Determining stages of cirrus life-cycle evolution: A cloud classification scheme

The authors present an attempt to determine the stages of cirrus life-cycle evolution based on in-cloud RHi measurements performed by the airborne Lidar WALES. Though I like the idea and also find the paper well organized and fluently written, I have a major concern with respect to the proposed cirrus life-cycle classification scheme which I explain in the following. To my opinion this point should be cleared before publishing the manuscript in ACP.


Introduction
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 (Dinh et al., 2014) and dynamics due to latent heat (Spichtinger, 2014), 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 get greater than the longwave absorption, and consequently can cause the surface of the Earth to cool (Baran, 2009).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 (Schnaiter et al., 2016;Gallagher et al., 2012;Zhang et al., 1999).Today many factors are known that determine these properties: the amount and composition of natural and anthropogenic aerosol particles in the troposphere and their ability to nucleate ice crystals (DeMott et al., 2010), the exact freezing condition online wavelengths provide the needed sensitivity to compose a complete water vapor profile from the partly overlapping line contributions 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 (Esselborn et al., 2008).
WALES is capable to provide collocated measurements of humidity in the form of water vapor volume mixing ratio r w , 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 has a vertical resolution of 15 m.Raw data is sampled at a rate of 5 Hz.At HALO's typical ground speed of 210 ms −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 and forecast temperature data that we interpolate both temporally and spatially onto the Lidar measurement cross-section.Then we calculate relative humidity with respect to ice from this temperature information and the measured absolute humidity: with temperature T , volume number density of air n air , and Boltzmann constant k B .We use the parameterization for water vapor saturation pressure over ice e sat,i by Murphy and Koop (2005).
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 Groß et al. (2014).They showed that during ascent and descent of a similar research flight in 2010 the temperature difference between ECMWF and on-board temperature sensors was always less than 1 K and estimated a resulting maximum relative uncertainty of 10-15 % in the calculated RH i at typical cirrus temperatures.

Cirrus evolution classification scheme
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 e. g. 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 (RH i > 100 %).Moderately supersaturated cloud-free parts are classified as ISSR.With higher supersaturations, the chances for the imminent nucleation of ice particles get increasingly higher.Therefore we introduce the classes HET out and HOM out in our classification.regions of HOM out .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.HET out regions, however, may exist in cases with no sufficient amount of aerosol ice nuclei.For homogeneous freezing, we extract a parameterization of the temperature dependent onset humidity for HOM from Koop et al. (2000, their Fig. 3) for a nucleation rate ω = 0.0167 s −1 and a droplet size of 0.5 µm (see Table 1, 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 (Hoose and Möhler, 2012) 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 measurements of aerosol linear depolarization ratio (ADEP) and Lidar ratio can be used to identify the relevant aerosol type in the measurement area.To this end, we use an aerosol classification suggested by Groß et al. (2013).Then we employ simplified onset parameterizations RH M D i,HET (T ) and RH CS i,HET (T ) (see Table 1 and Krämer et al. (2016, their Fig. 4)).Until more detailed parameterizations are available, this imposes a uncertainty for the determination of the exact border of heterogeneous freezing regions.The classes ISSR and HET out 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 RH i,HOM (T ) threshold is surpassed we classify as HOM in .This region shows active ice nucleation.Together with HET in , that we classify analogously, it represent the youngest evolution stage of a cirrus cloud.HET in will also show active nucleation as long as ice nuclei are present in the freezing region.
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.This classification scheme is applied independently to every recorded data point, enabling the detailed study of individual cloud parts.

Case study ML-CIRRUS 2014-03-29
We demonstrate the applicability of our classification scheme in a cirrus case that was obtained during the ML-CIRRUS field In this meteorological setting the research flight was performed with the aim to sample 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 11200 m.The investigated cirrus cloud was encountered over Southern France during this leg that is running 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).

