The geographical distributions of summertime cirrus with different cloud top heights above the Tibetan Plateau are investigated by using the 2012–2016 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. The cirrus clouds with different cloud top heights exhibit an obvious difference in their horizontal distribution over the Tibetan Plateau (TP). The maximum occurrence for cirrus with a cloud top height less than 9 km starts over the western plateau and moves up to the northern regions when cirrus is between 9 and 12 km. Above 12 km, the maximum occurrence of cirrus retreats to the southern fringe of the plateau. Three kinds of formation mechanisms – large-scale orographic uplift, ice particle generation caused by temperature fluctuation, and remnants of overflow from deep-convective anvils – dominate the formation of cirrus at less than 9 km, between 9 and 12 km, and above 12 km, respectively.
Cirrus is the high-altitude ice cloud identified as one of the most uncertain components in the current understanding of the climate variability (Rossow and Schiffer, 1999; Sassen and Mace, 2002; Solomon et al., 2007). Cirrus clouds can profoundly affect the radiative budget of the earth–atmosphere system. They scatter the incoming solar radiation (albedo effect), prevent the outgoing longwave radiation from leaving (the greenhouse effect), and reemit the infrared radiation into space (infrared effect) (McFarquhar et al., 2000; Zerefos et al., 2003; Corti and Peter, 2009). The optical thickness and temperature of cirrus have the potential to change these radiative effects. Despite influencing the atmospheric heat transport, cirrus also plays an essential role in the stratosphere–troposphere exchange of trace constituents, especially water vapor (Rosenfield et al., 1998). Recently, there has been particular interest in cirrus in the upper troposphere and lower stratosphere (UTLS), a transition region generally recognized to control the entry of troposphere air into the stratosphere (Gettelman et al., 2004; Fueglistaler et al., 2009; Randel and Jensen, 2013).
With the onset of the Asia summer monsoon (ASM), abundant anthropogenic aerosols and their precursors are transported to the Tibetan Plateau (TP) and can be quickly conveyed to the upper troposphere (UT), with the vertical transportation being confined by the upper-level ASM anticyclone (Fu et al., 2006; Park et al., 2009; Randel et al., 2010). By scrutinizing the seasonal variation in moisture and cirrus over the TP, Gao et al. (2003) stated that the mean high cloud reflectance over the TP hit its peak in April and arrived at its minimum in November. Besides, the topographic lifting over a significant barrier can boost the elevation of relatively warm and moist air, which contributes to the substantial number of cirrus clouds from March to May (Zhao et al., 2019; Yang et al., 2020). Apart from the aerosols and water vapor, satellite observations also suggest that cirrus clouds are connected with the outflow from deep convection, which frequently occurs over the TP (Li et al., 2005; Jin, 2006). Therefore, the abundant aerosols and their precursors in the UTLS, the topographic lifting, and the deep convection activities could act together to promote frequent cirrus occurrence over the TP during the ASM period.
Currently, there are two leading mechanisms for cirrus formation: deep-convective detrainment and in situ formation associated with Kelvin or
gravity waves as well as synoptic-scale ascent (Jensen et al., 1996;
Pfister et al., 2001; Boehm and Lee, 2003; Immler et al., 2008; Fujiwara et
al., 2009). It is found that cirrus is directly related to
the fallout and decay of the outflow from deep convection (Prabhakara et
al., 1993; Wang et al., 1996). Observations show cirrus generally occurs in
the vicinity of convectively active areas like the tropical western Pacific
or at places with low outgoing longwave radiation (OLR) (Winker and
Trepte, 1998; Eguchi et al., 2007). Cirrus clouds are formed when deep
convection detrains hydrometeors from the planetary boundary to the upper
troposphere (Luo et al., 2011). Moreover, the temperature fluctuations
driven by the large-scale vertical uplifting or atmospheric wave activities
in the upper troposphere also lead to the in situ formation of cirrus
(Riihimaki and McFarlane, 2010). The role of the mechanisms mentioned above
in the formation of cirrus over the TP is more complex and less understood.
Ground-based radar and lidar observations are adopted to explore the
characteristics and potential causes of ice clouds over the plateau (He et
al., 2012; Zhao et al., 2016). However, discussions based on cloud top
height are relatively sparse due to the cloud contamination from the layer
above. Also, these observations are limited to a relatively short time and a fixed site, mainly Naqu (31.5
In this paper, we investigate the variation in cirrus spatial distribution over the TP from the altitude perspective. Our particular interest is to identify the dominant contributors to the formation of cirrus at different heights over the TP and to provide the first insight into the possible mechanisms on a regional scale. In Sect. 2, the descriptions of the data and method are presented. Section 3 provides the geographical distribution of cirrus and discusses its relationship with topographic height, gravity waves, and deep convection. Section 4 contains a summary and brief discussion.
