Estimation of the importance of natural cloud seeding

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(1).. Master Thesis / Bachelor Thesis. Title of MSc / BSc Thesis Master Thesis. Departement Environmental Systems Science / Earth Sciences, ETH Zürich Institute for Atmospheric and Climate Science. Estimation of the importance of natural cloud seeding. Name Supervisor 1, Institute for Atmospheric and Climate Science, ETH Zürich (main advisor) Name Supervisor 2, Institute for Atmsopheric and Climate Science, ETH Zürich by … Jana Ulrike Proske. Submitted by Supervised by David Neubauer, Zane Dedekind and Ulrike Lohmann. Name of student Atmospheric Student number Physics Group, place and date Institute for Atmospheric and Climate Science,. Department of Environmental Systems Science, ETH Zürich. Zürich, 13 April 2020.

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(3) Über die fachliche Auseinandersetzung mit dem Lerngegenstand hinaus ist die Vermittlung und Einübung wissenschaftlichen Arbeitens wesentlicher Bestandteil der Kursarbeit. Dazu zählt die Erstellung einer Dokumentation, in der die wichtigsten Ergebnisse festgehalten werden. Im Rahmen der Projektarbeiten erfahren die Teilnehmenden eine Einführung in Präsentationstechniken. Durch ergänzende Veranstaltungen wie Vorträge und themenspezifische Exkursionen werden das interdisziplinäre Interesse und Verständnis gefördert. Neben der Kursarbeit wird ein vielfältiges Rahmenprogramm mit Musik, Theater und Sport author angeboten, of the written here enclosed and that I have das von denwork Teilnehmenden eigenverantwortlich gestaltet wird. compiled it. Declaration of Originality. I hereby confirm that I am the sole in my own words. Parts excepted are corrections of form and content by the supervisor. In einer Gemeinschaft von Lehrenden und Lernenden auf Zeit erfahren die Teilnehmenden With my signature I confirm that eine ihrer Leistungsfähigkeit und Lernbereitschaft angemessene intellektuelle. Herausforderung in unterschiedlichen Wissenschaftsbereichen. Durch die vielschichtige. • I have committed none of the forms of plagiarism described in the ‘Citation etiquette’ information Auseinandersetzung mit dem Themenkomplex Klimawandel regt die JGW-NachhaltigkeitsAkademie zur Reflexion über eigene und fremde Verhaltensweisen an und motiviert zu sheet. eigenem gesellschaftlichem Engagement im Umweltbereich. In intensiver fachlicher Arbeit und motivierten Jugendlichen und in unmittelbarem Austausch mit den Kursleiterinnen und Kursleitern erweitern die Jugendlichen die eigenen Kenntnisse und Fähigkeiten und entwickeln ihre Persönlichkeit fort. Zugleich lernen sie verschiedene Arbeitsformen der Hochschulausbildung kennen, erhalten eine Orientierung auf mögliche spätere Studienfächer und machen neue fachliche wie soziale Erfahrungen.. gleichaltrigen, ähnlich befähigten • I have documented all methods, datamitand processes truthfully.. • I have not manipulated any data.. • I have mentioned all persons who were significant facilitators of the work. Zum Kurs. • I am aware that the work may be screened electronically for plagiarism. Milan Cronen hat am Kurs „Mit Daten und Modellen. Zürich, 13.04.2020. das Erdsystem erforschen“ teilgenommen. Im Kurs erarbeiteten sich die Teilnehmenden zunächst einen Überblick über die klimatischen, biogeochemischen und ökologischen Zusammenhänge im Erdsystem und mögliche Auswirkungen der Umweltveränderungen im 21. Jahrhundert. Es wurden modellund daten-getriebene Forschungsansätze anhand von Statistik und Differentialgleichungen diskutiert und am Computer in der Programmiersprache R umgesetzt, um ein besseres Verständnis der Analyse und Interpretation von Erdbeobachtungsdaten zu erhalten.. Lars Kaiser. ————————————— Ulrike Proske Signature. Moritz Zeising. (Akademieleitung der JGW-NachhaltigkeitsAkademie 2017). 2.

