2.1 WALES lidar measurements on HALO during the POLSTRACC campaign

From 7 December 2015 until 20 March 2016, the international field campaign PGS (POLSTRACC/GW-CYCLE/SALSA) was conducted with the High Altitude and Long Range Gulfstream G550 research aircraft HALO out of Oberpfaffenhofen, Germany (48^∘ N, 11^∘ E), and Kiruna, Sweden (68^∘ N, 20^∘ E). The POLSTRACC campaign particularly focused on investigating polar stratospheric chemistry and dynamics in the 2015–2016 Arctic winter. With a ceiling altitude of 15 km HALO is perfectly suited to study the evolution of the lower part of the polar vortex and to provide specific in situ PSC information at these altitudes.

PSC abundance above HALO flight altitudes was detected with the WALES lidar instrument (Wirth et al., 2009; Groß et al., 2014). The WALES lidar as configured during POLSTRACC has backscatter channels at 532 and 1064 nm wavelengths and additionally a high-spectral-resolution lidar (HSRL) channel and a depolarization channel at 532 nm wavelength for particle detection. The HSRL capability allows the retrieval of the extinction-corrected backscatter coefficient of clouds at 532 nm without assumptions about the phase function of the particles (Esselborn et al., 2008). The backscatter ratio R is the ratio of the un-attenuated (extinction-corrected) total (unpolarized) backscatter coefficient and the molecular backscatter. The extinction correction is done by the HSRL channel using a molecular reference profile calculated from pressure and temperature data from ECMWF operational analyses at ∼16 km horizontal resolution (6 h temporal resolution) and short-term forecasts (1 h steps) to interpolate between the analyses. To further distinguish between particles of different type we use the depolarization of the linear polarized laser light caused by scattering on non-spherical particles. The depolarization caused by molecular scattering is removed from the signal, following the method outlined by Freudenthaler et al. (2009). The quantity used further is therefore called linear particle depolarization ratio. The relative sensitivity of the two polarized channels is recalibrated regularly during flight to guarantee reliable depolarization values. The particle depolarization ratio is sensitive to the particle shape and size. Spherical particles do not depolarize and particles much smaller than the wavelength of the laser light also show unmeasurable low values. But in general there is no simple relation between depolarization and particle shape, size or composition; see for example Reichardt et al. (2002) for a more detailed discussion of this topic. Nevertheless, cloud regions which show distinct depolarization ratios point to a significantly different shape or size distribution. In combination with the backscatter ratio R, the particle depolarization has been successfully used to discriminate different PSC types from ground-based, airborne and spaceborne lidar measurements (e.g. Pitts et al, 2009, 2011, 2013; Achtert and Tesche, 2014, and references therein).

2.2 Spaceborne CALIOP lidar data

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite measures backscatter at wavelengths of 1064 and 532 nm, with the 532 nm signal separated into parallel and perpendicular polarization components. The general performance of CALIOP and calibration of the CALIOP data are discussed in Hunt et al. (2009) and Powell et al. (2009). The PSC results in this paper are based on the Version 2.0 CALIOP PSC detection and composition discrimination algorithm (Pitts et al., 2018), which uses night-time-only profiles from 8.2 to 30 km altitude of CALIOP V4.10 Lidar Level 1B 532 nm data smoothed to a uniform 5 km horizontal (along track) by 180 m vertical resolution grid.