The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) mission offers comprehensive observations of clouds and aerosols
from the troposphere to the stratosphere (Winker et al., 2009; Thorsen et
al., 2013), and it has been proved to be highly accurate and reliable in
detecting cirrus clouds (Nazaryan et al., 2008). To determine the occurrence
number of cirrus clouds at different heights, we use the CALIPSO cloud layer
level 2 Version 4.10 data (Vaughan et al., 2009), which are acquired from
the NASA Earth Sciences Data Center (ASDC) at:
CALIPSO original orbital daily data are interpolated into grid point data with a latitude-by-longitude resolution of
We also employ the OLR data from the National Oceanic and Atmospheric
Administration (NOAA) satellites. OLR is calculated daily as the average of
the daytime and nighttime measurements by the Advanced High-Resolution
Radiometer with
Data used in the paper also include the Japanese 55-year Reanalysis dataset
(JRA-55;
Chen et al. (2014) showed that JRA-55 gave the best capture of the diurnal
rainfall cycle over the TP and the eastward precipitation propagation to the
eastern lees among four reanalysis datasets. Besides, JRA-55 has the
smallest root mean square error in the
Profile data, such as temperature and specific humidity from ERA5, are also utilized in this study. The variables are vertically interpolated from 1000 to 1 hPa as 37 pressure levels. By verifying with 3000 high-quality and independent sounding observations, the ERA data are shown to produce a relatively small mean bias in temperature profiles during the TP Experiment (Bao and Zhang, 2013). Other studies also prove the reliability and quality of ERA temperature and geopotential height data over the plateau (Gerlitz et al., 2014).
Cirrus occurrence number is the total number of profiles identified as
cirrus. To better probe the vertical development of cirrus, cirrus
occurrence events are further grouped into four types based on the cloud top
height:
Distribution of cirrus occurrence numbers during the June–August
period from 2012 to 2016. The cirrus top height is
Figure 2 shows the monthly mean surface net thermal radiation, water vapor evaporation, latent heat flux, and sensible heat flux from ERA5 data. Radiative cooling is the net outgoing radiative energy flux (Sun et al., 2017); it can be given as
Geographical distribution of monthly mean surface
The zonal distribution of vertical winds averaged from
Figure 1b demonstrates the spatial distribution of cirrus occurrence number with cloud top height between 9 and 12 km from 2012 to 2016 in summer. It is evident that the occurrence number starts to reduce over the highland and expands towards the north and northeast of the plateau. Considering that large values also occur at the north side of the TP, cirrus with a cloud top between 9 and 12 km is generated by external forcing different from orography.
Jensen and Pfister (2004) pointed out that the in situ transient temperature
fluctuation can boost the atmospheric dehydration efficiency and produce a
more significant number of ice crystals along with smaller ice particle
size, consequently creating more cirrus events. Following the classical
Lorenz-type decomposition of atmospheric circulation (Lorenz,
1967; Lu et al., 2016), transient temperature fluctuation is calculated to
explain the formation of cirrus. Figure 4 shows the geographical
distribution of (a) transient temperature fluctuation and (b) 5-year
averaged specific humidity at 250 hPa (about 11 to 12 km). There is
significant temperature fluctuation at the north side of the Tibet Plateau,
with a peak near 79
Geographical distribution of
The cirrus distribution with the top height between 12 and 15 km is depicted in
Fig. 1c. The regions with maxima dramatically shift to the southern fringe of the
plateau, suggesting that cirrus above 12 km over the TP is triggered by
another formation mechanism. Dai et al. (2018) suggested cirrus that clouds over
Naqu are correlated with deep-convective activity over the Tibetan Plateau.
Deep convection is widely accepted as a key factor for cirrus formation. In
order to probe the connection between the cirrus higher than 12 km and deep
convection, the convective overflow height and daily averaged OLR
distribution for summers of 2012–2016 are displayed in Fig. 5a and b. The
altitude where the smallest potential temperature gradient is located is
defined as the maximum convective overflow level, and the cirrus base can be
found near or above the convective outflow level (Pandit et al., 2014). The
place where the maximum convective outflow level is around 12 km lies in
most areas of the eastern TP, and the OLR values in these regions are near 200 W m
It should also be mentioned that the timing of the twice-daily CALIPSO overpasses is not in sync with the period of daily OLR data. Meanwhile, the convective outflow level and OLR calculated from reanalysis data still exhibit bias and uncertainty over the TP at a regional scale. Therefore, deep convection only offers a necessary condition for the uplift of cirrus, but it is not sufficient enough to ensure the occurrence of cirrus. The areas of cirrus number maxima may not agree very well with the center of low OLR and high convective overflow height.