Cirrus leg overview
The following discussion of the classification scheme focuses on the southward flight leg stretching about 400 km to the north and 200 km to the south of the Pyrenees (Fig. 2, white flight path).Fig. 2 b shows a false color image of the Pyrenees area derived from SEVIRI ("Spinning Enhanced Visible and InfraRed Imager") data at 14:30 UTC.Higher, cooler clouds have a bluish color; low clouds are depicted in yellow.Coming from the North, the flight path intersects an ice cloud that is part of a larger cloud regime expanding from Southern France towards the Algerian coast.This cloud is followed by a completely cloud-free area north of the Pyrenees.Over the mountain ridge some localized high clouds are crossed.
In Fig. 3 we plot a cross-section showing backscatter ratio at a wavelength of 532 nm along the chosen part of the flight path.Here atmospheric features apparent in Fig. 2 can be studied in greater detail.On the lee side, north of the Pyrenees (14:19-14:34 UTC), a high cirrus cloud is observed that extends from a height of 7 km to 11 km.The southern and middle parts are dominated by high BSR values from 50 up to 200, whereas the northern section exhibits lower values.Aerosol linear depolarization ratios of more than 30 % inside the cloud (not shown) and temperatures below 240 K clearly indicate a pure ice cloud.Over the Pyrenees (14:42-14:53 UTC) a lower cirrus cloud is located at an altitude of about 6 km.Its spatially restricted occurrence over the mountain ridge indicates a formation due to forced updrafts stemming from the southerly cross-mountain flow.Even lower, at a height of 4 km a thick aerosol layer is discernible.An analysis of ADEP indicates that this layer contains Saharan mineral dust which is consistent with the origin of the air masses in North Africa.
Furthermore, in the region between the two clouds (14:34-14:43 UTC) gravity lee waves are discernible at an altitude of about 9500 m and also in the lower aerosol layer.These waves are expected to influence at least parts of the northern cirrus cloud.We will investigate them in more detail in Sect.4.3.

Classifying evolution stages
In the following we will apply our classification scheme to the high cirrus cloud north of the Pyrenees.Relative humidity with respect to ice (Fig. 4 b) is calculated from this data using the ECMWF model temperature field.
As expected, supersaturated regions (blue) are found mostly inside of the cirrus.There are also major subsaturated regions (red) in the northern part of the cloud.South of the cirrus high supersaturations exist in cloud-free air, mostly in the crests of the gravity waves (14:34-14:36 UTC).The highest supersaturations are measured in the most southern part of the cloud (14:33-14:34 UTC).They indicate a nucleation region.
To investigate individual parts of the cloud in more detail, we apply our classification and visualize the result in Fig. 5. Data pixels are classified (Sect.3.2) and marked in color accordingly and in-cloud and cloud-free regions can be distinguished by the black contour line for a BSR value of 2. Heterogeneous freezing is identified using the RH M D i,HET (T ) threshold.Subsaturated regions outside of the cloud are left blank and areas where no valid data is available are indicated by black hatching.
The above mentioned humid layer, discernible in Fig. 4, reaches ice supersaturation (ISSR) in the two crests of the gravity lee wave to the south of the cloud (14:34-14:36 UTC).Here, values of RH i are even higher than the threshold for HET freezing.At the cloud edge, also the HOM freezing threshold is surpassed (14:33-14:34 UTC).The southern section of the cirrus (14:32-14:34 UTC) is dominated by ice nucleation and represents the youngest part of the cloud.
In the middle (14:26-14:32 UTC), a section of moderate supersaturation (DEP) is located.This is an already well developed part of the cirrus that is dominated by depositional growth of ice crystals.After an initial ascent (14:32-14:34 UTC), the cloud top level slopes from over 10 km down to under 9 km at the northern edge.This indicates a large-scale descent reducing supersaturation and evoking the intermediate DEP region as well as large connected regions of subsaturation (SUB) in the northern part of the cloud (14:19-14:26 UTC).Here the cloud is starting to break up, as ice particles are sublimating.
From these results all cirrus life-cycle stages can be identified: from ice nucleation (HET, HOM) aided by vertical displacements in a gravity lee wave, to crystal growth by deposition of water vapor in a moderately supersaturated region DEP, to the dissolving of the cloud in a subsaturated region (SUB), probably caused by a large-scale descent.
The detailed distribution of these major stages of cirrus evolution features a horizontal order instead of a general vertical structure found in cirrus clouds over France (Comstock et al., 2004).Surprisingly we even find SUB regions at the cloud top level located above DEP regions in the northern part of the cloud (Fig. 5).Similarly, model simulations, investigating the influence of dynamics on the evolution of a cirrus cloud (Spichtinger and Gierens, 2009), also found more complex horizontal distributions deviating from a simplistic cirrus evolution pattern comprising ice nucleation at cloud top level, a crystal growth in the middle an sublimation at the bottom.Thus our classification illustrates how the large-scale meteorological context, wind and gravity wave fields can effect the structure of individual clouds.Atmos.Meas. Tech. Discuss., doi:10.5194/amt-2016-332, 2016 Manuscript under review for journal Atmos.Meas.Tech.Published: 24 October 2016 c Author(s) 2016.CC-BY 3.0 License.

