Large-scale deep convection associated with the Asian summer monsoon (ASM) during boreal summer induces a strong and persistent anticyclonic vortex in the upper troposphere–lower stratosphere (UTLS), known as the ASM anticyclone (ASMA) (e.g., Hoskins and Rodwell, 1995) or, previously, as the Tibetan high (e.g., Krishnamurti and Bhalme, 1976). The ASMA is confined by the subtropical westerly jet stream to the north (40–45∘ N) and the equatorial easterly jet stream to the south (10–15∘ N), and spans roughly one-third of the Northern Hemisphere's longitudes (20–140∘ E). Its geographic center is above the Tibetan Plateau and the altitude of maximum strength of the anticyclonic circulation is around the local tropopause (17–18 km), which is the highest worldwide during the ASM season (e.g., Dethof et al., 1999; Bian et al., 2012; Garny and Randel, 2016; Ploeger et al., 2015; Pan et al., 2016). The ASMA is subject to strong dynamical variability, oscillations and eddy shedding (e.g., Randel and Park, 2006; Yan et al., 2011; Garny and Randel, 2013; Vogel et al., 2014; Nützel et al., 2016).

From satellite measurements, the ASMA is known to be enriched in tropospheric trace species and pollutants, including water vapor, carbon monoxide, methane, hydrogen cyanide, peroxyacetyl nitrate (Randel et al., 2001, 2010; Park et al., 2004, 2007, 2008; Fadnavis et al., 2014; Ungermann et al., 2016) and aerosols, forming the Asian tropopause aerosol layer (ATAL) (Vernier et al., 2011, 2015; Thomason and Vernier, 2013). This is due to persistent deep convection over heavily polluted regions, such as the Indian subcontinent and southeast Asia, lifting pollutants from the boundary layer to the upper troposphere, where the anticyclonic winds keep the air masses horizontally confined. The unique dynamical structure of the ASMA, with the tropopause located at higher potential temperature than its surroundings (θ > 380 K), potentially provides a very efficient pathway for the transport of these pollutants into the lower stratosphere. Transport across the tropopause can occur either vertically above the ASMA, by radiative-driven slow ascent (e.g., Garny and Randel, 2016) or overshooting convection (Fu et al., 2006; “chimney model”), or adiabatically across the horizontal boundaries of the ASMA, hence bypassing the cold-point tropopause (“blower model”; Pan et al., 2016). Lagrangian trajectory calculations suggest that about half of the air mass in the ASMA enters the stratosphere by the end of the ASM season (Garny and Randel, 2016); however, the most effective transport pathway is currently the subject of debate (e.g., Orbe et al., 2015; Garny and Randel, 2016; Pan et al., 2016; Ploeger et al., 2017).

Lagrangian trajectories were also used to investigate the origin of the air masses in the ASMA (Bergman et al., 2013; Vogel et al., 2015), although this approach is limited by the convective nature of the transport. Nevertheless, these studies are consistent with satellite observations (Fu et al., 2006), regional weather forecasting (Heath and Fuelberg, 2014) and global atmospheric circulation models (Fadnavis et al., 2013; Pan et al., 2016) which all indicate that southern slopes of the Himalayas (i.e., latitudes approximately 25–35∘ N south of the Tibetan Plateau) are a hot spot for the transport of boundary layer pollutants to the ASMA. Considering the recent rapid increase of pollutant emissions from India (Krotkov et al., 2016), it is crucial for global chemistry climate models to properly represent the ASMA dynamics, thermodynamic structure and processes.