The cloud top upper limit for cirrus over the plateau is 18 km, as observed by lidar. However, for cloud top above 15 km, the CALIPSO lidar observations see much less cirrus over the plateau, and there is almost no geographical variation in cirrus numbers over these regions. Therefore, their features and the corresponding mechanisms are not discussed in this paper.
Distribution of
To quantify the impact of the above driving forces on cirrus formation
at their corresponding heights, we further calculate their pattern
correlation coefficients (Feng et al., 2016). These coefficients reveal the
relationship between two variables at corresponding locations. As indicated
by Table 1, topographic height determines the distribution of cirrus below 9 km with the pattern correlation coefficient being 93.7 %. For cirrus between
12 and 15 km, both the convective outflow level and OLR contribute to its
occurrence with pattern correlation coefficients of 77.9 % and
The pattern correlation coefficients between the two
variables. The asterisk represents coefficients passing the
In this paper, we investigated the spatial distribution of cirrus clouds
over the TP in the Asia summer monsoon season with 5 years of CALIPSO data (2012–2016). Remarkable differences in the distributions of cirrus occurrence
numbers are found at different heights. The cirrus with a cloud top altitude
less than 9 km extends over almost the whole western and central part of the
plateau, especially over the regions with a topographic height greater than
4500 m. For cirrus with a top height between 9 and 12 km, distinct maxima in
occurrence numbers move up to the northeastern plateau and the north side of
the TP. For cirrus between 12 and 15 km, the maxima retreat to the southern
region. There are three formation mechanisms which determine the cirrus top
height over the plateau, and the evidence is discussed in the following.
The cirrus with a top height below 9 km is closely tied to orography,
with a pattern correlation coefficient between the topographic height and
the cirrus occurrence number of 93.7 %. The surface radiative cooling and
latent heat brought by the terrain height in summer contribute to the cirrus
formation. Still, the weak subsidence in the upper layers prohibits further
vertical growth of cirrus over the west flank of the TP. The temperature perturbation induced by convective activities, including
gravity waves, is responsible for the cirrus occurrence maxima at the
corresponding locations when the cloud top is between 9 and 12 km. The
fluctuation can boost the atmospheric dehydration efficiency and influence
the ice nucleation process, generating more cirrus particles. The convective blow-off mechanism causes large values of cirrus numbers
between 12 and 15 km. The geographical distribution pattern of cirrus is quite
similar to that of the convective overflow height and OLR with the pattern
correlation coefficient being 77.9 % and
Our research provides the first detailed analysis of how the distribution of
cirrus shifts geographically over the TP from the height perspective over a
regional scale. The results help to map out the thermal and dynamical
structures of the atmosphere, which determine the vertical extent of cirrus
at different geographical locations over the plateau. The unique vertical
distribution of cirrus over the TP indicates special features of the
connection between cirrus and physical processes, and they are distinct from
interactions in other regions like the tropical ocean. Therefore, the
phenomena discovered in this article may promote our knowledge of cirrus
over the TP and provide useful information for model simulations. Since
CALIPSO crosses the Equator at 01:30 and 13:30 LT and the
orbit repeats only once in 16 d, our research is limited by the sampling
time and the orbiting range-resolved resolution. More precise verification
of the cirrus formation mechanisms needs to combine with intensive
geostationary and in situ observations to consider the diurnal cycle.
Meanwhile, the formation of cirrus requires the joint effort of sufficiently
cold and moist atmospheric conditions, favorable convective activities, and a possible condensation nucleus. Other potential mechanisms, such as
the Rossby wave (Dai et al., 2019), could also play a role. Our study
tends to address the most dominating mechanisms to generate cirrus over the
plateau and provides an insight into their physical process. Therefore, other
relatively trivial and less significant mechanisms are ignored in this
paper. In the future, we will explore other mechanisms more thoroughly by
case study if possible.
The datasets can be obtained from the corresponding author upon request.
QH and FZ designed the study. FZ, QH, QRY, JLM, and YW contributed to data analysis, interpretation, and paper writing. TC, DL, CC, and YG did further analysis and interpreted the results. All authors contributed to improving the paper.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Study of ozone, aerosols and radiation over the Tibetan Plateau (SOAR-TP) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
The authors gratefully acknowledge NOAA/OAR/ESRL PSD, Boulder, Colorado,
USA, for providing the interpolated OLR data on their website (
This study was partially supported by the National Natural Science Foundation of China (NSFC, grant nos. 91637101, 42075125, and 41775129), and the Shanghai Science and Technology Committee Research Special Funds (grant no. 16ZR1431700).
This paper was edited by Wenshou Tian and reviewed by three anonymous referees.