI-D-11-00267.1, 2012.Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS cloud optical and microphysical
products: Collection 6 updates and examples from Terra and Aqua, IEEE T.
Geosci. Remote, 55, 502–525, 10.1109/TGRS.2016.2610522, 2017.Randall, D., Khairoutdinov, M., Arakawa, A., and Grabowski, W.: Breaking
the cloud parameterization deadlock, B. Am. Meteorol. Soc., 84, 1547–1564,
2003.Randall, D., Branson, M., Wang, M., Ghan, S., Craig, C., Gettelman, A., and
Edwards, J.: A community atmosphere model with superparameterized
clouds, Eos Trans. AGU, 94, 221–222, 2013.Song, H., Zhang, Z., Ma, P.-L., Ghan, S., and Wang, M.: An Evaluation of
Marine Boundary Layer Cloud Property Simulations in Community Atmosphere
Model Using Satellite Observations: Conventional Sub-grid Parameterization
vs. CLUBB, J. Climate, 31, 2299–2320, 2018.Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Bendetti, A., Mitrescu, C., and the CloudSat Science Team: The CloudSat Mission and the A-Train, B. Am. Meteorol. Soc.,
83, 1771–1790, 10.1175/BAMS-83-12-1771, 2012.Stevens, B., Vali, G., Comstock, K., Woods, R., Van Zanten, M. C.,
Austin, P. H., Bretherton, C. S., and Lenschow, D. H.: Pockets of open cells
and drizzle in marine stratocumulus, B. Am. Meteorol. Soc., 86, 51–57,
2005.Suzuki, K., Stephens, G., Bodas-Salcedo, A., Wang, M., Golaz, J.-C.,
Yokohata, T., and Koshiro, T.: Evaluation of the warm rain formation process
in global models with satellite observations, J. Atmos. Sci., 72,
3996–4014, 10.1175/JAS-D-14-0265.1, 2015.Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model
Intercomparison Project Observational Simulator Package: Version 2, Geosci.
Model Dev., 11, 77–81, 10.5194/gmd-11-77-2018, 2018.Takahashi, H., Lebsock, M., Suzuki, K., Stephens, G., and Wang, M.: An
investigation of microphysics and subgrid-scale variability in warm-rain
clouds using the A-Train observations and a multiscale modeling framework,
J. Geophys. Res.-Atmos., 122, 7493–7504, 2017.Tanelli, S., Durden, S. L., Im, E., Pak, K. S., Reinke, D. G., Partain, P.,
Haynes, J. M., and Marchand, R. T.: CloudSat's Cloud Profiling Radar After
Two Years in Orbit: Performance, Calibration, and Processing, IEEE T.
Geosci. Remote, 46, 3560–3573, 2008.Tao, W. K., Chern, J., Atlas, R., Randall, D., Lin, X., Khairoutdinov, M., Li, J.-L., Waliser, D. E., Hou, A., Peters-Lidard, C., Lau, W., and Simpson, J.: A multiscale modeling system developments,
applications, and critical issues, B. Am. Meteorol. Soc., 90, 515–534,
2009.Wang, M., Ghan, S., Easter, R., Ovchinnikov, M., Liu, X., Kassianov, E.,
Qian, Y., Gustafson Jr., W. I., Larson, V. E., Schanen, D. P., Khairoutdinov,
M., and Morrison, H.: The multi-scale aerosol-climate model PNNL-MMF: model
description and evaluation, Geosci. Model Dev., 4, 137–168,
10.5194/gmd-4-137-2011, 2011.Wang, M., Larson, V.,
Ghan, S., Ovchinnikov, M., Schanen, D., Xiao, H., Liu, X., Guo, Z., and
Rasch, P.: A multiscale modeling framework model (superparameterized CAM5)
with a higher-order turbulence closure: Model description and low-cloud
simulations, J. Adv. Model. Earth Syst., 7, 484–509,
10.1002/2014MS000375, 2015.Webb, M., Senior, C., Bony, S., and Morcrette, J.: Combining ERBE and
ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric
climate models, Clim. Dynam., 17, 905–922, 2001.Wood, R.: Drizzle in stratiform boundary layer clouds. Part I:
Vertical and horizontal structure, J. Atmos. Sci., 62, 3011–3033, 2005.
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch,
P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging
for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14,
8631–8645, 10.5194/acp-14-8631-2014, 2014.Zhang, Y., Klein, S. A., Boyle, J., and Mace, G. G.: Evaluation of
tropical cloud and precipitation statistics of Community Atmosphere Model
version 3 using CloudSat and CALIPSO data, J. Geophys. Res.-Atmos., 115,
D12205, 10.1029/2009JD012006, 2010.