Cirrus play an important role for weather and climate: besides their influence on the water vapor budget in the upper troposphere through condensation and evaporation and dynamics due to latent heat , they modify the radiation balance of the Earth and atmosphere. Thin, opaque cirrus clouds transmit most of the incident solar radiation and absorb long-wave radiation from the Earth's surface. As they are typically high and cold, they only emit little long-wave radiation into space, and thus cause a trapping of radiative energy in the Earth–atmosphere system, which eventually contribute to a rising surface temperature. If the cloud is thick, reflection of solar radiation back to space can become greater than the long-wave absorption, and consequently can cause the surface of the Earth to cool . This net radiative effect depends on macroscopic cloud properties like optical thickness, ice water content, and geometric extent as well as on its microphysical parameters such as ice crystal number, size, and shape .

Recent studies have investigated factors that affect these properties: the amount and composition of natural and anthropogenic aerosol particles in the troposphere and their ability to nucleate ice crystals are determining details of heterogeneous freezing , but aerosol distribution and properties have not been well known until today, in particular in the temperature range of cirrus clouds. Exact freezing conditions and mechanisms were studied by from in situ measurements taken during flight campaigns conducted in Central and North America. They found a dominance of heterogeneous freezing with mineral dust and metallic particles as the main source of residual particles. Although they managed to span a range of geographic regions and seasons, it is not clear whether those results are globally valid.

High water vapor supersaturations inside and outside of cirrus clouds point to several microphysical processes and often occur together with low ice crystal numbers . Sublimation of ice crystals was reported to result in the disappearance of facets and corners, changing the crystal symmetry . The dependence of freezing on the updraft velocity during cloud formation is theoretically known , but measurements are difficult and rare. In idealized settings, there is also a good understanding of the relevant processes in cloud formation and break up. But in nature no two clouds are alike and there exists a confusing variability of conditions under which they occur. This makes it difficult to represent cirrus clouds adequately in global circulation models for weather and climate prediction .

In order to gain more insight into the particular role of different cirrus clouds, great efforts were made to classify cirrus by the meteorological contexts in which they occur . Categories include synoptic, orographic, lee wave and anvil cirrus. Recently a more general classification was introduced that distinguishes the groups of liquid origin and in situ clouds that describe whether the cirrus is formed by existing water droplets freezing at water saturation or by the freezing of solution droplets or heterogeneous nucleation at ice supersaturation but below water saturation . Such classifications of recorded data are a prerequisite for statistically investigating the specific properties and influences of different clouds and to extract the governing mechanisms and parameters from remote sensing and in situ measurements.

Likewise, detailed knowledge of cloud properties at different stages of evolution is yet to be gained, as a cloud is expected to show different properties at the time of formation and dissipation. During the evolution of a cloud ice particle number, effective radius and particle shape evolve as well. For instance, showed that during dissipation, ice crystals lose single facets and corners, thus changing their geometry significantly. Besides such changes of microphysical properties, macrophysical qualities like the exact location, altitude and extent of cloud parts in a specific evolution state may also result in a different net radiative effect. A classification scheme that reveals the spatial distribution of evolution stages would facilitate the investigation of possible dependencies on cirrus evolution.

first illustrated and documented the vertical and dynamical structure of ice-generating cirrus uncinus clouds. This early work and following in situ measurements indicate that there is a vertical order of cirrus evolution stages with ice nucleation near cloud top level, deposition of water vapor onto ice crystals and thus particle growth in the middle, and sublimation and sedimentation at cloud base level. A more recent, statistical study by evaluated an extensive data set of ground-based lidar measurements taken at the ARM Southern Great Plains site (Oklahoma, USA) over a time period of 1 year. Vertical profiles of determined relative humidity with respect to ice (RHi) inside of cirrus clouds were divided into the upper most 25 %, the middle 50 %, and the lower 25 % of total cloud depth. The frequency distribution of RHi of the upper 25 % shows a considerable amount of supersaturated regions with high RHi values up to 160 %, associated with ice nucleation. The distribution of the lower 25 % is shifted towards subsaturation with a maximum between 70 and 80 % and values down to 10 %, clearly dominated by crystal sedimentation and sublimation. Therefore they showed that the generally accepted vertical order of evolution stages dominated the majority of measured clouds while individual clouds, depending on cloud type, generation mechanism, cloud age, and atmospheric dynamics, may show strongly differing distributions . The classification scheme that we present is based on atmospheric lidar cross sections and therefore facilitates the detailed investigation of evolution stages, their vertical and horizontal order, the impact of atmospheric dynamics, and their specific optical properties.