Currently, most of the observational evidence regarding the chemical composition of the Asian UTLS is derived from satellite measurements, providing information with good regional and temporal coverage, but with limited vertical resolution. Highly vertically resolved datasets in the UTLS are important for understanding the physical boundaries that control the vertical distribution of chemical species, and microphysical processes like the nucleation of cirrus clouds and aerosols. This requires accurate in situ measurements in the ASMA. Aircraft measurements are available from dedicated campaigns (e.g., Gottschaldt et al., 2018) or civil aviation-based observational networks (e.g., Rauthe-Schöch et al., 2016), although these are either sparse in space and time or limited by the relatively low cruising altitude of passenger aircrafts (10–12 km). Balloon-borne measurements are particularly suited to the investigation of the UTLS, and balloon campaigns dedicated to the study of the ASMA and ATAL have increased in frequency over the last decade (e.g., Bian et al., 2012; Vernier et al., 2018; this work).

In this article we present and discuss the data collected by the StratoClim balloon campaigns, carried out at two sites on the southern slopes of the Himalayas from 2016 to 2017. State-of-the-art instruments were used to measure vertical profiles of temperature, water vapor, ozone and aerosol backscatter, from the surface to the middle stratosphere. Here we first provide an overview of all of the measurements, showing their mean profiles and standard deviation ranges of natural variability for the different campaign periods. We then focus on analyzing the thermodynamic structure of the UTLS during the ASM season and how it relates to the vertical distributions of water vapor, ozone and aerosols. One aim of this work is also to pave the way for ongoing more targeted modeling and intercomparison studies within StratoClim and other activities.

The measurements were performed in Nainital, Uttarakhand, India (29.35∘ N, 79.46∘ E: NT), and Dhulikhel, Nepal (27.62∘ N, 85.54∘ E: DK), hosted by the Aryabhatta Research Institute of Observational Sciences (ARIES) and Kathmandu University (KU), respectively. Both sites are located on the southern slopes of the Himalayan mountain range, at elevations of 1820 m (NT) and 1530 m (DK) above sea level. In this region, the terrain elevation increases steeply from the sea-level heights of the Indo-Gangetic Plain to the south, to elevations above 3000 m on the Tibetan Plateau to the north. Strong orographic forcing induces persistent deep convection and heavy rainfall during the monsoon season (Vellore et al., 2016).

The measurements were conducted during three distinct periods of time, including two extensive monsoon-season campaigns, in NT in 2016 (2–31 August, 30 balloon soundings: NT16AUG) and in DK in 2017 (30 July–12 August, 28 balloon soundings: DK17), and one shorter post-monsoon campaign in NT (2–8 November 2016, 5 balloon soundings: NT16NOV) (note that a list of the main acronyms used in this paper, including the abbreviations of stations and campaign periods, is given in Table 1). The frequency of the soundings and the composition of the payloads varied depending on the meteorological conditions and the operational constraints. Various logistic limitations affected our DK17 campaign, resulting in a reduced measurement schedule (most notably, the number of backscatter measurements was limited). Nevertheless, important scientific data were collected. The DK17 campaign took place simultaneously with the StratoClim aircraft campaign, based at Kathmandu Airport (Nepal), which performed eight scientific flights using the high-altitude M55-Geophysica research aircraft.

All soundings employed meteorological latex balloons (TOTEX, Japan) that were filled with hydrogen gas in order to allow them to ascend at a rate of about 5 m s-1. The maximum burst altitude of these balloons is around 35 km, and more than 70 % of our soundings reached at least 30 km (see Table S1 in the Supplement). A standard meteorological radiosonde was used to host additional instruments through its XDATA interface (Oelsner and Tietz, 2017) and to transmit the data of all instruments to the ground station at a frequency of 1 Hz. In particular, we used RS41-SGP (Vaisala, Finland) radiosondes (Vaisala, 2017), and the DigiCORA MW41 sounding system (Vaisala, Finland) as a ground station (Vaisala, 2014). Additional instruments employed were an electrochemical concentration cell (ECC, manufacturer: EN-SCI, USA) (Komhyr, 1969) for the ozone (O3) mixing ratio, a cryogenic frost-point hygrometer (CFH, EN-SCI, USA) (Vömel et al., 2007, 2016) for the water vapor (H2O) mixing ratio, and the Compact Optical Backscatter Aerosol Detector (COBALD, MyLab, Switzerland) for aerosol backscatter.