Investigating the influence of lee waves
A special feature of this case study is the presence of lee wave patterns in the cloud region.Fig. 6 gives a close-up view of BSR data in the cloud-free area south to the cirrus cloud.A layer of slightly higher BSR (> 1.2) is located above an altitude of 9500 m.It shows clear oscillations at its boundary to a lower, cleaner layer of air.One period extends over about 66 seconds in measurement time which corresponds to an apparent wavelength of 14 km with vertical displacements of up to 190 m.ECMWF model data, available for this cirrus case, features a horizontal grid spacing of about 16 km and thus is not able to resolve the small-scale lee waves in its temperature gradients.As a result, temperatures in the crests might be even lower and RH i values therefore underestimated.
To investigate this possible deviation and its influence on our classification results, we simulate adiabatic cooling of an air parcel along a hypothesized trajectory (Fig. 5, blue line) in front of the cirrus cloud.The trajectory runs 200 m under and parallel to the contour line of BSR = 1.2 (not shown), that separates the two distinct layers of air.The wind direction in this region differs by less than 10°from the flight path.This makes us confident that the simulated trajectory resembles a real trajectory reasonably well, under the assumption of a stationary air flow.SEVIRI images from 14:00 UTC and 15:00 UTC (not shown) indicate, that the northern and southern edges of the cloud are moving only about 20 km to the north along the flight path.That corresponds to a cloud velocity of under 6 ms −1 , compared to wind speeds of up to 35 ms −1 at cirrus altitude.As in a lee wave cloud (Field et al., 2012), air is flowing through the region, becoming part of the cloud in the south and leaving the cloud in the north.In the confined area and time frame of our simulation, we consider the underlying wind and wave fields to be quasi-stationary.However this might certainly not be true for the duration of the whole flight leg (14:18 -14:41 UTC).Also our simulation is not intended to provide corrected temperature data but to illustrate the general influence of gravity waves on cloud formation and of unresolved temperature fluctuations on our classification.
In Fig. 7, ECMWF temperature and relative humidity calculated with ECMWF temperature along the trajectory are plotted (blue) as a function of measurement time.As the trajectory follows the vertical displacements of the gravity wave along the wind direction, i. e. from right to left in Fig. 3 to 7, relative humidity and ECMWF temperature show oscillations.They stem from the undisturbed temperature gradient, as ECMWF does not resolve the small-scale lee waves.Besides clear oscillations, a development towards higher RH i and lower temperature, approaching the cloud, is discernible.From 14:36:20 UTC on, RH i shows supersaturation and reaches values of 120 % and 130 % in the following two crests, surpassing the HET threshold.Now we start a parcel at the beginning of the trajectory (measurement time: 14:40 UTC) initialized with the ECMWF temperature at this point.As it follows the trajectory, the temperature is calculated from its vertical displacement using the dry adiabatic temperature gradient.The simulated temperature and relative humidity is plotted in green.Compared to ECMWF, the temperature in the last two crests south of the cloud edge is more than 0.5 K lower and values of RH i are higher by 10 %.
The deviations result from the non-adiabatic temperature gradient in the ECMWF data.
These results emphasize the role of lee waves in the most southern part of the studied cirrus cloud.Comparably cooler temperatures due to adiabatic cooling in the wave crests facilitate the early nucleation of ice crystals.We find that our classifi-Atmos.Meas. Tech. Discuss., doi:10.5194/amt-2016-332, 2016 Manuscript under review for journal Atmos.Meas.Tech.Published: 24 October 2016 c Author(s) 2016.CC-BY 3.0 License.cation, using original ECMWF data with relatively coarse spatial resolution (horizontal grid spacing: 16 km), is able to reveal the relevant classification features within the gravity wave region.The classification quality will be even better in cases where latest ECMWF data with an improved grid spacing of 9 km, or output from regional models is available.
Overall our classification proved to be applicable in a meteorological context that comprises both small-scale and largescale dynamics.It identifies all relevant stages of cirrus evolution and their detailed spatial distribution and thus, also reveals the influences of gravity waves and large-scale atmospheric motion on the studied cirrus cloud.