Such a classification needs information on RHi and temperature, as they are two governing variables in ice particle formation, growth, and disappearance . In order for ice to form in the atmosphere, RHi must reach or surpass 100 %. It is well known that tropospheric air masses often show substantial supersaturations with respect to ice . These so called ice-supersaturated regions (ISSRs) result mainly from upward motion of air masses originating in diverse atmospheric dynamics like large-scale synoptic ascent (e.g., warm conveyor belt), convective systems or mesoscale gravity waves .

The existence of an ISSR does not automatically imply the existence or formation of a cirrus cloud. For the homogeneous freezing (HOM) of solution droplets at cirrus temperatures (typically below 235 K), high supersaturations of the order of 140 % are necessary . Additionally, solid aerosol particles can act as ice nuclei and thus lead to freezing under a much broader range of conditions. Heterogeneous freezing onset temperatures and saturation ratios depend strongly on aerosol type, coating of the particles and their size, and are still subject to current research .

Once ice particles are present and no cooling, for example from updraft motion, takes place, remaining supersaturation is quickly depleted by deposition of water vapor onto existing crystals. In updraft regions of ice clouds, however, it may take a few minutes to a few hours until the air reaches the quasi-steady supersaturation which is a function of vertical velocity and ice particle size distribution and higher than 100 % for positive vertical velocities . Furthermore, effects that hinder phase relaxation are discussed, stemming from, for example, liquid coating around ice particles or dynamic processes . Thus, supersaturation inside of ice clouds can persist for a substantial amount of time. Likewise, regions of subsaturation with respect to ice can emerge when heavy ice particles sediment out of the ISSR, or RHi is reduced by warming. These regions are dominated by sublimation of ice crystals .

It can be seen that detailed knowledge of humidity and temperature in and around the cloud, as well as knowledge about freezing onset conditions is necessary in order to identify different stages of cloud evolution. The airborne Differential Absorption Lidar WALES (WAter vapour Lidar Experiment in Space), flown aboard the German research aircraft HALO (High Altitude and LOng range, model: Gulfstream G550), makes major parts of the needed data available. It provides an unique data set of collocated, high spatial resolution measurements of atmospheric backscatter, depolarization, and water vapor, enabling us to distinguish in-cloud and cloud-free regions, to identify the relevant aerosol type in the vicinity of the cloud, and to calculate relative humidity.

In this paper, we present a detailed classification scheme for the evolution of cirrus clouds. Besides WALES measurements we use complementary model temperature fields from the European Centre for Medium-range Weather Forecasts (ECMWF). Provided with high-resolution, two-dimensional lidar cross sections of the atmosphere, we are able to study the structure of clouds and the spatial distribution of classified evolution stages. By setting in situ and remote sensing data in perspective to cirrus evolution, it facilitates the study of the specific optical, microphysical, and radiative properties of evolution stages. We apply our scheme in a first case study of a lee-wave-influenced cirrus cloud over France, encountered during the ML-CIRRUS 2014 campaign , demonstrate its applicability, and investigate the impact of both mesoscale and large-scale dynamics on the cloud structure. We end with a summary of the classification scheme and a brief outline of its potential in cirrus cloud research.

In spring 2014, the Mid Latitude Cirrus Experiment ML-CIRRUS was conducted. It was designed to investigate natural cirrus and anthropogenic contrail cirrus with regard to their nucleation, life cycle, and climate impact. In this campaign, the German research aircraft HALO, equipped with a combined in situ and remote sensing payload, performed 16 measurement flights above Europe. The on-board cloud probes, WALES lidar and novel ice residual, aerosol, trace gas, and radiation instruments probed midlatitude cirrus clouds originating from, for example, air traffic, warm conveyor belts, jet streams, or mountain waves .

WALES is an airborne Differential Absorption Lidar that measures the tropospheric water vapor concentration below the research aircraft by simultaneously emitting laser pulses at three online and one offline wavelength in the water vapor absorption band around 935 nm . The averaged pulse energy is 35 mJ with a repetition rate of 200 Hz. The partly overlapping contributions from the three online wavelengths provide the needed sensitivity to compose a complete water vapor profile that ranges from just below the aircraft down to ground level. Additionally, WALES is equipped with one channel at 1064 nm and one high spectral resolution channel at 532 nm using an iodine filter. Both receiver channels are designed to detect the depolarization of the backscattered light .

WALES is capable of providing collocated measurements of humidity in the form of water vapor volume mixing ratio rw, backscatter ratio (BSR), and aerosol depolarization ratio (ADEP). Those measurements form a two dimensional curtain along the flight track of the research aircraft intersecting the atmosphere below. The lidar data we use in this paper have a vertical resolution of 15 m. Raw data are sampled at a rate of 5 Hz. At HALO's typical ground speed of 210 m s-1 and after averaging for a better signal-to-noise ratio, horizontal resolution is 2.5 km for humidity and 210 m for BSR and ADEP.