For the pressure (p) and temperature (T) measurements analyzed in this work, the uncertainties of the RS41-SGP radiosondes(hereafter: RS41) given by the manufacturer are 0.6/1 hPa (at pressures lower/higher than 100 hPa) and 0.3/0.4 K (at altitudes lower/higher than 16 km), respectively. The performance of ECC sondes has been assessed by many studies (e.g., Smit et al., 2007), and the uncertainties are estimated to be 5–10 % in terms of O3 partial pressure. CFH is a frost-point hygrometer based on the chilled-mirror principle with an uncertainty of less than 10 % for the H2O mixing ratio up to an altitude of 28 km (Vömel et al., 2007). ECC and CFH have been regularly deployed in the ASM region since 2009 (Bian et al., 2012). COBALD is a detector for aerosol backscatter measurements at optical wavelengths of 455 nm (blue visible) and 940 nm (infrared) developed at ETH Zürich, and downscales the original backscatter sonde by Rosen and Kjome (1991) in weight and size. The COBALD is able to detect cirrus clouds (e.g., Brabec et al., 2012) as well as aerosol layers, such as the ATAL (Vernier et al., 2015). In addition, one RS92-SGP radiosonde (Vaisala, Finland) was added to almost all of the payloads for an intercomparison with the performance of the RS41 radiosondes (not discussed in this paper). Finally, we note that due to logistical constraints, the first two soundings in NT16AUG employed iMet-1-RSB radiosondes (InterMet, USA) (InterMet, 2006), which offer the XDATA interface (Wendell and Jordan, 2016) and utilize SkySonde version 1.9 (Jordan and Hall, 2016) as data acquisition software, instead of RS41.

In this study, we use the pressure measured by RS41 as the main vertical coordinate for all instruments. All variables are binned in pressure intervals of 1 hPa for p > 300 hPa, and 0.5 hPa for p < 300 hPa, yielding an improved signal-to-noise ratio and a dataset with consistent vertical levels. This binning corresponds to a vertical resolution of approximately 25 m in the UTLS. A quality check is performed for all instruments, based on an interpretation of their house-keeping data, and data points showing anomalous behavior are rejected. In this context, the contamination of CFH measurements deserves a special mention, as this effect was observed in a significant number of cases. It is seen in the drift towards high frost-point temperatures in the stratosphere, corresponding to water vapor mixing ratios exceeding physical constraints (see Fig. S1 in Supplement), which we attribute to the deposition of supercooled water droplets onto the inner walls of the instrument's inlet tube while passing through mixed-phase clouds. This hypothesis is currently the subject of a dedicated modeling study. To avoid instrumental artifacts such as these, we do not accept H2O mixing ratio measurements higher than 10 ppmv in the stratosphere for the analysis in this paper, which are unrealistic, as well as all measurements at pressures below 20 hPa (see Sect. 3). Ice saturation (Sice), i.e., relative humidity with respect to ice, is calculated using the frost-point temperature measured by CFH, the air temperature measured by RS41 and the parameterization for saturation vapor pressure over ice by Murphy and Koop (2005). The COBALD data are expressed as backscatter ratio (BSR), i.e., the ratio of the total-to-molecular backscatter coefficient. This is calculated by dividing the total measured signal by its molecular contribution, which is computed from the atmospheric extinction according to Bucholtz (1995), and using air density derived from the measurements of temperature and pressure (Cirisan et al., 2014). The COBALD BSR uncertainty as inferred by this technique is estimated to be around 5 % (Vernier et al., 2015).

The number of deployments of each instrument during the different campaign periods is summarized in Table 2. A full list of all 63 balloon soundings with date and time of launch, payload description, burst altitude and notes on malfunctions and contamination events is given in Table S1 in the Supplement. Note that, for the conversion of pressure to geometric altitude (z), mean profiles of p vs. z measured by RS41 are also shown in Fig. S2 in the Supplement.