Following the methodology of Pitts et al. (2009, 2013), PSCs are detected as statistical outliers relative to the background stratospheric aerosol population in either 532 nm perpendicular backscatter (ß_perp) or 532 nm scattering ratio R, which is the ratio of total backscatter to molecular backscatter. Successive horizontal averaging (5, 15, 45 and 135 km) is also used to ensure that strongly scattering PSCs (e.g. fully developed STS and ice) are found at the finest possible spatial resolution while also enabling the detection of more tenuous PSCs (e.g. low number density liquid–NAT mixtures) through additional averaging. CALIOP PSC composition classification is based on comparing CALIOP data with temperature-dependent theoretical optical calculations of ß_perp and R for non-equilibrium mixtures of liquid (binary H₂SO₄−H₂O or STS) droplets and NAT or ice particles. The assumption of NAT instead of nitric acid dihydrate particles is based on Höpfner et al. (2006a), who found no spectroscopic evidence for the presence of NAD from MIPAS observations of PSCs over Antarctica in 2003. The main improvement in the PSC algorithm (Pitts et al., 2018) is that the threshold value of R separating ice and NAT mixture PSCs (R_ice) is calculated as a function of altitude and time based on the observed abundance of nitric acid and water as estimated from nearly coincident Aura MLS measurements (Manney and Lawrence, 2016). Thus, R_ice (or 1∕R_ice) takes into account the impact of denitrification and dehydration on the optical signature of ice and NAT mixture clouds. In mid–late January 2016, 1∕R_ice ranges between 0.2 and 0.4 in the 18–24 km altitude region. The CALIPSO PSC areal coverage is estimated as the sum of the occurrence frequency (number of PSC detections divided by the total number of observations) in 10 equal area latitude bands spanning 90–50^∘ N, multiplied by the area of each band. We assume that the CALIOP observations from the approximately 15 daily orbits are representative of the PSC coverage within each latitude band. The data are aggregated on daily timescales and smoothed over 7 days to reduce noise.

2.3 Meteorological data sets

We use meteorological data from two operational analyses of the Integrated Forecasting System (IFS) of the ECMWF to describe the meteorological conditions of the polar stratosphere in winter 2015–2016. From December 2015 to March 2016, the IFS produced the operational analyses cycle 41r1 (T_L1279L137) with a horizontal resolution of about 16 km (0.14^∘ × 0.14^∘ at the equator) and the experimental IFS cycle 41r2 (T_C1279L137) simultaneously with a higher resolution of about 8 km (0.07^∘ × 0.07^∘ at the equator). The later IFS cycle became operational after 8 March 2016 (Hólm et al., 2016). The enhanced horizontal resolution was achieved by changing from linear to cubic spectral truncation and introducing an octahedral reduced Gaussian grid (Malardel and Wedi, 2016). Here, we show data in both resolutions for the 1 December 2015 to 8 March 2016 period to investigate the effects of a higher resolution on the meteorological data set, and after 8 March 2016 the high-resolution data are presented. We give 6-hourly operational analysis and use 1-hourly forecast data to interpolate between the time steps.

In addition, to compare to previous years, we use 6-hourly ERA-Interim reanalysis data (Dee et al., 2011) retrieved at a horizontal resolution of (1^∘ × 1^∘). ERA-Interim is a global atmospheric reanalysis from 1989 to today. The data assimilation system used to produce ERA-Interim data is based on the release of the IFS cycle 31r2 (T_L255L60) in 2006. The system includes a 4-dimensional variational analysis with a 6 h analysis window. The spatial resolution of the data set is ∼80 km horizontally on 60 vertical levels from the surface up to 0.1 hPa. ERA-Interim data can be downloaded from the ECMWF public data sets' web interface or from the MARS archive. A detailed documentation of the ERA-Interim data archive is given by Berrisford et al. (2011).

We derive the minimum temperature T_min (K) between 65^∘ and 90^∘ N at the 30 hPa pressure surface from the ERA-Interim reanalysis and from the two operational analyses of the IFS. We further calculate the area A_ice with temperatures below the ice frost point T_ice using Murphy and Koop (2005) and the area A_NAT with temperatures below the NAT equilibrium temperature T_NAT using Hanson and Mauersberger (1988) for 4.6 ppmv H₂O and 7 ppbv HNO₃, as measured by MLS in the Arctic vortex in January 2016 (Manney and Lawrence, 2016).

5.1 Extension of the PSC on 22 January 2016

A large synoptic-scale PSC was measured by WALES during a flight from Kiruna to the northern tip of Greenland on 22 January 2016 as shown in Fig. 3. The PSC extended between 14 and 25 km altitude over a horizontal distance of 2200 km. It was continuously observed within in the Arctic vortex from 72^∘ N to the outermost return point at 86^∘ N. High backscatter ratios are indicators for the presence of large particle surfaces and high depolarization ratios suggest the presence of solid particles. The co-located measurements of the particle backscatter ratio and depolarization ratio thus allow for a cloud classification into different PSC types.