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(5) Abstract Clouds and their feedbacks represent one of the largest uncertainties in climate projections. As the ice phase influences many key cloud properties and their lifetime, its formation needs to be better understood in order to improve climate and weather prediction models. Natural cloud seeding can trigger glaciation in clouds. Via the seeder-feeder mechanism, it has been shown to enhance precipitation formation. However, up to date an estimate of its occurrence frequency is lacking. In this study, we estimate the occurrence frequency of the seeder-feeder mechanism over Switzerland, and investigate its impact in a modelling case study. We use the DARDAR satellite product to estimate the occurrence frequency of multi-layer cloud situations, where an ice cloud can provide seeds to a lower lying feeder cloud. These situations are found to occur in 33 % of the observations. 15 % of observations have an ice cloud above another cloud, separated, while 18 % show an ice cloud connected to a lower cloud, with a potential for in-cloud seeding. Cloud distances are distributed uniformly between 10 m and 10 km. They are found to not vary with topography. Seasonally, winter nights have the most multi-layer cloud occurrences, in 40 % of the measurements. Additionally, in situ and liquid origin cirrus cloud size modes can be identified according to the ice crystal mean effective radius in the DARDAR data. Using sublimation calculations we show that in a significant number of cases the seeding ice crystals do not sublimate before reaching the lower lying feeder cloud. Depending on whether plate like or spherical crystals were assumed, 10 % to 20 % of crystals, respectively, survived a fall of 2 km. We investigate the representation of the seeder-feeder mechanism with a case study in the weather and climate model COSMO. In sensitivity simulations we inhibit the seeder-feeder process by setting sedimenting ice fluxes outside of clouds to 0. At altitudes below 7 km, glaciation is inhibited due to the missing seeder-feeder effect, which leads to decreased ice and snow concentrations. Also a delay of precipitation formation is observed. At higher altitudes, an increase in temperature due to the removal of sublimating ice crystals leads to increased updrafts and increased ice crystal formation. However, this is not an effect from the inhibited cloud seeding specifically, but presents a confounding influence from the removal of ice crystals. In conclusion, the high occurrence frequency of seeding situations and the survival of the ice crystals indicate that the seeder-feeder process and natural cloud seeding are widespread phenomena over Switzerland. However, further investigations of the magnitude of the seeding ice crystals’ effect on lower lying clouds and precipitation are necessary to estimate the impact of natural cloud seeding..

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(7) Acknowledgements Working on this Master thesis has been a great experience, which allowed me to grow scientifically and personally. In addition to the supportive atmosphere at IAC there are some people I need to thank in particular for making this such an enriching and pleasurable experience. David Neubauer has been an awesome supervisor. Most of all I treasure his critical thinking and the fact that he was always on top of the project. Talking to him sometimes felt like talking to an extended brain, with ideas and arguments bouncing back and forth so smoothly. His in-depth feedback and ideas have been invaluable, as well as the fact that he is always quick to reply. I want to thank him for pushing me and the analysis further and for stretching my capabilities. With Zane Dedekind I had a second supervisor who always encouraged me, believed in me and raised my confidence. He introduced me to COSMO very patiently and took all the hurdles with me. I thank him for being so very much approachable and for always having an open ear for me. This project and my motivation throughout benefited a lot from Ulrike Lohmann, her honesty, positive spirit and passion for science, as she always managed to reignite my excitement about the topic. The discussions with all of my supervisors were always stimulating and encouraged me to think further. My scientific understanding and confidence grew a lot during these meetings. I very much value all of their forward looking attitude and the fact that I always came out of our meetings with new ideas and plans. I am fortunate to have been able to build upon preliminary work by Verena Bessenbacher. She gladly changed her DARDAR analysis code to fit my purposes and helped me get it to work. I thank her for being always approachable and ready to help hands-on, and also for showing continuing interest in this project. My thank also goes to Lukas Jansing and Michael Sprenger for patiently introducing me to LAGRANTO. Lastly, I want to thank the Atmospheric Physics group for the inclusive atmosphere and the encouragement, and my fellow Master students and friends for their support and for the nice time along the way.. All simulations within this study were performed with the Consortium for Small-scale Modeling (COSMO). The simulations were performed and are stored at the Swiss National Supercomputing Center (CSCS)..