Figure 1 shows mean profiles and standard deviations of temperature, the H2O mixing ratio and the O3 mixing ratio calculated from all measurements performed during the three campaign periods, namely NT16AUG (blue), NT16NOV (green) and DK17 (red). Figure 1a–c show the entire measured profiles, while Fig. 1d–f show a zoomed in profile of the UTLS region. In this section we briefly discuss their main features. Aerosol backscatter measurements will be discussed in Sect. 6.

In the troposphere, average H2O mixing ratios differ by up to a factor of 40 between the dry season(November) and the ASM season (July–August). Massive latent heat release by condensation during the wet period, in contrast to dry conditions in winter, is reflected in different lower tropospheric lapse rates for the two seasons, with about 5.5 K km-1 in July–August and 8 K km-1 in November. This is consistent with the meridional shift of the intertropical convergence zone (ITCZ) and the associated deep convection patterns in the Asian sector, which reach about 30∘ N in boreal summer (Lawrence and Lelieveld, 2010). Lower tropospheric O3 in NT16AUG compared to NT16NOV is likely due to the enhanced washout of ozone precursor gases during the monsoon season. Higher tropospheric O3 in NT16AUG vs. DK17 might be related to photochemical smog transport from the New Delhi urban area and the highly populated Indo-Gangetic Plain (e.g., Kumar et al., 2010).

The structure of the tropopause region is very different during the three measurement periods. In contrast to the sharp cold-point tropopause (CPT) of the ASM season, the November measurements show an almost isothermal layer above the lapse-rate tropopause (LRT, defined according to the World Meteorological Organization: WMO, 1957); therefore, in NT16AUG and DK17 the LRT coincides with the CPT, while in NT16NOV the average LRT and CPT are about 2.5 km apart. Seasonal variations of the LRT–CPT separation are consistent with Munchak and Pan (2014) and related to the varying meridional position of the jet stream (see Sect. 4.1). Interestingly, comparing the two ASM season datasets also reveals significant differences. The average CPT is 10 hPa higher (88 vs. 97.5 hPa), corresponding to about 600 m in altitude, and 5 K colder (-81.7 vs. -76.8 ∘C) in DK17 compared to NT16AUG. Water vapor in the UTLS is minimum in NT16NOV, with mixing ratios around 2.5 ppmv above the LRT. During the ASM, UTLS H2O is higher, but different vertical distributions are observed. In NT16AUG, H2O mixing ratio decreases monotonically with altitude, with a mean value of 6.8 ppmv at the CPT. In DK17, the H2O mixing ratio shows a minimum at the CPT (3.5 ppmv), and a local maximum above it (6 ppmv). Mean altitude, pressure, potential temperature and temperature of the LRT and CPT for the three campaign periods are summarized in Table 3.

Lower stratospheric temperatures (20–60 hPa) differ by about 2–4 K between November and July–August, which is consistent with the climatological annual cycle of stratospheric temperature (Randel et al., 2003). Stratospheric H2O mixing ratios are in the range of 4–6 ppmv up to 20 hPa, with a slight increase with altitude due to the oxidation of methane. Above approximately 20 hPa (27 km), all CFH measurements show an unrealistic increase in the H2O mixing ratio, which is a measurement artifact. At such high altitudes and low air densities, outgassing from the balloon skin and the payload train can play a significant role in contaminating the humidity measurements (Kräuchi et al., 2016). Water vapor data in this range are not considered in this analysis. Differences in stratospheric ozone between NT16AUG and DK17 are likely due to interannual variability.

For relating the measurements to the large-scale atmospheric flow, here we analyze meteorological data from the European Center for Medium-Range Weather Forecast (ECMWF) for the three campaign periods.

