Studies have shown that changes in cloud cover are responsible
for the rapid climate warming over the Tibetan Plateau (TP) in the past 3
decades. To simulate the total cloud cover, atmospheric models have to
reasonably represent the characteristics of vertical overlap between cloud
layers. Until now, however, this subject has received little attention due to
the limited availability of observations, especially over the TP. Based on
the above information, the main aim of this study is to examine the
properties of cloud overlaps over the TP region and to build an empirical
relationship between cloud overlap properties and large-scale atmospheric
dynamics using 4 years (2007–2010) of data from the CloudSat cloud product
and collocated ERA-Interim reanalysis data. To do this, the cloud overlap
parameter α, which is an inverse exponential function of the cloud
layer separation D and decorrelation length scale L, is calculated using
CloudSat and is discussed. The parameters α and L are both widely
used to characterize the transition from the maximum to random overlap
assumption with increasing layer separations. For those non-adjacent layers
without clear sky between them (that is, contiguous cloud layers), it is
found that the overlap parameter α is sensitive to the unique
thermodynamic and dynamic environment over the TP, i.e., the unstable
atmospheric stratification and corresponding weak wind shear, which leads to
maximum overlap (that is, greater α values). This finding agrees well
with the previous studies. Finally, we parameterize the decorrelation length
scale L as a function of the wind shear and atmospheric stability based on
a multiple linear regression. Compared with previous parameterizations, this
new scheme can improve the simulation of total cloud cover over the TP when
the separations between cloud layers are greater than 1 km. This study thus
suggests that the effects of both wind shear and atmospheric stability on
cloud overlap should be taken into account in the parameterization of
decorrelation length scale L in order to further improve the calculation of
the radiative budget and the prediction of climate change over the TP in the
atmospheric models.
Introduction
The Tibetan Plateau (TP), which is also known as the “roof of the world” or
the “world water tower”, plays a significant role in determining global
atmospheric circulations, in addition to its strong influence on climate over
Asia via its thermodynamic and dynamic forcings (Yanai et al., 1992; Ye and
Wu, 1998; Duan and Wu, 2005; Xu et al., 2008; Wu et al., 2015). Studies have
shown that the TP has experienced significant climate warming over the past
three decades (e.g., Yang et al., 2014; Kang et al., 2010), and it will
continue in the future (e.g., Duan and Wu, 2006; Wang et al., 2008). The
rapid warming has caused glacier retreat and the expansion of glacier-fed
lakes (Zhu et al., 2010), permafrost degradation (Cheng and Wu, 2007), and
the weakening of surface heating and atmospheric heating (Yang et al., 2011).
Based on satellite and surface observations, many studies have linked the
rapid warming over the TP to changes in cloud cover over this region (e.g.,
Chen and Liu, 2005; Duan and Wu, 2006; Li et al., 2006; Yang et al., 2012;
You et al., 2014). For example, a recent study has indicated that the
increased nocturnal cloud cover over the northern TP could increase the
nighttime temperature by enhancing downward surface infrared radiation, while
the decreased daytime cloud cover over the southern TP has contributed to the
increase in daytime surface air temperature by enhancing downward surface
solar radiation (Duan and Xiao, 2015). It means that a reliable simulation of
cloud cover in the climate models is required for the prediction of climate
change over the TP.
However, our incomplete understanding of the cloud physical processes and the
limited cloud observations over the TP mean the simulation of total cloud
cover in the climate models is still unreliable. One of the remaining challenges
involves how to reasonably represent the characteristics of the vertical
overlapping of cloud layers in these models. Cloud overlap means that two or
more cloud layers are simultaneously present over the same location but at
different levels in the atmosphere. To derive the total cloud cover between
cloud layers, models have to make some assumption about how the cloud layers overlap in the vertical direction, such as, maximum, random, and minimum
assumptions. If the cloud covers of two model layers are given by Ci and
Cj, respectively, total cloud cover between these two layers from a maximum assumption is Ci,jmax=maxCi,Cj,
while the random and minimum assumptions define the total cloud cover as
Ci,jran=Ci+Cj-Ci×Cj and Ci,jmin=minCi+Cj,1, respectively. Thus, the maximum
assumption minimizes the total cloud cover, while minimum assumption produces
minimal overlap between cloud layers and results in maximum total cloud
cover (Weger et al., 1992). The total cloud cover predicted by the random
assumption will fall somewhere between the maximum and minimum assumptions (Geleyn
and Hollingsworth, 1979). Studies have shown that these different overlap
assumptions result in obviously different total cloud covers and will
significantly affect the calculated radiative budgets and heating/cooling
rate profiles (Morcrette and Fouquart, 1986; Barker et al., 1999; Barker and
Fu, 2000; Chen et al., 2000; Pincus et al., 2005; Zhang and Jing, 2010, 2016;
Zhang et al., 2013; Jing et al., 2016).
To improve the simulation of total cloud cover, Hogan and Illingworth (2000)
revisited the cloud overlap assumptions and proposed a simpler and more
useful expression for the degree of cloud layer overlap (exponential random
overlap assumption) using ground-based radar measurements. In their
expression, the observed cloud cover between two cloud layers can be
expressed as the linear combination of the maximum and random overlap by
using a weighting factor, termed as cloud overlap parameter, α:
α=Ci,jobs-Ci,jranCi,jmax-Ci,jran.
The overlap parameter α ranges from 0 (random) to 1 (maximum) when
the observed total cloud cover falls between the values using the maximum and
random overlap assumptions. The α will be negative if the degree of
cloud overlap is lower than that predicted by the random overlap assumption.
Finally, Hogan and Illingworth (2000) fitted the reduction in α with
layer separation D as an inverse exponential function of the decorrelation
length scale L: α=e-D/L. Thus, α and L are both used
to characterize the transition from the maximum to random overlap assumption
with increasing layer separations. Until now, many efforts have been made to
derive the values of α and L using ground-based radar observations
(e.g., Mace and Benson-Troth, 2002; Willén et al., 2005; Naud et al.,
2008; Oreopoulos and Norris, 2011) and to improve the representation of L
in the models (Shonk et al., 2010, 2014; Di Giuseppe and Tompkins, 2015). For
example, Oreopoulos and Norris (2011) derived L based on radar measurement
taken over the US southern Great Plains. Their results indicated that L
ranges from 2 to 4.5 km across different seasons and that smaller spatial
scales correspond to smaller L values. Based on 2 months of cloud mask
profile information from the space-based radar and lidar, Barker (2008)
quantified the properties of cloud overlap on a global scale and found a wide
range of L values, with a median value of 2 km. In other studies, the
decorrelation length scale L is also parameterized as a function of
latitude (Shonk et al., 2010, 2014), total cloud cover (Yoo et al., 2013), or
wind shear (Di Giuseppe and Tompkins, 2015). These findings suggest that
meteorological factors could be connected to the way in which cloud layers
overlap.
(a) CloudSat overpass tracks (blue line: daytime; red line:
nighttime) over the Tibetan Plateau (27–39∘ N;
78–103∘ E); (b) A sample of the CloudSat 2B-GEOPROF-LIDAR
cloud mask product along the ground track of 200 km (white color: cloud
fraction > 99 %; light blue: 0 < cloud fraction < 99 %;
deep blue: clear sky; orange color: surface). (c) The observed and
calculated segment-average cloud cover profiles based on maximum and random
assumptions for different spatial scales and given cloud mask sample
in (b). (d) The corresponding cloud overlap parameters of
contiguous cloud layers for 25, 50, 100, and 200 km spatial scales,
respectively. Note that the observations below 1 km over the TP surface have
been removed.