Summary and conclusions
We presented a novel cirrus classification scheme capable of identifying all evolution stages of the cirrus life-cycle.It is based on airborne Lidar measurements with high spatial resolution of water vapor, backscatter and aerosol depolarization.This data is 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 (ISSR) as well as regions of homogeneous (HOM out ) and heterogeneous freezing (HET out ) are determined.These indicate favorable areas for cirrus cloud formation.Inside of a cloud, ice nucleation (HET in , HOM in ), depositional growth (DEP) and sublimation regions (SUB) are distinguished.They represent the formation, growing and break up phase 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 small-scale gravity lee waves.Here it revealed a non-standard horizontal order of the aforementioned life-cycle 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, we are investigating in our ongoing research 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 perspective to cirrus evolution.By combining those data sources, the specific optical and microphysical properties of different cirrus stages now can be explored.
Thus we aim to achieve more detailed insights in radiative properties of cirrus clouds under various formation and life-cycle conditions.

in-cloud
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 west of Ireland to the Iberian Peninsula and further to the western part of North Africa (Fig. 2 a).At 300 hPa, high southerly winds with wind speeds up to 35 ms −1 are observed on the leading edge over Southern France and Spain.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 high dust concentrations of Saharan mineral dust were expected.Atmos.Meas.Tech.Discuss., doi:10.5194/amt-2016-332,2016 Manuscript under review for journal Atmos.Meas.Tech.Published: 24 October 2016 c Author(s) 2016.CC-BY 3.0 License.
Fig. 4 and Fig. 5 give a close-up view of the selected data marked with a black rectangle in Fig. 3. Water vapor volume mixing ratio r w measured by WALES is plotted in Fig. 4 a.A black contour line (BSR= 2) marks the cloud border.Being an absolute humidity measure, 6 Atmos.Meas.Tech.Discuss., doi:10.5194/amt-2016-332,2016 Manuscript under review for journal Atmos.Meas.Tech.Published: 24 October 2016 c Author(s) 2016.CC-BY 3.0 License.rw generally decreases with increasing altitude, as temperature is decreasing and almost all sources of water vapor are located at the Earth's surface.Contrastingly, a humid layer, surrounded by dryer air at a height of approximately 9000 m, can be found upstream of the cirrus cloud (14:34 -14:40 UTC).In this region, the water vapor data exhibits the same oscillations as previously seen in the BSR data.

Figure 1 .Figure 2 .
Figure 1.Cirrus life-cycle classification scheme based on WALES backscatter ratio (BSR) and relative humidity (RHi) derived from WALES humidity and ECMWF temperature field (description see text).

Figure 3 .
Figure 3. Backscatter ratio (BSR) at 532 nm measured along the flight path (white line in Fig. 2).Hatched areas indicate data that was excluded due to detector saturation or low signal to noise ratio and the terrain profile is shown in dark gray.The black rectangle marks the cirrus region that is studied further (see Fig. 4, 5).

Figure 4 .Figure 5 .
Figure 4. Humidity data of cirrus marked in Fig. 3: (a) Water vapor mixing ratio rw as measured with WALES.(b) Relative humidity with respect to ice RHi derived from WALES data and ECMWF temperature field.The cirrus is outlined by a black contour line (BSR=2) and invalid data is marked by black hatching.

Figure 6 .
Figure 6.Close-up view of BSR data in the lee wave region south to the cirrus cloud.

Figure 7 .
Figure 7. Simulation of temperature T and relative humidity RHi. Green lines are simulated values along a derived trajectory (Fig. 5) assuming adiabatic cooling and heating within the gravity wave, respectively.In blue are ECMWF values interpolated to the trajectory location.