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(9) Contents 1 Introduction and background 1.1 Ice formation in clouds and the seeder-feeder mechanism . . . . . . . . . . . . . . . . . . . 1.2 1.3. Satellite data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 2 Methods and data. 1 1 5 5 7. 2.1. Satellite data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Analysis method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7 8. 2.2 2.3. Ice crystal sublimation calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modelling with COSMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Trajectory calculations using LAGRANTO . . . . . . . . . . . . . . . . . . . . . .. 9 13 14. 2.3.2. 14. Sensitivity study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3 Results and discussion 3.1 3.2. 16. DARDAR data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Case study simulations with COSMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Comparison between model results and satellite data . . . . . . . . . . . . . . . . .. 16 28 28. 3.2.2 3.2.3. 29 32. Trajectory analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4 Summary and conclusions. 41. Appendix A A.1 Satellite observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 44 44. A.2 Sublimation calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.3 Modelling with COSMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 47 49. A.3.1 Trajectory analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.3.2 Sensitivity simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 49 49. Bibliography. 51.

(10) List of Figures 1.1. Schematic illustration of the ‘classical’ seeder-feeder mechanism . . . . . . . . . . . . . . .. 1. 1.2. Schematic illustration of the seeder-feeder mechanism as it is understood within this study. 2. 1.3. Fall distance of a spherical ice particle (Hall and Pruppacher, 1976) . . . . . . . . . . . . .. 3. 2.1. Geographical distribution of satellite tracks . . . . . . . . . . . . . . . . . . . . . . . . . .. 8. 2.2. Vertical and latitudinal cross section of the cloud cover within the COSMO simulation on 18.05.2016 at 12 am . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14. Average distribution of distances between the base of an ice cloud layer and the next liquid cloud layer top below . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 17. 3.2. Sketch of the two seeder-feeder situations observed in this study. . . . . . . . . . . . . . .. 18. 3.3. Geographical distribution of distances smaller and larger than 100 m . . . . . . . . . . . .. 19. 3.4. Distribution of measured distances separated by topography . . . . . . . . . . . . . . . . .. 20. 3.5. Cloud cover difference between winter and summer derived from CALIPSO data . . . . .. 21. 3.6. Distribution of the mean effective ice crystal radii at the lowest ice cloud bases . . . . . .. 23. 3.7. Possible seeder-feeder situations plotted as a fraction of cases where the ice crystal reaches the highest liquid cloud top . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 25. 3.1. 3.8. Absolute frequency of ice cloud base temperature as a function of relative humidity with respect to water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 26. Absolute frequency of effective ice crystal radius at ice cloud base as a function of ice cloud height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 27. 3.10 Direct comparison between simulation ctrl02 and the satellite data . . . . . . . . . . . . .. 29. 3.9. 3.11 Position of the trajectories started from feeder cloud tops within seeder-feeder situations at 12 am on 18.05.2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 30. 3.12 Comparison between peaks of cloud ice content and the possibility of seeding events . . .. 31. 3.13 Hydrometeor mass concentrations in the control simulations . . . . . . . . . . . . . . . . .. 32. 3.14 Difference between the sensitivity simulations and the ensemble mean in the precipitating flux of ice crystals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 33. 3.15 Difference between the sensitivity simulations and the ensemble mean in hydrometeor mass concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 35. 3.16 Cross section of cloud droplet mass concentration, and in-cloud cloud droplet mass concentration divided by the cloud area fraction . . . . . . . . . . . . . . . . . . . . . . . . .. 36. 3.17 Cross section of ice crystal mass and number concentration, temperature and relative humidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 38. 3.18 Time series of the vertical distribution of the temperature increment due to latent heat and of the vertical wind velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 39.

(11) 3.19 Vertical distribution of ice crystal mass and number concentrations in the control and sensitivity simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 40. A.1 Comparison between cloud cover data derived from the DARDAR satellite product in this study and CALIPSO-GOCCP cloud cover data . . . . . . . . . . . . . . . . . . . . . . . .. 44. A.2 Frequency distribution of ice cloud base height as a function of the distance to the next liquid cloud top . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 45. A.3 Distribution of ice crystal sizes at the lowest ice cloud base . . . . . . . . . . . . . . . . . A.4 Frequency of effective ice crystal radii at ice cloud base height as a function of the distance to the next lower lying liquid cloud top . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.5 Frequency distribution of ice cloud base temperatures as a function of their distance to the next lower lying liquid cloud top . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.6 Frequency distribution of ice cloud base temperatures as a function of the ice cloud base height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.7 Absolute frequency of ice cloud base temperature as a function of relative humidity with. 45 46 46 47. respect to water, assuming plate like ice crystals . . . . . . . . . . . . . . . . . . . . . . . A.8 Absolute frequency of effective ice crystal radius at ice cloud base as a function of ice cloud. 47. height, assuming plate like ice crystals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.9 Time evolution of altitude and cloud cover for the backward and forward trajectories . . . A.10 Difference between the sensitivity simulations and the ensemble mean in ice crystal and. 48 49. rain droplet number concentrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.11 Precipitation amount in the control and sensitivity simulations . . . . . . . . . . . . . . .. 49 50. List of Tables 2.1. Variables used in the sublimation height calculation . . . . . . . . . . . . . . . . . . . . .. 10. 2.2 2.3 2.4. Constants used in the sublimation calculations for a sphere . . . . . . . . . . . . . . . . . Equations used in the sublimation calculations for a hexagonal plate . . . . . . . . . . . . Constants used in the sublimation calculations for a hexagonal plate . . . . . . . . . . . .. 11 11 11. 3.1 3.2. Distribution of distances between ice cloud base and liquid cloud top by topography . . . Climatology of the ice-liquid cloud distances . . . . . . . . . . . . . . . . . . . . . . . . . .. 19 21.