Figure 4Composite 2-dimensional occurrence histogram of the PSC observed on 22 January 2016 shown in Fig. 3 in the 1 ∕ backscatter ratio versus particle depolarization ratio coordinate system. The solid black lines denote the boundaries of the PSC types defined by Pitts et al. (2011), with the threshold between ice and NAT Mix2 $\mathrm{1}/R_{\text{ice}}=\mathrm{0.3}$. The dotted line separates the high-depol and low-depol ice modes. The symbols indicate the starting points of trajectories within different PSC types (square: NAT Mix2, hollow diamond: high-depol ice, filled diamond: low-depol ice and circle: STS).

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5.2 Classification of the PSC measured on 22 January 2016

Figure 4 shows the joint occurrence histogram of the inverse backscatter ratio 1∕R and particle depolarization for the PSC measurements in Fig. 3. The histogram bin size is 0.02×0.02 and the colour scale indicates the number of cloud observations (4 km horizontal by 100 m vertical) falling within each bin. Overlaid are the regions which correspond to different PSC types following Pitts et al. (2011), with two modifications. First, the sub-classification of Mix2 into Mix2-enhanced and normal Mix2 is dropped. Second, the 1∕R_ice threshold for ice and NAT regions is set to 0.3. Without the latter change, a substantial part of the branch connecting STS and fully developed ice clouds would have been counted as NAT Mix2 instead of ice, while ice is the more obvious interpretation of the lower branch in the ice class given the form of the joint histogram. The 1∕R_ice threshold for ice and NAT regions is discussed in more detail below.

We find two modes in the joint histogram of the ice class, a mode with high particle depolarization ratio (high-depol ice) connected to the NAT Mix2 regime and a mode with lower particle depolarization ratio at the same backscatter ratio (low-depol ice) connected to the STS class. Given several tens of thousands of individual measurement points within the ice class, these two different ice modes can clearly be distinguished. The NAT Mix2 regime with enhanced NAT particle concentrations is strongly populated. In contrast, no observation falls into the wave ice class forming in strong mountain waves.

Figure 5Classification of the synoptic-scale polar stratospheric ice cloud on 22 January 2016 using the classification given in Fig. 3. A synoptic ice PSC (red area) extends over several hundred kilometres above the HALO flight track. The thick black line encloses the low-depol ice mode (see Fig. 4). To the north-west, the ice PSC is embedded in NAT layers (yellow: NAT Mix2, green: NAT Mix1). A liquid STS layer (blue) is located below and to the south-east of the ice PSC. The wave-ice class is not populated. The ending points of the trajectories for ice (high-depol ice (hollow diamond) and low-depol ice (filled diamond)), NAT (square) and STS (circle) given in Fig. 8 are marked. The dashed line shows the T_ice contour and the dotted line shows the T_NAT contour lines derived from 6-hourly IFS operational weather analysis (cycle 41r2), interpolated to 1-hourly time steps using meteorological forecast data, and the water vapour field measured by WALES as well as the HNO₃ field from MLS.

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We use the classification by Pitts et al. (2011) and the threshold of $\mathrm{1}/R_{\text{ice}}=\mathrm{0.3}$ of ice versus NAT Mix2 to classify the PSC from Fig. 3. A large ice PSC (red areas in Fig. 5) extends in a cloud layer with a vertical thickness up to 6 km between 18 and 24 km altitude over distances of over 1400 km. The ice PSC is measured twice for more than 1.6 h during the outbound flight leg between 10:34 and 12:30 UTC and during the inbound flight leg between 13:50 and 15:32 UTC. The synoptic-scale ice PSC is observed mainly at temperatures below T_ice, as indicated by the dashed contour lines. Here T_ice is calculated based on temperature data from the integrated forecast system IFS (cycle 41r1) of ECMWF and measured water vapour mixing ratios. In the northern part of the cloud between 12:30 and 13:50 UTC, the ice PSC is surrounded by NAT Mix2 cloud layers (yellow area) extending above and next to the ice PSC to the north-east. Within the ice PSC, NAT can be masked by the higher optical signal from ice, but may be present. The southernmost part of the PSC observed between 10:15 and 10:34 UTC and again between 15:15 and 16:00 UTC consists of non-depolarizing liquid STS droplets (blue region). The STS layer extends towards the south and below the ice layer.