We use ECMWF analysis temperature data (available every 6 h), with a horizontal resolution of 0.25∘ and 91 vertical levels, which we interpolate linearly in time and bilinearly in space onto the lidar measurement cross section. Then we calculate relative humidity with respect to ice from this temperature information and the measured absolute humidity: RHi=rw×nair×T×kBesat,i(T), with temperature T, volume number density of air nair, and Boltzmann constant kB. We use the parameterization for water vapor saturation pressure over ice esat,i by .

The accuracy of calculated relative humidity relies strongly on the quality of absolute humidity and temperature data. WALES humidity measurements exhibit a mean statistical uncertainty of 5 %. The applicability of ECMWF temperature in this calculation was investigated by . They showed that during ascent and descent of a similar research flight in 2010, the mean temperature difference between ECMWF and on-board temperature sensors was 0.8 K and estimated a resulting maximum relative uncertainty of 10 to 15 % as an upper boundary for the calculated RHi at typical cirrus temperatures. In cases where collocated radiosonde or dropsonde measurements are available, their temperature profiles can be used to calibrate ECMWF temperature fields, eliminating possible offsets. As modern sondes feature measurement uncertainties of down to 0.2 K, the total relative uncertainty of RHi can therefore potentially be reduced to values as low as 6 %.

With atmospheric lidar cross sections at hand, we are able to identify in-cloud and cloud-free regions by applying a threshold for the backscatter ratio (see Fig. 1). As there is no sharp boundary between a cloud and its surrounding, this threshold value holds a certain arbitrarity. In the case study, we use a value of 2, but in cases where, for example, thick aerosol layers are present, this threshold might need to be increased to avoid classifying parts of the aerosol layer as in-cloud regions.

Looking at cloud-free parts of the cross section, regions that might possibly lead to cirrus cloud formation can be identified by searching for data points exhibiting ice supersaturation (RHi>100 %). Moderately supersaturated cloud-free parts are classified as ISSRout. With higher supersaturations, the chances for the imminent nucleation of ice particles become increasingly higher. Therefore we introduce the classes HETout and HOMout in our classification. They represent regions outside of the cloud where onset conditions for heterogeneous and homogeneous freezing, respectively, are surpassed. Their classification is implemented via temperature-dependent humidity thresholds.

It should be noted that ice forms at the latest point, as soon as conditions for homogeneous freezing are reached, as there is always a sufficient amount of solution droplets in the atmosphere . Therefore, a cloud classification should not feature considerable regions of HOMout. This fact should be kept in mind when choosing a BSR threshold value for the cloud border detection, making sure that HOM regions lie inside the cloud. However, this might not always be completely achievable without misclassifying aerosol layers (see above).

found that the homogeneous freezing temperatures of numerous aqueous solution droplets (1–10 µm) would fall on a single solute-independent curve, when plotted in terms of water activity aw. They suggested that this curve could be constructed by shifting the melting point curve by Δaw. From experiments on homogeneous freezing, they determined a shift by Δaw=0.305. For atmospheric applications, water activity is equal to relative humidity, when the droplet is in equilibrium with the water vapor pressure of the surrounding air . We use these findings to extract a parameterization of the temperature-dependent onset humidity for HOM (see Table 1, RHi,HOM(T)).

To determine a humidity threshold for HET, detailed information of the involved aerosol type, its coating, and size distribution would be required. Then results from laboratory experiments on onset freezing temperatures and saturations for this kind of aerosol could be used. As heterogeneous freezing conditions are still subject to current research and as comprehensive aerosol information is difficult to acquire solely from remote sensing, we make only a coarse distinction between two important aerosol types: mineral dust (MD) and coated soot (CS).

Together with a synergistic analysis, WALES lidar data can be used to identify the relevant aerosol type in the measurement area. To this end, we apply an aerosol classification suggested by . It uses the fact that aerosol types can be distinguished by three intensive optical properties, aerosol lidar ratio, aerosol linear depolarization ratio, and color ratio. Mineral dust shows linear depolarization values of more than 20 % and coated soot less than 20 %. Then we employ simplified onset parameterizations RHi,HETMD(T) and RHi,HETCS(T) (see Table 1 and their Fig. 4). Until more detailed parameterizations are available, this imposes an uncertainty for the determination of the exact border of heterogeneous freezing regions. In contrast to HOMout, HETout regions may exist in cases with no sufficient amount of aerosol ice nuclei or due to simplifications in the utilized RHi,HET(T) threshold. The classes ISSRout and HETout represent pre-stages of cirrus formation and indicate regions where a cirrus cloud is likely to develop.