To date, however, the related question of the cloud overlap over the TP
region has received little attention due to the limited availability of
observations. It is still an open question as to how the unique thermodynamic
and dynamic environment over the TP affects cloud overlap there. The
millimeter-wavelength cloud profiling radar (CPR) launched on CloudSat
(Stephens et al., 2002) and the cloud-aerosol lidar with orthogonal
polarization (CALIOP) (Winker et al., 2007) launched on CALIPSO
(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) provide
an unprecedented opportunity to investigate vertical cloud overlaps on a
global scale (e.g., Barker, 2008; Kato et al., 2010; Mace et al., 2009; Li et
al., 2011a, b, 2015; Tompkins and Di Giuseppe, 2015). In the following study,
we investigate the cloud overlap properties over the TP region and identify
an empirical relationship between decorrelation length scale L and
large-scale atmospheric dynamics by combining the cloud cover profile
information from the 2B-GEOPROF-LIDAR dataset (Mace et al., 2009; Mace and
Zhang, 2014) and the meteorological fields from the ERA-Interim reanalysis
dataset (Dee et al., 2011). The parameterization of decorrelation length
scale L will help to improve the simulation of total cloud cover and the
calculation of radiative energy budget over the TP in the models. This paper
is organized as follows. The datasets and methods used in this study are
briefly described in Sect. 2. Section 3 outlines the monthly and zonal
variations in the cloud overlap parameters over the TP region. The impacts of
the atmospheric state and large-scale atmospheric dynamics on cloud overlap
are presented in Sect. 4. The conclusions and discussion are given in
Sect. 5.
Datasets and methods
Four years (2007–2010) of data from the CloudSat 2B-GEOPROF-LIDAR,
ECMWF-AUX, and the daily 6 h ERA-Interim reanalysis are used to analyze the impacts of
atmospheric states and dynamics on the cloud overlap over the TP
(27–39∘ N; 78–103∘ E) region (Fig. 1a).
Satellite datasets
Radar signals can penetrate the optically thick cloud layers that attenuate
lidar signals, but lidar signals may sense the optically thin hydrometeor
layers that are below the detection threshold of radar signals. Thus, with
the unique complementary capabilities of the CPR on CloudSat and CALIOP on
CALIPSO, the 2B-GEOPROF-LIDAR dataset produces the most accurate description
of the locations of the hydrometeor layers in the atmosphere on the global
scale (Mace and Zhang, 2014). In this dataset, every CloudSat profile
includes 125 height layers (e.g., vertical bin), and the Cloud Fraction parameter reports the fraction of the lidar volume within each
radar vertical bin that contains hydrometeors (Mace et al., 2009; Mace and
Zhang, 2014). Several previous studies have identified a cloudy atmospheric
bin based on different thresholds of the lidar-identified cloud fraction,
including a 99 % (Barker, 2008; Di Giuseppe and Tompkins, 2015) or a
50 % threshold (Haladay and Stephens, 2009; Verlinden et al., 2011).
Here, a threshold of 99 % is used in our study. Due to the significant
attenuation of lidar signals to the optically thick layers, this parameter
fails to provide the Cloud Fraction for optically thick layers.
Thus, we also use the radar information (i.e., cloud LayerBase and
LayerTop fields) from the aforementioned dataset to construct the
complete two-dimensional cloud mask (See Fig. 1b). It is
worth noting that the
2B-GEOPROF-LIDAR dataset does not distinguish between cloud and
precipitation; therefore, any bias in our results caused by precipitation
cannot be removed in current analysis. Besides the 2B-GEOPROF-LIDAR dataset,
the ECMWF-AUX dataset (Partain, 2004), which is an intermediate dataset
consisting of the ancillary ECMWF state
variables interpolated across each CloudSat CPR bin, is also used to provide
the pressure and height information of each vertical bin in the cloud mask
profile. The vertical and horizontal resolutions of these products are 240 m
and 1.1 km, respectively. To avoid sunlight scattering contamination to
lidar observation and to minimize surface contamination of the CPR, we only
use the nighttime datasets above 1 km over the TP surface in the following
analysis.
Meteorological reanalysis dataset
The 6-hourly ERA-Interim reanalysis with a grid resolution of
0.25∘× 0.25∘ (Dee et al., 2011) is used to
characterize the atmospheric thermodynamic and dynamic states over the TP.
For each cloud mask profile in the 2B-GEOPROF-LIDAR, the vertical profiles of
the zonal wind u, meridional wind v, relative humidity RH, specific
humidity sh, and atmospheric temperature T closest to the cloud profile in
both space and time are extracted and further interpolated vertically to
match the vertical bins of the cloud mask profile. Following Di Giuseppe and
Tompkins (2015), the u and v winds at every vertical bin are then
projected onto the satellite overpass track, being averaged in the
along-track direction for all profiles in the selected CloudSat data segment
to derive the scene-average, along-track horizontal wind V. Here, we define
the wind shear dV/dzi,j between the layers i and
j, as follows:
dV/dzi,j=maxVi;Vj-minVi;VjDi,j,
where Vi and Vj are the horizontal winds at layers i and j,
respectively, and Di,j is the layer separation distance. The derived
wind shear will be used to calculate the cloud overlap parameter. For the
CloudSat overpass track (Fig. 1a), Di Giuseppe and Tompkins (2015) indicated
that the cross-track shear of the zonal wind u contributes little to the
statistics of wind shear.
Similarly to the wind shear, we calculate the vertical gradient of the
saturated equivalent potential temperature (∂θes/∂zi,j) between the same two layers to
quantify the dependence of the cloud overlap on the degree of the conditional
instability of the moist convection. Here,
θes=θexpLvrsCpTθ=T1000p0.286,Lv=2.5×106-2323×(T-273.16)rs=shRH×(1-sh),
where θ is the potential temperature, Lv is the latent
heat of vaporization, rs is the saturation mixing ratio, Cp
is the specific heat capacity at a constant pressure, and T is the
atmospheric temperature. The smaller the ∂θes/∂zi,j, the more unstable the atmosphere.
Furthermore, the scene-averaged vertical velocity at 500 hPa is also
extracted from the ERA-Interim reanalysis to analyze the impact of vertical
motion on cloud overlap. The positive values are for the updraft, and
negative values are for the subsidence.
The overlap parameter and its dependence on the spatial scale
Previous studies have shown that the overlap parameter α and
decorrelation length L are sensitive to the spatial scale of the general
circulation models (GCMs) grid box (Hogan and Illingworth, 2000; Oreopoulos
and Khairoutdinov, 2003; Oreopoulos and Norris, 2011; Pincus et al., 2005).
For example, Hogan and Illingworth (2000) found that the cloud overlap parameter
tends to increase with decreasing spatial and temporal resolutions (i.e.,
increasing vertical and horizontal grid scales) of GCMs.
The dependence of α on the layer separation and its
sensitivity to the spatial scale for
(a) noncontiguous and
(b) contiguous cloud pairs; the horizontal bars correspond to
means ± 3 standard errors, which represent the upper and lower
endpoints of the 99 % confidence interval. (c) The probability
distribution functions (PDFs) of the along-track horizontal scales of the
cloud system at a different height over the TP region. (d) The
variations in cloud sample number and the cumulative percentages with cloud
layer separations for both noncontiguous and contiguous clouds at a given
spatial scale of 50 km. The cumulative percentages represent the proportions
of cloud sample below the corresponding layer separation to all samples.