Figure 6Occurrence histogram of ice, NAT Mix2, NAT Mix1 and STS of the 22 January 2016 PSC (Fig. 5) versus temperature difference to T_ice.

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The ice, NAT Mix and STS occurrence histogram of this PSC is plotted versus temperature difference to T_ice in Fig. 6. The peak in ice occurrence is located 0.5 K below T_ice. The peak in NAT Mix2 (NAT Mix1) occurrence appears at 0.5 K (3 K) above T_ice, while STS occurrence peaks 0.5 to 1.5 K above T_ice. The occurrence of ice at temperatures below T_ice and of NAT Mix and STS at temperatures above T_ice is consistent with their expected temperature range; see also Pitts et al. (2013).

The 1∕R_ice threshold of 0.3 best matches the PSC measurements in the low-depol ice mode (see Fig. 4). Also, the PSC area classified as ice agrees best with the ice area derived from numerical weather predictions. For this threshold, the peak of the NAT Mix2 occurrence is located above T_ice, while it is located near and below T_ice for 1∕R_ice of 0.2. Therefore we use the threshold 1∕R_ice of 0.3 for the WALES observations on 22 January 2016. The analysis of CALIPSO measurements throughout the winter 2015/16 with changing HNO₃ and H₂O concentrations requires a variable 1∕R_ice threshold as used by Pitts et al. (2018). A detailed investigation of the 1∕R_ice threshold is given in the Supplement S1.

Figure 7CALIPSO PSC measurements on 22 January 2016 near the HALO flight path. (a) Backscatter ratios from CALIPSO at 532 nm wavelengths and (b) PSC type classification from Pitts et al. (2018). The time of the overpass between 10:42 and 10:47 UTC as well as the horizontal distance and the latitude of the CALIPSO footprint are shown. The northernmost turning point of the CALIPSO footprint is marked by the triangle. The CALIPSO footprint is shown in Fig. 8.

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The PSC was also measured by CALIPSO on 22 January 2016 between 10:42 and 10:47 UTC. Figure 7 shows the CALIPSO curtain plot measured at the CALIPSO footprint near the HALO flight track shown in Fig. 8. The classification by Pitts et al. (2018) is used to determine the PSC types from CALIPSO. PSC observations by WALES on the slower-flying HALO aircraft lead to the time differences of up to 2 h between the collocated PSC measurements. Similar to the WALES observations, the CALIOP lidar detected an ice PSC between 18 and 24 km altitude with a horizontal extension of about 1200 km. An STS layer is located below the ice cloud and predominantly NAT Mix1 PSCs are measured to the north-west. Only a few NAT Mix2 PSCs were measured by CALIPSO, mainly located at the edge of the ice cloud. Differences in the flight paths of HALO and the CALIPSO footprint explain the lower number of NAT Mix2 PSCs observed by CALIPSO compared to WALES. WALES measured NAT Mix2 mainly north-west of the CALIPSO footprint. Also, a fraction of the low-depol ice mode was located predominantly north-west of the CALIPSO PSC curtain and is therefore barely covered by CALIPSO.

Figure 88-day back-trajectories starting in the different PSC layers measured on 22 January 2016. (a) Temperatures (colour coded) of the back-trajectories derived from the IFS operational analysis (cycle 41r1) starting every 2 min at 21.5^∘ altitude in the PSC on 22 January 2016. Four back-trajectories starting in STS (1, circle), low-depol ice (2, LDP-ice, filled diamond), high-depol ice (3, HDP-ice, open diamond) and NAT Mix2 (4, square) are shown as black lines with small symbols marking every 48 h. The HALO flight track is shown as brown line and the CALIPSO footprint as red line. The starting points of the four trajectories are also given in Figs. 4 and 5. The temperature evolution along the four trajectories (STS: blue, NAT: green, LDP-ice: red and HDP-ice: orange) is shown in panel (b) as temperature difference to T_NAT for 8 days and (c) as the temperature difference to T_ice for 36 h prior to the observation. The ice saturation ratio S_ice is given in panel (d). T_NAT is calculated from Hanson and Mauersberger (1988) for an altitude-dependent climatological HNO₃ profile; H₂O, T_ice and S_ice are calculated from the meteorological data set.