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(13) AR. AR233-EA33-21.tex. XMLPublishSM (2004/02/24). P1: JRX. Chapter 1. Introduction and background OROGRAPHIC PRECIPITATION. 6. Clouds are a major component of the Earth atmosphere system, influencing its radiative balance and the water cycle. At the same time, clouds and cloud feedbacks contribute the largest uncertainty to projections of climate sensitivity in global climate models (Cess et al., 1990; Soden and Held, 2006; Williams and Tselioudis, 2007). They must therefore be better understood in order to assess climate change and improve projections (Boucher et al., 2013). Cloud microphysics, and especially their ice content, determine key cloud properties, such as their albedo and lifetime. The representation of the ice phase in clouds is therefore necessary to estimate the Earth’s radiation budget and its response to climate change (Sun and Shine, 1995; Matus and L’Ecuyer, 2017). Furthermore, most precipitation globally originates from the ice phase. Therefore, its formation needs to be understood in order to improve forecasts of precipitation in numerical weather prediction models and the representation of the water cycle in climate simulations. Natural cloud seeding can trigger the formation of ice within clouds. In particular, the seeder-feeder mechanism can lead to the glaciation of clouds and enhance precipitation. Moreover, it has been associated with the enhancement of extreme precipitation and flooding (Rössler et al., 2014). An understanding of the seeder-feeder mechanism is therefore necessary to improve the representation of the cloud ice phase in weather and climate models, and ultimately to reduce uncertainty in climate simulations.. 1.1. Ice formation in clouds and the seeder-feeder mechanism. The seeder-feeder mechanism was originally proposed to explain an observed enhancement of precipitation over mountains. In this classical setting, precipitation from an overlying “seeder” cloud falls into an orographic “feeder” cloud. Here, the hydrometeors feed on available moisture and grow by accretion, coalescence, or riming, which leads to an enhancement of precipitation over the orography (Roe, 2005). A schematic of the process is displayed in Figure 1.1. This classical seeder-feeder mechanism, involving orography and liquid precipitation particles, has been observed in field studies in locations as different as Southern Wales (Hill et al., 2007), Poland (Dore et al., 1999) and New Zealand (Purdy et al., 2005). The enhancement of precipitation has been investigated in a number of idealized modelling studies (e.g. Carruthers and Choularton (1983) and Robichaud and Austin (1988)). In a widened sense, the process. Figure 1.1: Schematic illustration of the ‘classical’ seeder-feeder mechanism: the precipitation that originates from an overlying ice cloud is enhanced by passage through a lower-lying orographic ‘feeder’ cloud (Roe, 2005).. gure 6 Schematic illustrations of different mechanisms of orographic precipitati stable upslope ascent, (b) partial blocking of the impinging air mass, (c) dow ley flow induced by evaporative cooling, (d) lee-side convergence, (e) convect.