In this section we focus on analyzing the UTLS structure of the NT16AUG and DK17 measurements. The NT16NOV measurements will be discussed again in Sect. 6.

In this section we analyze the aerosol and cloud backscatter measurements by COBALD, which have not been discussed so far. COBALD was originally designed to investigate ice cloud properties, including cirrus (e.g., Brabec et al., 2012; Cirisian et al., 2014) and polar stratospheric clouds (e.g., Engel et al., 2014), yet recent measurements from Lhasa, China, were also used for in situ detection of ATAL aerosols (Vernier et al., 2015, 2018). Here we address both aspects.

Since the BSR of aerosol droplets is 1–2 orders of magnitude smaller than that of cirrus clouds, the characterization of the ATAL requires cloud-filtering techniques to eliminate in-cloud measurements, and a large dataset for a statistically significant evaluation (e.g., 18 soundings are used in Vernier et al., 2015). We performed 17 COBALD soundings in NT16AUG, but due to logistical constraints only 3 could be realized during the DK17 campaign; furthermore, these soundings mostly sampled cloudy conditions near the CPT. For this reason, a clear-sky aerosol BSR profile could not be established from the DK17 dataset. Conversely, the three COBALD soundings available from NT16NOV are almost fully clear-sky measurements and therefore allow for the calculation of a clear-sky aerosol BSR profile. The NT16NOV measurements provide a useful reference state of background aerosols, without ASMA confinement and without a supply of aerosols and precursor gases from the monsoonal deep convection, for comparison with NT16AUG. In the following, we first provide an overview of the main characteristics of the observed cirrus clouds, and then detail the cloud-filtering technique and the ATAL detection during the year 2016.

We analyzed 63 balloon measurements of temperature, water vapor, ozone and aerosol backscatter, collected during 2016 and 2017 on the southern slopes of the Himalayas. The UTLS structure in this region exhibits a strong seasonal variability, with tropical features (sharp CPT) during the ASM, and midlatitudinal features (large LRT–CPT separation) during the post-monsoon season. To analyze the structure of the UTLS during the ASM season, we formulated a framework composed of three significant levels (LRM, CPT, TOC) and two layers (ATTL, CLS), identified from the temperature and potential temperature lapse rates and Lagrangian backward trajectories – this framework is illustrated by the schematics in Fig. 6. Figure 12 summarizes the mean profiles of temperature, dθ/dz, the H2O mixing ratio, the O3 mixing ratio, ice saturation and aerosol BSR455 measured during NT16AUG (top panel) and DK17 (bottom panel), highlighting the relevance of these levels and layers.

During both of the ASM season campaigns, the isentropic level of the LRM (θ = 362–364 K) was higher than in previous measurements in the deep tropics (θ ≈ 345 K) (Gettelman and de F. Forster, 2002; Pan et al., 2014) and on the Tibetan Plateau (355–360 K) (Bian et al., 2012), suggesting that convection is deeply penetrating on the southern slopes of the Himalayas. The CPT (θ = 382–384 K) was also higher than at tropical sites (375 K) (Gettelman and de F. Forster, 2002; Pan et al., 2014), but lower than on the Tibetan Plateau (390 K) (Bian et al., 2012), which is consistent with the “bulging” of the CPT in the ASMA (e.g., Pan et al., 2016) and suggests an orographic influence. The average LRM and CPT occur at a higher altitude in DK17 than in NT16AUG (400–600 m), but due to the colder temperatures in DK17 (on average 5 K at the CPT), the shift in potential temperature space is small (1.5–2 K). We also note that the TOC coincides with a local maximum in the thermal stability profile (dθ/dz) in DK17, which is the same feature as the level of maximum stability defined by Sunilkumar et al. (2017).