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6.1 Temperature history of trajectories starting in ice, NAT Mix2 and STS PSC layers

The trajectory calculations were performed using the Hybrid Single-Particle Lagrangian Integrated Trajectory dispersion model (HYSPLIT; Draxler and Hess, 1998) with operational forecast of the deterministic IFS (cycle 41r1 interpolated to 0.25^∘×0.25^∘) from ECMWF at 3-hourly time steps as meteorological input. We calculate 8-day back-trajectories starting in the PSC observations every 2 min along the HALO flight track (corresponding to approximately 24 km horizontal spacing) at 21.5 km altitude (near 30 hPa) to investigate PSC formation. We further calculate T_NAT based on Hanson and Mauersberger (1998) for an altitude-dependent climatological HNO₃ profile and use H₂O, T_ice and the ice saturation ratio S_ice from the meteorological data set to discuss ice nucleation pathways. Further we perform sensitivity studies every 2 min at higher and lower altitudes (19, 21 and 23 km) to account for particle sedimentation, which is not included in the simplified trajectory calculations shown here. As a rough estimate, a 10 µm sized ice crystal sediments about 1 km in a day (Fahey et al., 2001). For aspherical particles the sedimentation rates are even lower (Woiwode et al., 2014; Weigel et al., 2014; Woiwode et al., 2016); hence, our simplified calculations generally cover the altitude range of sedimenting PSC particles.

In Fig. 8 we show temperatures of back-trajectories starting every 2 min in the PSC at 21.5 km altitude at the WALES cross section of the outbound flight leg to the north (corresponding to the section from 10:30 to 12:34 UTC in Fig. 5). The air mass trajectories circulate around the pole within these 8 days, and the innermost trajectories (NAT Mix2 at the observation) stay near the Arctic cold pool and remain cold (T<190 K) for 8 days, while the outermost trajectories (STS at the time of observation) encounter higher temperatures (T>198 K) outside of the cold pool. The innermost NAT Mix2 trajectory 4 bypasses Greenland and circulates within the vortex core, while the more southern trajectories are slowly lifted above Greenland. The slow lift contributes to the synoptic cooling within the Arctic cold pool and induces a transient low-amplitude mountain wave temperature disturbance in the lee of the Greenlandic island (dark blue areas in Fig. 8). The synoptic cooling induces the formation of a large-scale ice PSC above and to the east of Greenland within the cold pool of the polar vortex. Teitelbaum et al. (2001) previously investigated the important role of synoptic-scale dynamics for PSC formation.

To investigate PSC formation, four trajectories representative of STS starting at 10:38 UTC (label 1, circle, blue line in panel B and C), in low-depol ice at 11:36 UTC (label 2, filled diamond, red line), in high-depol ice at 12:14 UTC (labels 3, open diamond, orange line) and NAT Mix2 at 12:34 UTC (label 4, square, green line) are selected to represent the temperature histories of the back trajectories of the different PSC types. The numbering of the trajectories is in chronological order during the outbound flight leg and the colour coding in panels (b) and (c) corresponds to the PSC type classification in Fig. 5, with a blue line for STS, a green line for NAT Mix2 and a red line for low-depol ice at the time of the WALES PSC observation. The orange line is used to distinguish the trajectory starting in the high-depol ice mode from the low-depol ice mode (red line). Small symbols denote 48 h time steps along the 8-day back trajectories.

Starting with the outermost STS trajectory, its temperature varies between T_NAT−2 K < T < T_NAT+11 K for 6.5 days as the trajectories circulate mainly out of the Arctic cold pool. Then, the temperature decreases below T_NAT∼36 h and below T_ice∼16 h prior to the observation before increasing to T_ice+2 K at the time of STS detection. The temperature at the time of observation is within the STS temperature range.