(14) 1.1. Ice formation in clouds and the seeder-feeder mechanism. Figure 1.2: Schematic illustration of the seeder-feeder mechanism as it is understood within this study: ice particles fall out of an ice cloud into a lower-lying cloud where they act as seeds for ice formation and feed on its hydrometeors and moisture, thereby enhancing precipitation or initiating the glaciation of the lower cloud.. of falling precipitation particles that feed on the moisture and hydrometeors in a lower part within the same cloud can also be understood as a seeder-feeder process (in-cloud seeder-feeder mechanism, Hobbs et al. (1980)). Braham (1967) noted the possibility of ice crystals from cirrus clouds acting as seeds for ice formation in lower-lying warmer clouds. In this special case of the seeder-feeder mechanism, the seeding precipitation is specified as ice, but the presence of orography is not a prerequisite for the mechanism’s occurrence. This natural cloud seeding is depicted in Figure 1.2. It is the focus of this study, where hereafter the seeder-feeder mechanism refers to ice particles falling from an ice cloud into a lower-lying cloud or a lower-lying part of the same cloud, which is either liquid, ice or mixed-phase. Pure ice clouds, which act as seeder clouds in this study, can form either from a lifting of liquid droplets or directly from water vapour. Recent studies have suggested to classify cirrus clouds accordingly, as liquid or in situ origin ice clouds (Luebke et al., 2013; Luebke et al., 2016; Krämer et al., 2016; Gasparini et al., 2018; Wolf et al., 2019). This is a useful classification also because the formation mechanism has been shown to influence clouds’ microphysical properties (Luebke et al., 2016; Wolf et al., 2019). In order to understand why seeding ice crystals can have a large influence on cloud properties, one needs to recapitulate the process of ice nucleation within clouds. In the atmosphere, ice can nucleate homogeneously from cloud droplets only at temperatures colder than −38 ◦C (Kanji et al., 2017). At warmer temperatures, ice can only be formed via heterogeneous nucleation on ice nucleating particles that act as nuclei. Once ice particles are formed, they grow by riming or vapour deposition. This latter growth can be rapid in the Bergeron-Findeisen process, where the water saturation is between saturation with respect to water and ice and where therefore the ice crystals grow at the expense of liquid droplets (Malberg, 2007). Furthermore, the ice crystals can multiply through secondary ice production, in processes such as the ejection of small ice splinters in the Hallett-Mossop process, between −3 and −8 ◦C (Mossop et al., 1974), frozen droplet shattering, and ice-ice collisional breakup (Sullivan et al., 2018). Therefore, a mixed-phase cloud is not stable and will glaciate and/or form precipitation. In the absence of ice nuclei or temperatures low enough for homogeneous ice nucleation to occur, ice particles entering a supercooled liquid cloud from above or below act in a similar manner, reducing the supercooled liquid fraction and ultimately glaciating the cloud and/or enhancing precipitation. This is what happens in the seeder-feeder mechanism: ice crystals from an ice cloud fall into a lower cloud, where they feed on water vapour and rime with supercooled water droplets, thus leading to the glaciation of the cloud and/or an enhancement of precipitation formation. Because of the aforementioned enhancement processes in the ice phase, the seeder-feeder mechanism with seeding ice crystals is more efficient than the classical liquid seeder-feeder mechanism, and has been found to lead to a larger precipitation enhancement (Choularton and Perry, 1986). In the seeder-feeder mechanism ice crystals must fall through a subsaturated layer between the ice 2.

(15) Chapter 1. Introduction and background. Figure 1.3: Fall distance of a spherical ice particle of radius 160 µm and density 0.7 × 103 kg/m3 , computed for different relative humidities and evaporation coefficients (relative humidity with respect to ice from (1) to (4): 30%, 50%, 70%, 90% with evaporation coefficient β = 0.1; and evaporation coefficient β = 0.01 (c), 0.03 (b), 1.0 (a); NACA Standard Atmosphere) (Hall and Pruppacher, 1976). A prerequisite for the seeder-feeder mechanism is that ice crystals survive the distance between the ‘seeder’ and the ‘feeder’ cloud.. and the lower cloud layer. Naturally, they will partly sublimate and shrink. Ultimately, this leads to the full sublimation of the ice crystal if it does not reach the supersaturated lower cloud layer in time. For natural cloud seeding to take place, the survival of the ice crystals during the fall into the lower cloud layer is crucial. Braham (1967) observed a spectacular case of ice crystals that survived a distance of 5 km in clean air. This demonstrated the feasibility of ice crystal survival for natural cloud seeding (Hitschfeld, 1968; Locatelli et al., 1983). Hall and Pruppacher (1976) were the first to conduct a theoretical study of the fall distances of ice crystals. They found that “ice particles could survive distances of up to 2 km when the relative humidity with respect to ice was below 70%” (see Figure 1.3). Since they assumed a standard atmospheric profile and constant relative humidity, their results are more illustrative than a realistic representation of an ice crystal falling between two cloud layers. In a direct comparison to observations with humidity profiles from surrounding weather stations, they could not reproduce the spectacular observations from Braham (1967), but rather found the ice crystals surviving only a distance of 1.5 km. Following Hall and Pruppacher (1976) and other theoretical and observational studies, Rogers and Vali (1978) concluded that ice crystals could survive long fall distances between clouds and therefore attested the potential of the seeder-feeder mechanism to significantly contribute to the ice content in clouds. Recently, Marcolli (2017) noted in a review article of pre-activation that the preservation of ice within particle pores after full sublimation of the macroscopic ice crystal offers the possibility for seeding through pre-activated particles. Hence, the remnants of sublimated ice crystals could still act as seeds for ice formation within the seeder-feeder mechanism. Observations and importance of the seeder-feeder mechanism Natural cloud seeding by ice crystals falling from a higher ice cloud into a lower cloud has been observed in various field studies, starting in the 1950s. Some of these studies report an impact of the seeder-feeder mechanism, mostly in terms of precipitation enhancement. In the following, an overview of selected studies is given. As a first step, Dennis (1954) observed snow trails around the top of showers, associating them with the production of precipitation. Following visual observations of ice crystal tails that reached lower-lying cumulus clouds and initiated storm development by Braham (1967), the seeder-feeder process was increasingly observed in remote sensing and aircraft campaigns. Hobbs et al. (1980) observed ice 3.