To examine the dependence of the overlap parameter on the spatial scale, each
CloudSat orbit over the TP region is divided into segments with different
horizontal lengths including 25, 50, 100, and 200 km. Hereafter, this
horizontal length is referred to as the effective spatial scale of the GCM's
grid box. Figure 1b shows an example of cloud mask from the 2B-GEOPROF-LIDAR
dataset over the TP region. This cloud mask includes eight, four, two, and one
segments, which correspond to the horizontal resolution of 25, 50, 100, and 200 km, respectively. Given the threshold of 99 % for cloud fraction,
the segment-average cloud cover profile of each segment is first derived.
Here, it is important to emphasize that cloud fraction and cloud cover are
different variables in our study. The Cloud fraction reports the
fraction of lidar volumes in each radar vertical bin that contains
hydrometeors and is used to identify a cloudy atmospheric bin based on the
chosen threshold of 99 %. When averaging all cloud fraction profiles in
the along-track direction for a given CloudSat data segment, we derive the
segment-average cloud cover profile, which represents the percentage of
clouds in a given spatial scale and certain height. Then, the vertical
overlap between any two atmospheric layers in this profile is calculated if
the cloud covers (Ci and Cj) of both layers exceed 0. Layers are
analyzed in pairs and there is no “double-counting”. If cloud layer pairs have the
same separation distance but different altitudes, they will be categorized
as belonging to the same statistic group. Following Hogan and Illingworth (2000) and Di
Giuseppe and Tompkins (2015), we consider the non-adjacent layers to be a
contiguous cloud pair when all layers between them are classified as cloud
layers. Otherwise, these layers are classified as a noncontiguous cloud pair
(Hogan and Illingworth, 2000; Di Giuseppe and Tompkins, 2015).
Based on the definitions of different overlap assumptions and α in
the introduction section, Fig. 1c and d show an example of the observed and
calculated segment-average cloud cover profiles based on maximum and random
assumptions, and corresponding overlap parameters of contiguous cloud pairs
for the 25, 50, 100, and 200 km spatial scale in a given cloud mask sample
(Fig. 1b). It is clear that the observed and calculated cloud covers and
corresponding overlap parameters tend to increase as the spatial scale
increases. In the meantime, the observed cloud covers tend to transform from the
maximum to the random overlap assumption with increasing layer separations.
By collecting 4 years of cloud sample from the 2B-GEOPROF-LIDAR dataset,
Fig. 2a and b further show the dependence of α on the layer
separation and its sensitivity to the spatial scale for both noncontiguous
and contiguous cloud layers. Many studies have used ground- and space-based
radars to examine the validity of the random overlap assumption for vertically noncontiguous clouds (Hogan and Illingworth, 2000; Mace at al.,
2002; Naud et al., 2008; Di Giuseppe and Tompkins, 2015). Figure 2a shows
that the degree of cloud overlap of the noncontiguous clouds over the TP
region is lower than the random overlap, especially when the layer separation
is smaller than 2 km. Given the spatial scale of 50 km, almost all of the
α values are negative and fall between -0.25 and -0.05. Thus, the
total cloud cover would still slightly be underestimated for noncontiguous
cloud pairs by using the random overlap assumption. Assuming a cloud layer
separation of less than 9 km, α for noncontiguous cloud pairs
increases as the spatial scale increases (e.g., from 25 to 200 km). For a
contiguous cloud pair (Fig. 2b), α decreases from 0.95 to 0 with an
increasing separation. In the meantime, a slight dependence of α on the
spatial scale is also observed for contiguous cloud pairs when they are
separated by a distance of about 1 to 4 km. This indicates that the maximum
overlap is slightly more common for a larger horizontal domain, which is
consistent with previous studies (Hogan and Illingworth, 2000; Oreopoulos and
Khairoutdinov, 2003; Oreopoulos and Norris, 2011).
Selection of thresholds for cloud cover and spatial scale
Regarding the dependence of α on a spatial scale, Tompkins and Di
Giuseppe (2015) theorized that some overcast or single cloud layers would be
removed from the samples when the spatial scale is smaller than the cloud
system scale, thus biasing α and its decorrelation length L. Given a
spatial scale of 50 km, the ratio of the spatial scale to the cloud system
scale decreases strongly from the equator to the poles because many of the
frontal cloud systems of the middle and high latitudes are larger than the
convective cloud systems over the tropics. Ultimately, the corresponding bias
in α would increase with latitude. For these reasons, regional
atmospheric models should account for the typical cloud system scale in their
parameterization schemes when using a fixed horizontal resolution.
Figure 2c depicts the probability distribution functions (PDFs) of the
horizontal scales of the along-track cloud systems at different heights over
the TP region. Here, the horizontal scale of a cloud system at a given height
along the CALIPSO/CloudSat track is determined by calculating the number of
continuous cloud profiles (N) at a given height. Using a 1.1 km
along-track resolution for the CPR measurements, the along-track scale (S)
of a cloud system is S=N× 1.1 km (Zhang et al., 2014; Li et al.,
2015). It is clear that the probability of a cloud system with a small-scale
decreases with increasing height (Fig. 2c). The mean horizontal scale of
59.2 km for a cloud system at a height of 15 km is almost 12 times greater
than that (i.e., 4.6 km) at a height of 2 km. For the TP region, we can see
that the horizontal scales of cloud system below 10 km are smaller than the
spatial scale of 50 km; thus, we apply the spatial scale of 50 km to
perform the following analysis, although this scale would still result in
significant errors in α at greater atmospheric heights (e.g.,
15 km), where clouds have a larger horizontal scale.
The monthly variations in the pentad-averaged (a) cloud
overlap parameter, α, (c) conditional instability to moist
convection, ∂θes/∂z (K km-1),
(e) wind shear, dV/dz (m s-1 km-1), and (g) vertical velocity ω (hPa day-1) at 500 hPa,
for the contiguous cloud layers over the TP. The monthly variations in the
pentad-averaged (b)α, (d)∂θes/∂z, (f)dV/dz,
and (h)ω for the contiguous clouds for the layer separation
of 2 km (red) and 3 km (black).
In addition, to further reduce the sensitivity of α to the spatial
scale caused by data truncation, we follow the study from Tompkins and Di
Giuseppe (2015) and apply a simple data filter so that only atmospheric
layers with segment-average cloud cover below a given threshold of 50 %
are retained. As stated by Tompkins and Di Giuseppe (2015), data might still
be truncated with this filter, but the sensitivity of the results to the
spatial scale should largely be reduced. Here, we need to emphasize that the
thresholds of 99 and 50 % used in our study correspond to the
cloud fraction and cloud cover, respectively. After limiting the spatial
scale (50 km) and upper limit of cloud cover (50 %), the number of
available cloud layer pair samples is still at least 1 million, thus
ensuring representative sampling. Figure 2d shows the variations in sample
number and the cumulative percentage with cloud layer separation for both
noncontiguous and contiguous clouds at a given spatial scale of 50 km. It
shows that the cumulative proportion of cloud sample significantly increases
with increasing layer separation. For the contiguous cloud, the cumulative
percentage accounts for 90 % of all samples when layer separation is
smaller than 4 km. Given the 1.1 km along-track resolution of the CPR
measurements and a spatial scale of 50 km (that is, about 50 CloudSat
profiles), each cloudy CloudSat profile has a cloud cover of about 2 %
(Di Giuseppe and Tompkins, 2015).
Monthly and zonal variations in overlap parameter for contiguous
clouds
Figure 3a shows the monthly variations in α for the contiguous
cloud pairs based on pentad averages over the TP. In Fig. 3a, the maximum
separation of contiguous cloud layers gradually increases from January
(approximately 6 km) to August (beyond 8 km) and then gradually decreases,
indicating that the cloud systems over the TP during summer are thicker than
those clouds during other seasons due to frequent strong convective motions.
