Atmos. Chem. Phys., 20, 11799–11808, 2020 https://doi.org/10.5194/acp-20-11799-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Possible mechanisms of summer cirrus clouds over the Tibetan Plateau Feng Zhang1,2, Qiu-Run Yu3, Jia-Li Mao4, Chen Dan4, Yanyu Wang5, Qianshan He6,7, Tiantao Cheng1, Chunhong Chen6, Dongwei Liu6, and Yanping Gao8 1Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China 2Innovation Center of Ocean and Atmosphere System, Zhuhai Fudan Innovation Research Institute, Zhuhai, China 3Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Quebec, Canada 4Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China 5Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, China 6Shanghai Meteorological Service, Shanghai, China 7Shanghai Key Laboratory of Meteorology and Health, Shanghai, China 8Shanxi Institute of Meteorological Sciences, Taiyuan, China Correspondence: Qianshan He ([email protected]) Received: 30 October 2019 – Discussion started: 2 January 2020 Revised: 30 July 2020 – Accepted: 9 August 2020 – Published: 19 October 2020 Abstract. The geographical distributions of summertime cir- 1 Introduction rus with different cloud top heights above the Tibetan Plateau are investigated by using the 2012–2016 Cloud-Aerosol Li- Cirrus is the high-altitude ice cloud identified as one of dar and Infrared Pathfinder Satellite Observation (CALIPSO) the most uncertain components in the current understand- data. The cirrus clouds with different cloud top heights ex- ing of the climate variability (Rossow and Schiffer, 1999; hibit an obvious difference in their horizontal distribution Sassen and Mace, 2002; Solomon et al., 2007). Cirrus clouds over the Tibetan Plateau (TP). The maximum occurrence for can profoundly affect the radiative budget of the earth– cirrus with a cloud top height less than 9 km starts over the atmosphere system. They scatter the incoming solar radia- western plateau and moves up to the northern regions when tion (albedo effect), prevent the outgoing longwave radia- cirrus is between 9 and 12 km. Above 12 km, the maximum tion from leaving (the greenhouse effect), and reemit the in- occurrence of cirrus retreats to the southern fringe of the frared radiation into space (infrared effect) (McFarquhar et plateau. Three kinds of formation mechanisms – large-scale al., 2000; Zerefos et al., 2003; Corti and Peter, 2009). The orographic uplift, ice particle generation caused by tem- optical thickness and temperature of cirrus have the potential perature fluctuation, and remnants of overflow from deep- to change these radiative effects. Despite influencing the at- convective anvils – dominate the formation of cirrus at less mospheric heat transport, cirrus also plays an essential role in than 9 km, between 9 and 12 km, and above 12 km, respec- the stratosphere–troposphere exchange of trace constituents, tively. especially water vapor (Rosenfield et al., 1998). Recently, there has been particular interest in cirrus in the upper tro- posphere 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), abun- dant anthropogenic aerosols and their precursors are trans- Published by Copernicus Publications on behalf of the European Geosciences Union. 11800 F. Zhang et al.: Possible mechanisms of summer cirrus clouds over the Tibetan Plateau ported to the Tibetan Plateau (TP) and can be quickly con- to provide the first insight into the possible mechanisms on veyed to the upper troposphere (UT), with the vertical trans- a regional scale. In Sect. 2, the descriptions of the data and portation being confined by the upper-level ASM anticyclone method are presented. Section 3 provides the geographical (Fu et al., 2006; Park et al., 2009; Randel et al., 2010). By distribution of cirrus and discusses its relationship with to- scrutinizing the seasonal variation in moisture and cirrus over pographic height, gravity waves, and deep convection. Sec- the TP, Gao et al. (2003) stated that the mean high cloud tion 4 contains a summary and brief discussion. 