(16) 1.1. Ice formation in clouds and the seeder-feeder mechanism. crystals falling from shallow convective cells, growing and increasing precipitation in cold- and warmfrontal rainbands. They combined particle measurements with radar reflectivities to find that, in their case, about 20 % of the precipitation mass originated in the seeder and about 80 % in the feeder zone. The authors inferred an enhancement of precipitation through the seeder-feeder mechanism. Similarly, Locatelli et al. (1983) observed a seeder-feeder process in Washington, in the United Sates, and found that the seeding precipitation rate of ≈ 0.01 mm/h was intensified by 0.01 to 0.07 mm/h through riming in the feeder cloud. They also observed that ice crystals that had fallen into the feeder cloud grew by vapour deposition, explaining enhanced ice needle concentrations in the stratocumulus feeder clouds. In a study of artificial seeding with dry ice, Hobbs et al. (1981) achieved a direct view of the effect of the seeding ice crystals onto cloud particle size spectra. They found that the seeded cloud contained ice particles between 200 µm and 1 mm in size, which the comparable unseeded cloud sections did not contain. About 40 min after seeding with dry ice at a rate of 0.05 kg/km, the concentrations of small ice particles decreased while the concentration of large particles did not. The authors attributed this to the collection of the small by the large particles, since the latter were observed to be aggregates or graupel-like. By seeding with larger rates of 0.1 kg/km, they even observed precipitation reaching the ground about 40 min after seeding. They thereby confirmed that seeding ice crystals affect the cloud drop size spectra and enhance precipitation formation. More recently, airborne and remote sensing studies have reported seeder-feeder cases in Morocco (Ansmann et al., 2008), in Cape Verde (Ansmann et al., 2009), over the Great Plains of the US (Fleishauer et al., 2002), the Sierra Nevada (Creamean et al., 2013), the Beaufort Sea (Pinto et al., 2001) and the Arctic (Hobbs et al., 2001). Evidence of the impact of the seeder-feeder process in these studies is mixed: Ansmann et al. (2008) reported that the ice virga “caused strong ice production [in the lower clouds] at temperatures as high as −12 to −15 ◦C”. On the other hand, Fleishauer et al. (2002) found evidence that the seeder-feeder effect in their observations was weak. Modelling studies of the seeder-feeder mechanism In addition to field observations, the seederfeeder process has also been studied in mostly idealized model simulations. A selection of these studies is presented in the following. In a case study, Fernández-González et al. (2015) simulated an observed seeder-feeder episode in the Guadarrama Mountains on the Iberian Peninsula. They found that when a seeder cloud formed within the model, the seeding ice crystal collected supercooled cloud droplets and thereby prevented the formation of freezing drizzle, instead leading to moderate snowfall. Rutledge and Hobbs (1983) simulated warm-frontal lifting. Seeding ice crystals efficiently removed moisture from the feeder cloud, leading to its glaciation. The seeding ice crystals themselves made up 25 % of the total mass of precipitation, while the mass added to them in the feeder cloud made up 75 %. In the absence of the seeder-feeder process, non-precipitating cloud droplets formed. Most recently, Chen et al. (2020) used idealized model simulations to discern the effects of both downwelling longwave radiation and natural ice seeding from an upper cloud onto a lower-lying supercooled liquid cloud. The lower cloud dissipated, both because of the latent heat release caused by the seeded glaciation, and because of the reduction of the cloud top radiative cooling rate through the downwelling longwave radiation from the cloud above. These observational and model studies highlight that indeed the seeder-feeder process can influence the microphysical properties of lower-lying feeder clouds, leading to glaciation and/or enhanced precipitation. However, it is difficult to draw conclusions in terms of the overall importance of the mechanism from single case studies and idealized simulations without an estimate of the seeder-feeder process occurrence frequency. Frequency of the seeder-feeder process Dietlicher et al. (2019) took a first step towards such an estimate of the importance of natural cloud seeding. Instead of focusing on the interaction between the two cloud layers, they investigated natural cloud seeding as a pathway of ice formation. They added tracers for the formation of ice crystals into ECHAM6-HAM2, a