When the cloud layer separation is less than 1 km, the overlap parameter
α has little monthly variation and is always large (even beyond 0.7).
However, the monthly variation in α becomes manifest when the layer
separation is larger than 1 km. For a 2 km cloud separation, for example, α reaches its maximum of 0.45 in August and a minimum of 0.1 in February (see
Fig. 3d). For a separation of 3 km, α is generally lower but has the
similar monthly variation to those seen for a 2 km separation. The negative
values of α in Fig. 3a show that even the random overlap assumption could
underestimate the total cloud cover between two cloud layers with large
separation during all seasons except summer. These cloud overlap features may
be associated with the unique topographical forcing and corresponding
thermodynamic and dynamic environment of the TP. In summer, the TP is usually
considered an atmospheric heat source or “air pump” due to its higher
surface temperature compared with surrounding regions at the same altitude
(Wu et al., 2015). Additionally, humid and warm air intrudes from the South
Asia monsoon into the lower atmosphere over the TP, which intensifies the
atmospheric instability of moist convection when combined with the enhanced
surface heating (Taniguchi and Koike, 2008). This process further promotes
the transport of water vapor to high altitudes and favors the development of
convective clouds. Indeed, satellite observations have indicated that cumulus
prevails over the TP during the summer (Wang et al., 2014; Li and Zhang,
2016).
In view of the small horizontal scale of cumulus, a 50 km-spatial scale from
CloudSat should not bias the α estimate too much in our study.
However, previous studies have pointed out that precipitation may bias the
cloud overlap statistics toward maximum overlap (Mace et al., 2009; Di
Giuseppe and Tompkins, 2015), which is not accounted for in the present
study. If we exclude the samples with precipitation from the analysis, the
overlap parameter α would become smaller. The feature may be even
more obvious during summer due to more frequent precipitation over the TP
during this season (Yan et al., 2016). The seasonal variation in α is
also found at different ground sites (Mace and Benson-Troth, 2002; Naud et
al., 2008). For example, Oreopoulos and Norris (2011) indicated that cloud
overlap tends to be more random in the winter and mostly maximum during the
summer. In fact, these overlap properties are associated with the cloud
system scale, which is dominated by the large-scale dynamical situation
(Tompkins and Di Giuseppe, 2015).
The zonal variations in the (a)α,
(c)∂θes/∂z (K km-1),
(e)dV/dz (m s-1 km-1), and
(g)ω (hPa day-1) for the contiguous cloud layers over
the TP. The zonal variations in the (b)α,
(d)∂θes/∂z,
(f)dV/dz, and (h)ω for the
contiguous cloud layers for the layer separation of 2 km (red) and 3 km
(black).
Figure 3b and c show the monthly variations in pentad-averaged conditional
instability of moist convection (∂θes/∂z) and wind shear (dV/dz) for the contiguous
cloud pairs over the TP, respectively. Both ∂θes/∂z and dV/dz exhibit clear
monthly variations for all cloud-layer separations. The atmospheric stability
and wind shear gradually decrease from January to August and then steadily
increase (see Fig. 3c, d, e, and f). From Fig. 3c, we can see that the
adjacent atmospheric layers during May to September tend to be more unstable
and have weak wind shear. These atmospheric states favor the development of
clouds and result in maximum overlap between cloud layers. During other
months (e.g., December), clouds also tend to follow the maximum overlap more
although adjacent atmospheric layers are stable with large ∂θes/∂z and dV/dz. It might be
the case that vertical velocities are large because of extratropical
cyclones or other sources of baroclinic instability. When the layer
separation increases, atmospheric layers become more stable and then favor
random overlap, especially during the summer season. These results verify that a
more unstable atmosphere tends to favor a maximum overlap of cloud layers
over a random one, as shown in previous studies (Mace and Benson-Troth, 2002;
Naud et al., 2008). Note that Fig. 3d and f might reveal an inconsistency
between the wind shear and atmospheric stability. For example, we can see
that the wind shear for a 2 km layer distance is greater than that for a
3 km distance, but the atmosphere is also more unstable. This inconsistency
is probably because two cloud layers with the same separation but occurring
at different altitudes are sorted into the same statistical group. Or it is
also possible that other large-scale forcings might influence the overlap. In
addition, we find the monthly variations in pentad-averaged vertical velocity
(ω) at 500 hPa (see Fig. 3g and h) are also consistent with the
monthly cycle of α. It means that vigorous ascent tends to favor
maximum overlap. This result agrees well with the previous studies (Naud et
al., 2008).
The sensitivities of median overlap parameter α to
the (a) wind shear, (b) instability, and
(c) vertical velocity at 500 hPa at a given upper limit of cloud
cover (50 %) and spatial scale (50 km) for the contiguous cloud layers.
The horizontal bars correspond to means ± 3 standard errors, which
represent the upper and lower endpoints of the 99 % confidence interval.
Figure 4 shows the zonal variations in α, ∂θes/∂z, dV/dz, and ω
over the TP. Figure 4a and b indicate that α is larger in the south
part of the TP and smaller in the north. This is mainly because atmospheric
instability in the southern part of the TP enhances convective activity
(Fujinami and Yasunari, 2001). Due to the weakening of the monsoon and the
blocking by topography, less water vapor may reach the northern part of the
TP, and thus fewer clouds form there (You et al., 2014). Compared with the
southern TP, the stability and wind shear are both larger over the northern
part, especially for those cloud layers with large separation (e.g.,
> 2 km). These meteorological conditions will result in more frequent
negative α, indicating that the random overlap assumption used in models
would underestimate the total cloud cover and thus bias the surface radiation
over these regions (see Fig. 4a). The most significant warming occurring over
the northern part of the TP has been attributed to pronounced stratospheric ozone
depletion (e.g., Guo and Wang, 2012). However, a more recent study indicates
that the accelerated warming trend over the Tibetan Plateau may be due to the
rapid cloud cover increases at nighttime over the northern Tibetan Plateau
and the sunshine duration increase in the daytime over the southern Tibetan
Plateau (Duan and Xiao, 2015). Therefore, an accurate representation of cloud
overlap and its relations to atmospheric thermodynamic and dynamic conditions
in models are critically important to the understanding of rapid warming over
the TP. Although it is still difficult for models to capture the cloud
overlap properties, especially for those cloud layers with large separation
over the north TP, our results confirm that the α is well related to wind shear and instability. However, the zonal variation in α is
inconsistent with the variation in vertical velocity (see Fig. 4g and h).
Sensitivity of α to meteorological conditions and its
parameterization
To facilitate the parameterization of α for cases of contiguous
clouds, we further investigate the sensitivity of α to the different
meteorological conditions. Here, each meteorological factor over the TP
region is grouped into one of four bins as follows. The four bins for
∂θes/∂z are ∂θes/∂z> 5 K km-1, 2.5 <∂θes/∂z< 5 K km-1, 0 <∂θes/∂z< 2.5 K km-1, ∂θes/∂z< 0 K km-1. For wind shear, the
four bins are dV/dz< 0.5 m s-1 km-1,
0.5 <dV/dz<2 m s-1 km-1,
2 <dV/dz< 3.5 m s-1 km-1, and
dV/dz> 3.5 m s-1 km-1. For vertical
velocity, the four bins are ω<-40 hPa day-1,
-40 <ω< 0 hPa day-1,
0 <ω< 40 hPa day-1, and ω> 40 hPa day-1. These groupings ensure that a statistically
representative number of samples fall within each bin (i.e., at least
1 000 000 samples per bin). In addition, Li et al. (2015) indicated that
the overlap properties between different cloud types are also important for
the Earth's climate system. Although this study does not include the
information on cloud type, the dependence of α on meteorological
parameters found in our analysis actually demonstrates the effects of cloud
types on the α because different combinations of cloud type with the
same layer separation possibly occur in distinct wind shear and stability
conditions.