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 2 Data and method warm and moist air, which contributes to the substantial num- ber of cirrus clouds from March to May (Zhao et al., 2019; 2.1 Definition of CALIPSO cirrus clouds and the Yang et al., 2020). Apart from the aerosols and water va- NOAA OLR data por, satellite observations also suggest that cirrus clouds are connected with the outflow from deep convection, which fre- The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite quently occurs over the TP (Li et al., 2005; Jin, 2006). There- Observation (CALIPSO) mission offers comprehensive ob- fore, the abundant aerosols and their precursors in the UTLS, servations of clouds and aerosols from the troposphere to the topographic lifting, and the deep convection activities the stratosphere (Winker et al., 2009; Thorsen et al., 2013), could act together to promote frequent cirrus occurrence over and it has been proved to be highly accurate and reliable the TP during the ASM period. in detecting cirrus clouds (Nazaryan et al., 2008). To de- Currently, there are two leading mechanisms for cirrus termine the occurrence number of cirrus clouds at different formation: deep-convective detrainment and in situ forma- heights, we use the CALIPSO cloud layer level 2 Version tion associated with Kelvin or gravity waves as well as 4.10 data (Vaughan et al., 2009), which are acquired from synoptic-scale ascent (Jensen et al., 1996; Pfister et al., 2001; the NASA Earth Sciences Data Center (ASDC) at: https: Boehm and Lee, 2003; Immler et al., 2008; Fujiwara et al., //earthdata.nasa.gov/, (last access: 25 March 2019). With its 2009). It is found that cirrus is directly related to the fallout spatial resolution of 5 km and vertical resolution of 30 m and decay of the outflow from deep convection (Prabhakara (0–8.2 km) and 60 m (8.2–20.2 km), CALIPSO provides not et al., 1993; Wang et al., 1996). Observations show cirrus only the precise identification of cirrus clouds but also a generally occurs in the vicinity of convectively active areas glimpse into their vertical distribution, which allows us to like the tropical western Pacific or at places with low out- gain further insight into the formation mechanisms of cir- going longwave radiation (OLR) (Winker and Trepte, 1998; rus. To focus on the characteristics of cirrus occurrence dur- Eguchi et al., 2007). Cirrus clouds are formed when deep ing the ASM period, we collect 5 years of CALIPSO data convection detrains hydrometeors from the planetary bound- from June to August (2012–2016). The cloud layer prod- ary to the upper troposphere (Luo et al., 2011). Moreover, ucts include the feature classification flags to identify clouds the temperature fluctuations driven by the large-scale ver- and aerosols and to discriminate between their species fur- tical uplifting or atmospheric wave activities in the upper ther. The CALIPSO cloud subtyping algorithm follows the troposphere also lead to the in situ formation of cirrus (Ri- cloud top pressure thresholds from the International Satel- ihimaki and McFarlane, 2010). The role of the mechanisms lite Cloud Climatology Project (ISCCP) cloud-type classi- mentioned above in the formation of cirrus over the TP is fication scheme (Rossow and Shiffer, 1991). In this paper, more complex and less understood. Ground-based radar and we only use the data which are verified by the CALIPSO lidar observations are adopted to explore the characteristics discrimination algorithm as cirrus. (i.e., “Feature Type” pa- and potential causes of ice clouds over the plateau (He et rameter equals 2 and “Feature Subtype” parameter equals 6). al., 2012; Zhao et al., 2016). However, discussions based on Moreover, only data with a cloud-and-aerosol discrimination cloud top height are relatively sparse due to the cloud con- (CAD) score between 70–100 are considered in our analysis tamination from the layer above. Also, these observations are to avoid highly uncertain cloud features (Liu et al., 2009). limited to a relatively short time and a fixed site, mainly Naqu CALIPSO original orbital daily data are interpolated into (31.5◦ N, 92.1◦ E). Knowledge of the spatial distribution over grid point data with a latitude-by-longitude resolution of the whole plateau is not sufficient. Studies of cirrus charac- 1◦ × 2◦. We select relatively fine latitude grids and coarse teristics with different cloud top heights in a broader region longitude grids because observations are available along the are needed, and knowledge of their possible explanations is given CALIPSO orbit, while the adjacent track is separated critical to understand the thermal and dynamic effects of the by ∼ 1:6◦ in the longitude. The 1◦ × 2◦ box strikes a balance TP and to improve climate modeling further. between a region small enough to fully depict the variation in In this paper, we investigate the variation in cirrus spatial an individual grid and large enough to collect enough num- distribution over the TP from the altitude perspective. Our bers of observations. In this article, the TP is defined as the particular interest is to identify the dominant contributors to area that covers from 25 to 45◦ N and 65 to 105◦ E with an the formation of cirrus at different heights over the TP and altitude higher than 3000 m (Yan et al., 2016). In the cho- Atmos. Chem. Phys., 20, 11799–11808, 2020 https://doi.org/10.5194/acp-20-11799-2020 F.