general circulation model. In this model, they found that ice sedimentation from cirrus clouds was the dominant source of ice in the mixed-phase temperature regime, between −35 ◦C and 0 ◦C. This implies that in this climate model the seeder-feeder mechanism is of major importance for ice formation in mixed-phase clouds. With a simplistic approach, Seifert et al. (2009) and Ansmann et al. (2009) estimated the occurrence frequency of natural cloud seeding for their lidar field study datasets from Leipzig and Cape Verde. They simply excluded all mixed-phase clouds that had an ice cloud within 2 km above cloud top as a seeded ice cloud. In Cape Verde, about 20 % of observations at −20 ◦C contained such seeded clouds, while only about 25 % of observations at −20 ◦C contained clouds at all. In Leipzig, about 10 % of ice-containing clouds at −20 ◦C were marked as seeded (ice containing clouds made up 90 % of observations at that 4.

(17) Chapter 1. Introduction and background. temperature). This method is quite simplistic, because it does not take into account the survival of ice crystals explicitly. A more thorough, regional estimate of seeder-feeder occurrence frequency in the Arctic was derived by Vassel et al. (2019a). Using radiosonde and radar data from Svalbard, they deduced the frequency of multi-layered clouds as 29 %. Furthermore, they calculated the sublimation height of ice crystals when falling in between the two cloud layers. Under the assumption that ice crystals were hexagonal plates with a radius of 400 µm, 26 % of observations contained a seeding case. In 1978, Rogers and Vali stated that while natural cloud seeding “itself is now clearly documented, good measures of the importance of the process are not yet available.” Since then, field studies and model studies have added more documentation and process understanding. Recent studies have begun to elucidate the frequency and thereby the importance of the phenomenon regionally and in GCMs, but a thorough global estimate is still lacking. With global coverage and sensors increasingly capable of resolving clouds and their vertical distribution, satellite data offers an opportunity to fill the gap from single observations to whole-earth long-time observations to derive such a frequency estimate.. 1.2. Satellite data. Since 2006, two satellites together offer an unprecedented view of the vertical distribution of clouds: CloudSat and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations). CloudSat boards a 94 GHz radar that coincidently observes most of cloud condensate and precipitation in vertical profiles with a vertical resolution of 500 m (Stephens et al., 2002). The lidar on CALIPSO is able to identify the thin upper layers of cirrus clouds that the radar misses (Winker et al., 2010), while the radar is able to look through thick clouds where the lidar beam is attenuated. The two satellites are flown in close formation within the A-train satellite constellation (Stephens et al., 2008). They are designed for their data to be combined, as it is for example in the DARDAR (radar lidar) satellite product employed within this study (Delanoë and Hogan, 2008; Delanoë and Hogan, 2010; Ceccaldi et al., 2013). The over 10 years of global profile data derived from both satellites have “profoundly changed our perspectives on the atmosphere in general” (Stephens et al., 2018) and offered new insights into the global vertical distribution of clouds. An overview of the mission success can be found for example in Witkowski et al. (2018), and more detailed information on the satellites and the data used in this study is given in section 2.1. In the following, a small selection of studies using CloudSat and CALIPSO data is given to illustrate their potential. The global and long-term view of satellites onto the Earth atmosphere system yields data ideally suited for climatological studies. Hence, for example Gao et al. (2014) have studied the occurrence frequency as well as the microphysical properties of single-layer warm clouds over the Northern Hemisphere using CloudSat products. Similarly, Hong and Liu (2015) have investigated global ice cloud properties, resulting for example in a latitude-height climatology of effective ice crystal radii. More specifically, Naud et al. (2015) focused on extratropical cyclone cold fronts in CloudSat and CALIPSO data to derive a distribution of cloud and precipitation properties therein. For the seeder-feeder mechanism, multi-layered clouds are a prerequisite, which can also be investigated using CloudSat and CALIPSO data. Das et al. (2017) examined the distribution of multi-layered clouds over five monsoon