Parameterizations of decorrelation scale length L from the
exponential fit as a function of atmospheric stability ∂θes/∂z, wind shear dV/dz or
latitude Φ.
SchemeDescriptionDecorrelation length scale LWind shear (Di Giuseppe and Tompkins, 2015)Random/maximum, only wind shearL=4.4-0.45×dVdzWind shear (this study)Random/maximum, only wind shearL=2.19-0.14×dVdzWind-shear–instability (this study)Random/maximum, wind shear and instabilityL=2.18-0.09×dVdz-0.15×∂θes∂zLatitude (Shonk et al., 2010)Random/maximum, only latitudeL=2.899-0.02759×|Φ|
The monthly differences in total cloud cover (unitless) between
calculation and observation for different schemes (see Table 1) and their dependence on the layer separation.
Figure 5 illustrates the sensitivity of α to wind shear, instability,
and vertical velocity at a given upper limit of cloud cover (50 %) and
spatial scale (50 km) for the contiguous clouds. Since the cloud samples
with layer separation below 3.5 km account for 90 % of all samples for
contiguous clouds, we only present the results for layer distances smaller
than 3.5 km. Naud et al. (2008) tested the sensitivity of α to wind
shear at three sites and found that wind shear slightly affects α
when the layer distance is larger than 2 km. In a recent study, Di Giuseppe
and Tompkins (2015) demonstrated the important effect of wind shear on the
global cloud overlap by using a combination of the CloudSat-CALIPSO cloud
data and the ECMWF reanalysis dataset. Our results along with previous
studies suggest that the cloud overlap strongly depends on atmospheric
conditions, but their relationship displays some variability, in particular
spatially and seasonally. The effect of the atmospheric stability on cloud
overlap may be more important over convective regions (e.g., the
intertropical convergence zone and the TP during the summer season), while the effect
of wind shear may be dominant over the midlatitudes. Besides the wind shear
and instability, some studies also tested the sensitivity of the overlap
parameter to the large-scale vertical velocity. For example, Naud et
al. (2008) indicated that vertical velocities in the tropics are not captured
in the reanalysis dataset when convection occurs; thus, they only discussed
the impact of vertical velocity on the cloud overlap parameter over the
midlatitudes and found that vigorous ascent tends to favor maximum overlap.
Fig. 5c shows that vertical velocity at 500 hPa has some effect on the cloud
overlap parameter. However, by combining the effects of wind shear,
instability, and vertical velocity into parameterization of decorrelation
length scale L, we find that this scheme is not superior to a scheme which
only includes the wind shear and instability.
Here, we derive the decorrelation length scale L values (km) from the
least squares exponential fit to the original α curve at given wind
shear and instability bin. Then, we further parameterize L as a function
of wind shear or both wind shear and atmospheric instability based on a
(multiple) linear regression. The regression formula of L can be written as
L=Lα-b1∂θes∂z-b2dVdzorL=Lα1-c1dVdz.
Here, Lα, Lα1, b1, b2, and c1 are the fitting
parameters. Table 1 lists several parameterization schemes for the
decorrelation length scale L. The scheme with wind shear from Di Giuseppe
and Tompkins (2015) using the global CloudSat-CALIPSO cloud data and ECMWF
reanalysis dataset is shown for comparison. Di Giuseppe and Tompkins (2015)
discussed the uncertainties from fitting methods and the calculation of wind
shear. Related to the observational orbit, the impact of cross-track wind
shear is neglected in our study, which would exclude many large wind shears
associated with jet structures (Di Giuseppe and Tompkins, 2015). The
parameterization scheme of Shonk et al. (2010) is also shown in Table 1,
which is an empirical linear relationship between L and latitude based on
CloudSat and CALIPSO data. Our parameterization schemes in terms of wind
shear or both wind shear and instability are given in Table 1. Note that the
R-squared values (R2) for our wind shear and wind-shear–instability
schemes are 0.88 and 0.96, respectively.
The zonal differences in total cloud cover (unitless) between
calculation and observation for different schemes (see Table 1) and their dependence on the layer separation.
After deriving the regression formula of decorrelation length scale L, we
reapply it to all contiguous cloud samples and retrieve the L and
corresponding α based on the formula α=e-D/L and
dynamical conditions. Finally, the retrieved overlap parameter α is
used to calculate the total cloud cover between any two cloud layers by using
Eq. (1) and definitions of random and maximum overlap assumptions.
Figure 6 presents the monthly difference between calculated and observed
cloud covers using various overlap parameterization schemes. It is seen that
the maximum and random overlap assumptions result in large cloud cover
biases, especially for layer separations greater than 1 km for maximum
overlap and less than 2 km for random overlap where the bias exceeds
5 %. Compared with random and maximum assumptions, the differences
between total cloud cover caused by other schemes are small and range from -3 to
3 %. In addition, the wind shear scheme and the wind-shear–instability
scheme from the present study overall show less biases than other schemes.
However, several points should be further noted. First, the wind shear scheme
from Di Giuseppe and Tompkins (2015) significantly underestimates the cloud
cover for layer separations above 1 km (e.g., by up to 3 %). This large
bias may be because it is based on the global CloudSat-CALIPSO measurements
and ECMWF reanalysis dataset for a short period (January–July 2008); as
such, some obvious regional or seasonal cloud overlap properties are easily
obscured by global averaging. Furthermore, the role of atmospheric stability
is not considered in this scheme. However, the scheme from Di Giuseppe and
Tompkins (2015) shows little bias for layer separations below 1 km. This is
because this scheme retrieves much larger L and overlap parameter values
than other schemes. An interesting finding is that the latitude scheme from
Shonk et al. (2010) leads to a bias comparable to new schemes from this
study. The bias is even smaller for the latitude scheme when the layer separation
is below 1 km. In fact, Fig. 5 has demonstrated that the sensitivity of
α to wind shear and instability is rather weak when cloud layers are
very close. Our wind-shear–instability scheme further combines the impact of
atmospheric instability and has a relatively lower bias at large layer
separations with higher R-squared values (R2=0.96).
Figure 7 shows the zonal difference between calculated and observed cloud
covers for the aforementioned schemes. The differences between cloud cover caused
by different overlap schemes are obvious. Similar to Fig. 6, the maximum
and random overlap assumptions still result in the most prominent cloud cover
biases (exceeding ±5 %) at most of the layer separations. Compared with
our wind shear scheme and wind-shear–instability schemes, the scheme from Di
Giuseppe and Tompkins (2015) and latitude scheme from Shonk et al. (2010)
cause a clear underestimation of total cloud cover when cloud layer
separations exceed 1 km, especially for scheme from Di Giuseppe and
Tompkins (2015) (bias reaching -3 %). Only if cloud layer separations are
smaller than 1 km, do these two schemes produce a better cloud cover simulation
than our schemes. In summary, these results indicate that a new
parameterization (that is, our wind-shear–instability scheme) of
decorrelation length scale L, which includes the effects of both wind shear
and atmospheric stability on cloud overlap, may improve the simulation of
total cloud cover over the TP.
Conclusions and discussion
Clouds strongly modulate the Earth's radiative energy budget via changes in
their macro- and microphysical properties (e.g., Hartmann et al., 1992; Fu
and Liou, 1993; Fu et al., 2002; Kawamoto and Suzuki, 2012; Yan et al., 2014;
Wang et al., 2010). Many studies have shown that annual and seasonal changes
in total cloud cover are responsible for the rapid climate warming over the
Tibetan Plateau in the past 3 decades (e.g., Yang et al., 2012; You et al.,
2014; Duan and Xiao, 2015).