Slightly further to the north, the low-depol ice mode back-trajectory (red line) is located at the edge of the cold pool, with temperatures oscillating between T_NAT−6 K < T < T_NAT+6 K for 7 days. The temperatures decrease below T_NAT∼36 h and finally below T_ice∼16 h prior to the observation. At the time of observation of the low-depol ice mode, the trajectories' temperatures are near and below T_ice, explaining the detection of ice PSCs. The ice saturation ratio S_ice increases to 1.4 and varies between 1.4 and 0.92 in the last 16 h prior to observation. While S_ice<1.4 does not allow for homogeneous ice nucleation (Koop et al., 2000; Murphy and Koop, 2005), heterogeneous nucleation (e.g. Hoose and Möhler, 2012) could explain ice formation in this case. Thereby meteoric material (Cziczo et al., 2001; Voigt et al., 2005; Curtius et al., 2005) probably embedded in STS could serve as ice nuclei. This has been suggested by Engel et al. (2013) to explain the observation of an Arctic ice PSC in 2010. Small-scale temperature fluctuations might be present in our case, but cannot be resolved in the meteorological model.

In contrast, the northernmost NAT Mix2 trajectory (4, green line) circulates within the cold pool at temperatures T_NAT−8 K < T < T_NAT−4 K for 8 days without passing over Greenland. The temperatures remain above T_ice throughout that period and T>T_ice at the time of observations, consistent with NAT. Homogeneous NAT nucleation rates (Knopf et al., 2002) are too low to support homogeneous NAT nucleation in this case. In contrast, NAT nucleation rates on meteoric dust (Voigt et al., 2005; Grooß et al., 2005, 2014; Hoyle et al., 2013) may explain the formation of NAT within a few days and are consistent with the observations of NAT.

Similarly, the trajectory of the high-depol ice mode (3, orange line) circulates within the polar vortex for 8 days at temperatures between T_NAT−10 K < T < T_NAT. However, when passing over Greenland, the high-depol ice trajectories decrease below T_ice∼10 h prior to the observation. The temperature is T_ice−2 K at the time of observation of high-depol ice and S_ice increases up to 1.2. This temperature history supports heterogeneous ice nucleation. Laboratory measurements of heterogeneous ice nucleation (Hoose and Möhler, 2012) were performed for a suite of different ice nuclei at temperatures down to 213 K, hence above PSC formation temperatures. Meteoric dust could potentially serve as ice nuclei in the stratosphere at S_ice∼1.2; however, surface area densities of meteoritic material are significantly smaller than those of NAT PSCs. Therefore we investigate the possibility of ice nucleation on NAT and NAT serving as ice nuclei.

The temperature history of the high-depol ice trajectory also remains below the existence temperature of NAD ($T_{\text{NAD}}\sim T_{\text{NAT}}-\mathrm{2.3}$ K; Voigt et al., 2005) for about a week. The formation of NAD from binary nitric acid water solutions has been observed in the laboratory (e.g. Knopf et al., 2002; Wagner et al., 2005; Stetzer et al., 2006), but homogeneous nucleation rates are too low to explain denitrification (Knopf et al., 2002). Also, pseudo-heterogeneous nucleation rates (Knopf, 2006) cannot explain atmospheric nitric acid hydrate particle number densities. Möhler et al. (2006) suggest that ambient supersaturations with respect to NAD of 8 to 9 are required over days to explain NAD particle nucleation at number densities, which can be detected with current instrumentations. Furthermore, a phase change from NAD into NAT or a nucleation of NAT on NAD under atmospheric conditions is not supported by laboratory experiments (Tizek et al., 2004; Stetzer et al., 2006). Observational evidence for the presence of NAD in the atmosphere is missing so far; e.g. Höpfner et al. (2006a) found no spectroscopic evidence for the presence of NAD from MIPAS observations of PSCs over Antarctica. Therefore, we focus the discussion on ice nucleation on NAT in our study.

6.2 Ice nucleation pathways

The existence of the two ice modes with high and low particle depolarization ratios (Fig. 4) in combination with the trajectory analysis suggests two different ice nucleation pathways. (1) Ice nucleation in STS may account for the low-depol ice mode. Inclusions of meteoric material in STS as suggested by Engel et al. (2013) are below the detection limit of the WALES lidar but may be present. (2) A second ice nucleation pathway is required to explain the formation of the high-depol ice mode. The long time period below T_NAT prior to the observation and the similarity of the NAT Mix2 and the high-depol ice trajectories (except for the last hours, where the high-depol ice trajectories are ice supersaturated) points to ice nucleation on pre-existing NAT particles.