In both NT16AUG and DK17, high H2O and low O3 were found in the ATTL and CLS, which is the signature of deep convection, extending up to 1.5–2 km above the CPT. Convective features are more pronounced in DK17 than in NT16AUG, suggesting that convective activity on the southern slopes of the Himalayas was more intense during the ASM season in 2017 compared with 2016. In particular, an isolated H2O maximum in the CLS was observed in DK17, which we argue may be due to overshooting convection, as previously observed by Corti et al. (2008) and modeled by Dauhut et al. (2015, 2016).

The fact that the average CPTs in our datasets occur at lower potential temperatures than previously found above the Tibetan Plateau suggests that, in addition to slow ascent and overshooting convection (discussed in Sect. 5.3), isentropic transport from the Tibetan Plateau (below CPT) to the southern slopes of the Himalayas (above CPT) might also contribute to the high H2O observed in the CLS. Nevertheless, since the isentropic level of the CPT is subject to strong instantaneous perturbations associated with convection and wave activity (e.g., Boehm and Verlinde, 2000; Sherwood et al., 2003; Munchak and Pan, 2014; Muhsin et al., 2018), a conclusion based on just the average CPT from a limited number of profiles is to be taken with caution. Therefore, further investigations will be required to assess the relevance of the different transport pathways.

The high H2O observed in the CLS is particularly interesting due to its potential implications for stratospheric moistening. The air masses in the CLS have already crossed the CPT, and will unlikely be subject to further dehydration, so it appears that the high H2O in this layer is prone to being lifted further and mixed into the (drier) free stratosphere. However, it has been shown that vertical transport above the ASMA might not be very efficient due to the slow ascending velocities of the Brewer–Dobson circulation in this region and season, and that the most efficient transport pathway is quasi-horizontal transport through the horizontal boundaries of the ASMA and subsequent upwelling in the stratosphere above the deep tropics (Pan et al., 2016). Therefore, the fate of the air masses in the CLS (hence the moistening potential of the high H2O in this layer) needs to be addressed by explicitly taking the horizontal motion of the air into account, which we do not investigate in this work.

Cloud-filtering of the NT16AUG aerosol backscatter measurements reveals the signature of the ATAL, extending from the LRM to the TOC with maximum backscatter at the CPT, and with similar BSR enhancement as in previous measurements from Lhasa, China (Vernier et al., 2015). No aerosol enhancement was found in NT16NOV. In both NT16AUG and DK17, ice saturation is minimum at the LRM and increases in the ATTL, similarly to the tropics (Vömel et al., 2002). Due to the much colder temperatures, average Sice in DK17 is remarkably higher than in NT16AUG, in addition to being higher than previous measurements from the Tibetan Plateau (Bian et al., 2012). Numerous thin cirrus clouds were detected during the NT16AUG and DK17 campaigns (often embedded in the ATAL), and their optical properties suggest they might have been formed by heterogeneous freezing.

Our analysis provides a comprehensive and high-resolution overview of the UTLS structure and composition on the southern slopes of the Himalayas. The observed vertical distributions of water vapor, ozone and aerosols in the ASMA are in good agreement with the thermodynamically significant levels that we define (LRM and CPT), and the extents of enhanced H2O and aerosols (ATAL) above the CPT are in good agreement with the top of anticyclonic confinement estimated from air mass backward trajectories (TOC). Our approach based on significant levels, rather than fixed pressure or altitude stacks, also provides physically meaningful diagnostics for the comparison of our in situ measurements with global climate model outputs.

As often mentioned throughout this paper, a wide range of modeling, interpretation and comparison studies are ongoing, and aim to explore all of the different insights offered by this dataset, in addition to assessing its relevance in the context of stratospheric moistening via the ASMA system and related transport pathways. These investigations include microphysical modeling, Lagrangian trajectory analyses, instrumental studies and comparisons with other in situ measurements, such as the airborne measurements of M55-Geophysica during the StratoClim 2017 aircraft campaign and balloon soundings performed from various stations on the Tibetan Plateau from 2013 to 2017, as well as comparisons with different global modeling products.