To accurately simulate the total cloud cover and its impact on the radiative
energy budget, climate models need to reliably represent the cloud vertical
overlap, which has received less attention than necessary because of the
limited availability of regional cloud observations. In view of the passive
sensors only providing limited information about the cloud overlap (Chang and
Li, 2005a, b; Huang, 2006; Huang et al., 2005, 2006) and the vertically
resolved advantage of active sensors (Ge et al., 2017, 2018; Zhao et al.,
2016, 2017), this study utilizes the 4 years (2007–2010) of data from the
CloudSat cloud product and collocated ERA-Interim reanalysis data to analyze
the cloud overlaps over the Tibetan Plateau and to build an empirical
relationship between cloud overlap properties and large-scale atmospheric
dynamics. It is confirmed that the contiguous cloud layers tend to have
maximum overlap at small separation but gradually become randomly overlapped
with an increase in the layer separation. Focusing on the contiguous cloud
layers, we evaluate the effects of the meteorological conditions on the cloud
overlap. It is found that the unstable atmospheric stratification with a weak
wind shear over the TP would tend to favor maximum overlap, agreeing well
with previous studies. We parameterize the decorrelation length scale L,
which is used to characterize the transition from the maximum to random
overlap assumption, as a function of the wind shear and atmospheric
stability. Compared with other parameterizations, this new scheme improves
the prediction of total cloud cover over the TP when cloud layer separations
are greater than 1 km. Although the scheme derived in our study focuses only
on the TP, our results suggest that the parameterization of the decorrelation
length scale L by considering multiple thermodynamic and dynamic factors
and microphysical effects (e.g., precipitation) has the potential to improve
the model-simulated total cloud covers.
In a recent study, Di Giuseppe and Tompkins (2015) applied the wind-shear-dependent decorrelation length scale in the ECMWF Integrated
Forecasting System. They found that the impact of wind-shear-dependent
parameterization on radiative budget calculation is comparable in magnitude
to that of the latitude-dependent scheme of Shonk et al. (2010). Our results also
show that the latitude-dependent scheme has a similar bias of cloud cover to the new scheme developed in this study. Although our results cannot
identify which of the schemes is superior, the scheme based on the
meteorological factors has some potential advantages. For example, the cloud
overlap parameter is significantly controlled by atmospheric thermodynamic
and dynamical conditions; therefore, the long-term variations in
meteorological factors are bound to affect the trend of cloud overlap and
corresponding calculations of total cloud cover and radiation budget. Indeed,
a recent study has shown that rapid warming and an increase in atmospheric
instability over the TP leads to more frequent deep clouds, which are
responsible for the reduction in solar radiation over the TP (Yang et al.,
2012). By using surface observations over 71 stations, some studies verified
that annual and seasonal total cloud covers have declined during 1961–2005
(Duan and Wu, 2006; You et al., 2014). However, whether such variations in
total cloud cover are linked with the changes in the degree of cloud overlap over
the TP is still unclear. Thus, more efforts are needed to evaluate the
impact of cloud overlap on the total cloud cover variations over sensitive areas of climatic change (e.g., the Tibetan Plateau and the Arctic).
The CloudSat datasets are available from the CloudSat
website: (http://www.cloudsat.cira.colostate.edu/order-data, CloudSat
dataset, 2018). The ERA-Interim reanalysis daily 6 h products are downloaded
from the ERA-Interim website:
http://www.ecmwf.int/en/research/climate-reanalysis/era-interim
(ERA-Interim, 2018).
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was jointly supported by the key Program of the National
Natural Science Foundation of China (41430425), the Foundation for Innovative
Research Groups of the National Science Foundation of China (grant no.
41521004), National Science Foundation of China (grant nos. 41575015 and
41575143), and the China 111 project (grant no. B13045). We would like to
thank the CALIPSO, CloudSat, and ERA-Interim science teams for providing
excellent and accessible data products that made this study
possible. Edited by: Amanda
Maycock Reviewed by: three anonymous referees
References
Barker, H. W.: Overlap of fractional cloud for radiation calculations in
GCMs: A global analysis using CloudSat and CALIPSO data, J. Geophys. Res.,
113, 762–770, 2008.
Barker, H. W. and Fu, Q.: Assessment and optimization of the Gamma-weighted
two-stream approximation, J. Atmos. Sci., 57, 1181–1188, 2000.
Barker, H. W., Stephens, G. L., and Fu, Q.: The sensitivity of
domain-averaged solar fluxes to assumptions about cloud geometry, Q. J. Roy.
Meteor. Soc., 125, 2127–2152, 1999.
Chang, F. L. and Li, Z.: A New Method for Detection of Cirrus Overlapping
Water Clouds and Determination of Their Optical Properties, J. Atmos. Sci.,
62, 3993–4009, 2005a.
Chang, F. L. and Li, Z.: A near global climatology of single-layer and
overlapped clouds and their optical properties retrieved from TERRA/MODIS
data using a new algorithm, J. Climate, 18, 4752–4771, 2005b.
Chen, B. and Liu, X.: Seasonal migration of cirrus clouds over the Asian
Monsoon regions and the Tibetan Plateau measured from MODIS/Terra, Geophys.
Res. Lett., 32, 67–106, 2005.
Chen, T., Rossow, W. B., and Zhang, Y.: Radiative Effects of Cloud-Type
Variations, J. Climate, 13, 264–286, 2000.Cheng, G. and Wu, T.: Responses of permafrost to climate change and their
environmental significance, Qinghai–Tibet Plateau, J. Geophys. Res., 112,
F02S03, 10.1029/2006JF000631, 2007.CloudSat dataset: CloudSat 2B-GEOPROF-LIDAR and ECMWF-AUX products, available
at: http://www.cloudsat.cira.colostate.edu/order-data, last access: 23
May 2018.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M., A. Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., Van De Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A., J. Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette,J. J., Park,
B. K., Peubey, C., De Rosnay, P., Tavolato, C., Thépaut, J. N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the
data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
Di Giuseppe, F. and Tompkins, A. M.: Generalizing Cloud Overlap Treatment to
Include the Effect of Wind Shear, J. Atmos. Sci., 72, 2865–2876, 2015.
Duan, A. M. and Wu, G. X.: Role of the Tibetan Plateau thermal forcing in the
summer climate patterns over subtropical Asia, Clim. Dynam., 24, 793–807,
2005.
Duan, A. and Wu, G.: Change of cloud amount and the climate warming on the
Tibetan Plateau, Geophys. Res. Lett., 33, 395–403, 2006.Duan, A. and Xiao, Z.: Does the climate warming hiatus exist over the Tibetan
Plateau?, Sci. Rep.-UK, 5, 13711, 10.1038/srep13711, 2015.ERA-Interim: ERA-Interim reanalysis daily 6 h products, available at:
http://www.ecmwf.int/en/research/climate-reanalysis/era-interim, last
access: 23 May 2018.
Fu, Q. and Liou, K. N.: Parameterization of the radiative properties of
cirrus clouds, J. Atmos. Sci., 50, 2008–2025, 1993.
Fu, Q., Cribb, M. C., Barker, H. W., Krueger, S. K., and Grossman, A.: Cloud
geometry effects on atmospheric solar absorption, J. Atmos. Sci., 57,
1156–1168, 2000.Fu, Q., Baker, M., and Hartmann, D. L.: Tropical cirrus and water vapor: an
effective Earth infrared iris feedback?, Atmos. Chem. Phys., 2, 31–37,
10.5194/acp-2-31-2002, 2002.