Inside of a cloud (BSR > 2), we proceed in the same manner. When the RHi,HOM(T) threshold is surpassed we classify this region as HOMin. The region shows active ice nucleation. Together with HETin, which we classify analogously, it represents the youngest evolution stage of a cirrus cloud. HETin is also expected to show active nucleation as long as ice nuclei are present in the freezing region. However, due to the limitations mentioned above, the border of HET towards lesser supersaturated areas must be interpreted with caution. Also, ice crystals found in HET must not necessarily be formed by heterogeneous freezing, as sedimentation from higher levels featuring different nucleation conditions may take place. Still, heterogeneous freezing is an important freezing mechanism in midlatitudes and the class HETin adds more information to the classification, leading to a more complete characterization of cirrus clouds.

When relative humidity inside the cloud is lower than the freezing thresholds, we classify as DEP, as the remaining supersaturation is depleted by deposition of water vapor onto the existing ice particles. This intermediate evolution stage is dominated by depositional growth of ice crystals. The final evolution stage of a cloud sets in, when relative humidity falls below 100 %. In such an environment ice inevitably must sublimate. We classify this region as SUB.

During the life cycle of a cloud, nucleation, growth and sublimation events may occur more than once, e.g., when atmospheric dynamics cause renewed updrafts and a second freezing event on top of pre-existing ice takes place. As described, our method is able to identify nucleation, growth, sublimation regions and pre-stages of cloud formation. However on its own, it does not yield any information about earlier developments of those regions. Its very strength is to reveal the actual atmospheric state with regards to cirrus evolution at the time of measurement. This is done on a high spatial resolution that exceeds typical resolutions of GCMs, enabling the detailed study of individual cloud parts.

We demonstrate the applicability of our classification scheme in a cirrus case that was obtained during the ML-CIRRUS field campaign on 29 March 2014. The meteorological situation over western Europe and the Iberian Peninsula on the flight day is dominated by a trough extending from the west of Ireland to the Iberian Peninsula and further to the western part of northern Africa (Fig. 2a). At 300 hPa, high southerly winds with wind speeds up to 35 m s-1 are observed on the leading edge over southern France and Spain. Two days before the research flight, model forecasts indicated the existence of cirrus forming from high updrafts over the Pyrenees, as well as cirrus influenced by lee waves north of the mountain ridge. Additionally, highly dust-loaded air masses over southern France, originating from Saharan dust events over Algeria were expected.

In this meteorological setting the research flight was performed with the aim of sampling all stages of cirrus evolution that resulted from an overflow of the Pyrenees with high wind speeds and consequent gravity wave excitations in the lee of the mountain ridge. Therefore, the flight path in the relevant measurement region was chosen to run along the main wind direction, sampling the clouds along their path of advection.

The flight (Fig. 2, red flight path) started in Oberpfaffenhofen, Germany at 12:37 UTC and first went westward towards Paris, followed by a southward flight leg towards Spain at an altitude of 11 200 m. The investigated cirrus cloud was encountered over southern France during this leg, which runs with a bearing of 190∘ (white flight leg). Inside cirrus clouds, over the Pyrenees mountains, three legs at different lower altitudes followed. From the Mediterranean coast the aircraft turned eastward and probed cirrus at several altitudes near the Balearic Islands before it went northward towards Oberpfaffenhofen (landing at 19:50 UTC).

We presented a novel cirrus classification scheme capable of identifying all relevant stages of cirrus cloud evolution. It is based on airborne lidar measurements with high spatial resolution of water vapor, backscatter, and aerosol depolarization. This data are used together with ECMWF model temperature fields and knowledge and assumptions about onset conditions for homogeneous and heterogeneous freezing, to retrieve a cross section of the cloud, revealing the detailed distribution of evolution stages.

In cloud-free air (BSR < 2), ice supersaturated regions (ISSRout) as well as regions of homogeneous (HOMout) and heterogeneous freezing (HETout) are determined. These indicate favorable areas for cirrus cloud formation. Inside of a cloud, ice nucleation (HETin, HOMin), depositional growth (DEP), and sublimation regions (SUB) are distinguished. They represent the formation, growing, and breakup phases of a cirrus cloud, respectively.

We demonstrated the applicability of our classification in a first case study of a cirrus cloud that was observed in a complex meteorological situation comprising a thick aerosol layer, large-scale dynamics, and mesoscale gravity lee waves. Here it revealed a nonstandard horizontal order of the aforementioned evolution stages and helped to identify the influence of underlying wind and gravity wave conditions as well as large-scale dynamics on individual parts of the cloud.

With this valuable tool at hand, in our ongoing research we are investigating the large airborne lidar data set obtained during the ML-CIRRUS campaign. This classification scheme facilitates the study of the spatial distribution of evolution stages and can be used to set in situ and other remote sensing data, obtained during the campaign, in relation to cirrus evolution. By bringing together those data sources, the specific optical and microphysical properties of different cirrus stages can now be explored. The possibility of combining our classification with trajectory-based methods promises to reveal details of the temporal succession of evolution stages. Thus, we aim to achieve more detailed insights in radiative properties of cirrus clouds under various formation and life cycle conditions.