The hypothesis of ice nucleation on NAT ice is investigated in more detail using a Lagrangian match approach of 5-day back-trajectories matched with CALIPSO PSC measurements, similar to the method outlined by Santee et al. (2002). Domain-filling trajectories starting in the WALES ice PSC observations are calculated every 2 min along the flight path from 10:30 UTC until 16:00 UTC (corresponding to ∼24 km distance) at 15 different altitude levels with 500 m spacing between 17 and 24 km. Thus a total of 2490 trajectories are derived. We match the Lagrangian trajectories with all CALIPSO PSC curtain plots measured north of 60^∘ N within 5 days prior to 22 January 2016. The study of Pitts et al. (2018) is used to classify the PSC types within the CALIPSO curtains. A match is defined as a point along a trajectory lying within a given horizontal distance limit from one of the CALIOP footprints at the same time. To yield a statistically significant number of matches we set a distance limit of 100 km. A threshold of this order is justified by the limited accuracy of the trajectory calculations, too. In addition, to make the spatial resolution comparable with the initial spacing of the trajectories' starting points, 15 CALIPSO PSC data points with 3×180 m vertical and 5×5 km horizontal distance are grouped to smooth variations in the PSC type classification in nearby data points at the edge of PSC layers. A match point is classified as ice, NAT or STS, if >50 % of the CALIPSO pixel array is classified as the respective PSC type. The match method is selected to investigate whether a different PSC type was present before the high-depol and low-depol ice PSC observation by WALES on 22 January 2016. If so, the analysis indicates which PSC type has been detected prior to high-depol or low-depol ice.

Figure 9PSC type measured by CALIPSO prior to ice on match points with 5-day back-trajectories. Green squares indicate that NAT has been measured by CALIPSO prior to ice, blue circles indicate STS prior to ice, and light blue diamonds (nil) indicate that no PSC has been observed and (nom) that no match point of the trajectories and the CALIPSO curtain exist within 5 days. NAT has been detected by CALIPSO prior to the high-depol ice mode measured by WALES (thick black contour line) and predominantly STS or no PSC have been detected prior to the low-depol ice mode measured by WALES (thin grey contour line). UTC times of the WALES ice PSC observations and the horizontal distance are given. The starting points of the four trajectories from Fig. 8 are indicated by the black and white symbols.

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The trajectories are classified using the following criteria: (1) NAT has been measured in the CALIPSO curtain–trajectory match point prior to ice measured by WALES, or prior to the last CALIPSO match with ice on the same trajectory; (2) STS has been observed in the CALIPSO curtain–trajectory match point prior to ice; (3) no PSC (nil) has been observed in the match point prior to ice; or (4) no match (nom) has occurred along the trajectories.

The result of the match analysis is presented in Fig. 9 overlaid on the ice PSC contour of the 22 January PSC. The result is evident: NAT has been measured by CALIPSO on all match points directly prior to the high-depol ice mode. Thus the CALIPSO-trajectory match analysis delivers strong evidence for ice nucleation on NAT. Ice could have nucleated on pre-existing NAT particles, which could also explain the high particle depolarization ratio of the ice mode. In contrast, predominantly STS or no PSC has been observed on the match points prior to the low-depol ice mode. We note the possibility that due to fast evaporation times, STS might have existed along the trajectories classified as nil, but was not present at the time of the CALIPSO overpass. Only a few trajectories have no match points with CALIPSO.

To summarize, we find strong evidence for ice nucleation on NAT. Ice nucleated on NAT may produce ice particles with higher particle depolarization ratios. In contrast, the low-depol ice mode can be explained by ice nucleation in STS, possibly with solid meteoric inclusions as suggested by Engel et al. (2013). Ice nucleation in STS may lead to lower particle depolarization ratios of the ice mode. These two ice formation pathways would be consistent with the CALIPSO and WALES observations combined with results from domain-filling trajectory analysis.