Fujinami, H. and Yasunari, T.: The seasonal and intraseasonal variability of
diurnal cloud activity over the Tibetan Plateau, J. Meteorol. Soc. Jpn., 79,
1207–1227, 2001.Ge, J., Zhu, Z., Zheng, C., Xie, H., Zhou, T., Huang, J., and Fu, Q.: An
improved hydrometeor detection method for millimeter-wavelength cloud radar,
Atmos. Chem. Phys., 17, 9035–9047, 10.5194/acp-17-9035-2017,
2017.Ge, J., Zheng C., Xie, H., Xin Y., Huang J., and Fu, Q.: Mid-latitude Cirrus
Cloud at the SACOL site: Macrophysical Properties and Large-Scale Atmospheric
State, J. Geophys. Res., 123, 2256–2271, 10.1002/2017JD027724, 2018.
Geleyn, J. F. and Hollingsworth, A.: An economical analytical method for the
computation of the interaction between scattering and line absorption of
radiation, Contrib. Atmos. Phys., 52, 1–16, 1979.Guo, D. and Wang, H.: The significant climate warming in the northern Tibetan
Plateau and its possible causes, Int. J. Climatol., 32, 1775–1781,
10.1002/joc.2388, 2012.
Haladay, T. and Stephens, G.: Characteristics of tropical thin cirrus clouds
deduced from joint CloudSat and CALIPSO observations, J. Geophys. Res., 114,
D00A25–D00A37, 2009.
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The effect of
cloud type on Earth's energy balance: Global analysis, J. Climate, 5,
1281–1304, 1992.
Hogan, R. J. and Illingworth, A. J.: Deriving cloud overlap statistics from
radar, Q. J. Roy. Meteor. Soc., 126, 2903–2909, 2000.
Huang, J. P.: Analysis of ice water path retrieval errors over tropical
ocean, Adv. Atmos. Sci., 23, 165–180, 2006.Huang, J. P., Minnis, P., and Lin, B.: Advanced retrievals of multilayered
cloud properties using multispectral measurements, J. Geophys. Res., 110,
D15S18, 10.1029/2004JD005101, 2005.Huang, J. P., Minnis, P., and Lin, B.: Determination of ice water path in
ice- over-water cloud systems using combined MODIS and AMSR-E measurements,
Geophys. Res. Lett., 33, L21801, 10.1029/2006GL027038, 2006.
Jing, X., Zhang, H., Peng, J., Li, J., and Barker, H. W.: Cloud overlapping
parameter obtained from CloudSat/CALIPSO dataset and its application in AGCM
with McICA scheme, Atmos. Res., 170, 52–65, 2016.Kang, S., Xu, Y., You, Q., Flügel, W. A., Pepin, N., and Yao, T.: Review
of climate and cryospheric change in the Tibetan Plateau, Environ. Res.
Lett., 5, 015101, 10.1088/1748-9326/5/1/015101, 2010.
Kato, S., Sun-Mack, S., Miller, W. F., Rose, F. G., Chen, Y., Minnis, P., and
Wielicki, B. A.: Relationships among cloud occurrence frequency, overlap, and
effective thickness derived from CALIPSO and CloudSat merged cloud vertical
profiles, J. Geophys. Res., 115, 1–28, 2010.Kawamoto, K. and Suzuki, K.: Microphysical transition in water clouds Over
the Amazon and China derived from spaceborne radar and Radiometer data, J.
Geophys. Res., 117, D05212, 10.1029/2011JD016412, 2012.Li, J., Hu, Y., Huang, J., Stamnes, K., Yi, Y., and Stamnes, S.: A new method
for retrieval of the extinction coefficient of water clouds by using the tail
of the CALIOP signal, Atmos. Chem. Phys., 11, 2903–2916,
10.5194/acp-11-2903-2011, 2011a.
Li, J., Yi, Y., Minnis, P., Huang, J., Yan, H., Ma, Y., Wang, W., and Ayers,
K.: Radiative effect differences between multi-layered and single-layer
clouds derived from CERES, CALIPSO, and CloudSat data, J. Quant. Spectrosc.
Ra., 112, 361–375, 2011b.Li, J., Huang, J., Stamnes, K., Wang, T., Lv, Q., and Jin, H.: A global
survey of cloud overlap based on CALIPSO and CloudSat measurements, Atmos.
Chem. Phys., 15, 519–536, 10.5194/acp-15-519-2015, 2015.Li, Y., Liu, X., and Chen B.: Cloud type climatology over the Tibetan
Plateau: A comparison of ISCCP and MODIS/TERRA measurements with surface
observations, Geophys. Res. Lett., 33, L17716, 10.1029/2006GL026890,
2006.Li, Y. Y. and Zhang, M.: Cumulus over the Tibetan Plateau in the summer based
on CloudSat–CALIPSO data, J. Climate, 29, 1219–1230,
10.1175/JCLI-D-15-0492.1, 2016.
Mace, G. G. and Benson-Troth, S.: Cloud-Layer Overlap Characteristics Derived
from Long-Term Cloud Radar Data, J. Climate, 15, 2505–2515, 2002.Mace, G. G. and Zhang, Q.: The CloudSat radar-lidar geometrical profile
product (RL-GeoProf): Updates, improvements, and selected results, J.
Geophys. Res., 119, 9441–9462, 10.1002/2013JD021374, 2014.Mace, G. G., Zhang, Q., Vaughan, M., Marchand, R., Stephens, G., Trepte, C.,
and Winker, D.: A description of hydrometeor layer occurrence statistics
derived from the first year of merged CloudSat and CALIPSO data, J. Geophys.
Res., 114, D00A26, 10.1029/2007JD009755, 2009.
Morcrette, J. J. and Fouquart, Y.: The Overlapping of Cloud Layers in
Shortwave Radiation Parameterizations, J. Atmos. Sci., 43, 321–328, 1986.
Morcrette, J. J. and Jakob, C.: The response of the ECMWF model to changes in
the cloud overlap assumption, Mon. Wea. Rev., 128, 1707–1732, 2000.
Naud, C. M., Del Genio, A., Mace, G. G., Benson, S., Clothiaux, E. E., and
Kollias, P.: Impact of dynamics and atmospheric state on cloud vertical
overlap, J. Climate, 21, 1758–1770, 2008.Oreopoulos, L. and Norris, P. M.: An analysis of cloud overlap at a
midlatitude atmospheric observation facility, Atmos. Chem. Phys., 11,
5557–5567, 10.5194/acp-11-5557-2011, 2011.Oreopoulos, L. and Khairoutdinov, M.: Overlap properties of clouds generated
by a cloud-resolving model, J. Geophys. Res., 108, 4479,
10.1029/2002JD003329, 2003.
Partain, P.: Cloudsat ECMWF-AUX auxiliary data process description and
interface control document, Coop. Inst. for Res. in the Atmos., Colo. State
Univ., Fort Collins, 2004.Pincus, R., Hannay, C., Klein, S. A., Xu, K. M., and Hemler, R.: Overlap
assumptions for assumed probability distribution function cloud schemes in
large-scale models, J. Geophys. Res., 110, D15S09,
10.1029/2004JD005100, 2005.
Shonk, J. K., Hogan, R. J., Edwards, J. M., and Mace, G. G.: Effect of
improving representation of horizontal and vertical cloud structure on the
Earth's global radiation budget. Part I: Review and parametrization, Q. J.
Roy. Meteor. Soc., 136, 1191–1204, 2010.
Shonk, J. K. P., Hogan, R. J., and Manners, J.: Impact of improved
representation of horizontal and vertical cloud structure in a climate model,
Clim. Dynam., 38, 2365–2376, 2014.
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., Benedetti, A., Mitrescu, C., and CloudSat
Science Team.: The CloudSat mission and the A-Train, A new dimension of
space-based observations of clouds and precipitation, B. Am. Meteorol. Soc.,
83, 1771–1790, 2002.
Taniguchi, K. and Koike, T.: Seasonal variation of cloud activity and
atmospheric profiles over the eastern part of the Tibetan Plateau, J.
Geophys. Res., 113, 523–531, 2008.
Tompkins, A. and Giuseppe, F. D.: An interpretation of cloud overlap
statistics, J. Atmos. Sci., 72, 2877–2889, 2015.
Verlinden, K. L., Thompson, D. W. J., and Stephens, G. L.: The
Three-Dimensional Distribution of Clouds over the Southern Hemisphere High
Latitudes, J. Climate, 24, 5799–5811, 2011.Wang, B., Bao, Q., Hoskins, B., Wu, G., and Liu, Y.: Tibetan Plateau warming
and precipitation changes in East Asia, Geophys. Res. Lett., 35, L14702,
10.1029/2008GL034330, 2008.Wang, M. Y., Gu, J., Yang, R., Zeng, L., and Wang, S.: Comparison of cloud
type and frequency over China from surface, FY-2E, and CloudSat observations,
in: Remote Sensing of the Atmosphere, Clouds, and Precipitation, edited by:
Im, E., Yang, S., and Zhang, P., International Society for Optical
Engineering, SPIE Proceedings, Vol. 9259, 925913, 10.1117/12.2069110,
2014.Wang, W., Huang, J., Minnis, P., Hu, Y., Li, J., Huang, Z., Ayers, J. K., and
Wang, T.: Dusty cloud properties and radiative forcing over dust source and
downwind regions derived from A-Train data during the Pacific Dust
Experiment, J. Geophys. Res., 115, D00H35, 10.1029/2010JD014109, 2010.Weger, R. C., Lee, J., Zhu, T., and Welch, R. M.: Clustering, randomness and
regularity in cloud fields: 1. Theoretical considerations, J. Geophys. Res.,
97, 20519–20536, 10.1029/92JD02038, 1992.
Willén, U., Crewell, S., Baltink, H. K., and Sievers, O.: Assessing model
predicted vertical cloud structure and cloud overlap with radar and lidar
ceilometer observations for the Baltex Bridge Campaign of CLIWA-NET, Atmos.
Res., 75, 227–255, 2005.
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance assessment
of CALIOP, Geophys. Res. Lett., 34, 228–262, 2007.
Wu, G., Duan, A., Liu, Y., Mao, J., Ren, R., Bao, Q., He, B., Liu, B., and
Hu, W.: Tibetan Plateau climate dynamics: recent research progress and
outlook, Natl. Sci. Rev., 2, 100–116, 2015.Wu, H., Yang, K., Niu, X., and Chen, Y.: The role of cloud height and warming
in the decadal weakening of atmospheric heat source over the Tibetan Plateau,
Sci. China Ser. D., 58, 395–403, 10.1007/s11430-014-4973-6, 2015.
Xu, X., Lu, C., Shi, X., and Gao, S.: World water tower: An atmospheric
perspective, Geophys. Res. Lett., 35, 525–530, 2008.Yan, H., Li, Z., Huang, J., Cribb, M., and Liu, J.: Long-term
aerosol-mediated changes in cloud radiative forcing of deep clouds at the top
and bottom of the atmosphere over the Southern Great Plains, Atmos. Chem.
Phys., 14, 7113–7124, 10.5194/acp-14-7113-2014, 2014.Yan, Y., Liu, Y., and Lu, J.: Cloud vertical structure, precipitation, and
cloud radiative effects over Tibetan Plateau and its neighboring regions, J.
Geophys. Res.-Atmos., 121, 5864–5877, 10.1002/2015JD024591, 2016.
Yanai, M., Li, C. F., and Song, Z. S.: Seasonal heating of the Tibetan
Plateau and its effects on the evolution of the Asian summer monsoon, J.
Meteorol. Soc. Jpn., 70, 319–351, 1992.
Yang, K., Guo, X., He, J., Qin, J., and Koike, T.: On the climatology and
trend of the atmospheric heat source over the Tibetan Plateau: an
experiments-supported revisit, J. Climate, 24, 1525–1541, 2011.Yang, K., Ding, B., Qin, J., Tang, W., Lu, N., and Lin, C.: Can aerosol
loading explain the solar dimming over the Tibetan Plateau?, Geophys. Res.
Lett., 39, L20710, 10.1029/2012GL053733, 2012.
Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., and Chen, Y.: Recent climate
changes over the Tibetan Plateau and their impacts on energy and water cycle:
A review, Global Planet. Change, 112, 79–91, 2014.Ye, D. Z. and Wu, G. X.: The role of the heat source of the Tibetan Plateau
in the general circulation, Meteorol. Atmos. Phys., 67, 181–198,
10.1007/BF01277509, 1998.Yoo, H., Li, Z., You, Y., Lord, S., Weng, F., and Barker, H. W.: Diagnosis
and testing of low-level cloud parameterizations for the NCEP/GFS model
satellite and ground-based measurements, Clim. Dynam., 41, 1595–1613,
10.1007/s00382-013-1884-8, 2013.
You, Q., Jiao, Y., Lin, H., Min, J., Kang, S., Ren, G., and Meng, X.:
Comparison of NCEP/NCAR and ERA-40 total cloud cover with surface
observations over the Tibetan Plateau, Int. J. Climatol., 34, 2529–2537,
2014.Zhang, D., Luo, T., Liu, D., and Wang, Z.: Spatial Scales of Altocumulus
Clouds Observed with Collocated CALIPSO and CloudSat Measurements, Atmos.
Res., 148, 58–69, 10.1016/j.atmosres.2014.05.023, 2014.
Zhang, H. and Jing, X. W.: Effect of cloud overlap assumptions in climate
models on modeled earth-atmophere radiative fields, Chinese Journal of
Atmospheric Sciences, 34, 520–532, 2010.
Zhang, H. and Jing, X.: Advances in studies of cloud overlap and its
radiative transfer in climate models, J. Meteorol. Res.-PRC, 30, 156–168,
2016.Zhang, H., Peng, J., Jing, X., and Li, J.: The features of cloud overlapping
in Eastern Asia and their effect on cloud radiative forcing, Science China
Earth Sciences, 56, 737–747, 2013.
Zhao, C. F., Liu, L. P., Wang, Q. Q., Qiu, Y. M., Wang, W., Wang, Y., and
Fan, T. Y.: Toward Understanding the Properties of High Ice Clouds at the
Naqu Site on the Tibetan Plateau Using Ground-Based Active Remote Sensing
Measurements Obtained during a Short Period in July 2014, J. Appl. Meteorol.
Clim., 55, 2493–2507, 10.1175/JAMC-D-16-0038.1, 2016.Zhao, C. F., Liu, L. P., Wang, Q. Q., Qiu, Y. M., Wang, Y., and Wu, X. L.:
MMCR-based characteristic properties of non-precipitating cloud liquid
droplets at Naqu site over Tibetan Plateau in July 2014, Atmos. Res., 190,
68–76, 10.1016/j.atmosres.2017.02.002, 2017.
Zhu, L., Xie, M., and Wu, Y.: Quantitative analysis of lake area variations
and the influence factors from 1971 to 2004 in the Nam Co basin of the
Tibetan Plateau, Chinese Sci. Bull. 55, 1294–1303, 2010.