regions in the Northern Hemisphere. They found the overall cloud occurrence frequency to be similar, but for example single layered clouds were found more often in the East Asian monsoon, and multiple layer clouds were more frequent in the Indian monsoon. A global view on the occurrence of multi-layered clouds is derived in Mace et al. (2009). They showed that the occurrence of multi-layered clouds differs strongly with latitude and geography. For example, mid-level clouds cooccuring with cirrus are the dominant multi-layer clouds over the continents. Such estimates of global multi-layer cloud occurrence, as given in Mace et al. (2009), Wang et al. (2000), and Matus and L’Ecuyer (2017), are a first step at estimating the occurrence frequency of the seeder-feeder mechanism, as multilayered ice over mixed-phase clouds are one prerequisite. However, to give an estimate of actual seederfeeder cases, sublimation calculations need to be combined with the seeder-feeder situation/multi-layer cloud occurrence frequencies as done by Vassel et al. (2019a).. 1.3. Modelling. One motivation for the study of the seeder-feeder mechanism is an improved representation of the ice phase in numerical weather prediction and climate models. Hence, it is intriguing to see how these models currently represent the seeder-feeder mechanism. In order to represent the seeder-feeder mechanism, a 5.

(18) 1.3. Modelling. model at least needs to contain ice crystals, allow them to sediment, grow and shrink by diffusion and to interact with cloud droplets through riming. The two-moment microphysics scheme from Seifert and Beheng (2006) contains all of these processes, and is in turn included in the regional weather and climate model Consortium of Small-Scale Modeling (COSMO). COSMO is used for operational weather forecasting for example by Meteo Swiss today, and whether it can represent processes such as the seederfeeder process is therefore relevant. The seeder-feeder process has previously been studied as a test case for the then new COSMO autoconversion scheme (Zängl et al., 2010). Furthermore, Vassel (2018) used COSMO to study another type of interaction between two cloud layers in the Arctic, namely via radiative fluxes. In this study, COSMO is chosen to simulate a seeder-feeder case study in order to test the representation of the seeder-feeder mechanism and its impact in the model. When using model simulations to make weather predictions or climate projections or to investigate the impact of a process, one needs to keep in mind the uncertainty inherent to the dynamical chaos of the atmosphere (compare Wilks (2011, chapter 7)). Because of measurement uncertainty, a model will always start its simulation from a state different from reality. But due to the dynamical chaos, forecasts started from slightly different initial conditions will deviate in their results for the future. Any single model forecast represents merely one possible future state of the atmosphere. In order to sample these possible future states, one can start simulations from a sample of the phase space of the initial conditions. In the present study, this is done to generate an ensemble of control simulations that is compared to the sensitivity simulations.. 6.

(19) Chapter 2. Methods and data This study can be divided into two methodological sections. Firstly, the occurrence frequency of a second cloud layer below an ice cloud layer was estimated using satellite data. It was further evaluated whether ice crystals could survive a fall from the ice layer into the potentially liquid layer, i.e. not sublimate before reaching the lower cloud layer. Secondly, a single case study with a seeder-feeder situation was chosen and simulated with the atmospheric model COSMO. Throughout this study, the programming languages CDO (Schulzweida, 2018) and Python1 were used to handle data and analyse it. The satellite data analysis and the sublimation calculations were conducted with Python as well. Analysis and plotting scripts are archived at https://doi.org/10.5281/zenodo. 3741425, and the generated data is archived at https://doi.org/10.5281/zenodo.3733263.. 2.1. Satellite data. The satellite data product used in this study is based on radar, lidar and infrared radiometer data from the CloudSat and CALIPSO satellites. The satellites were launched jointly on 28 April 2006 into the A-Train or afternoon constellation (Stephens et al., 2002). This constellation has a sun-synchronous orbit at 705 km altitude and an inclination of 98.2°. In February 2018 CloudSat exited the A-Train upon function loss of one of its reaction wheels. It was lowered to an orbit below the A-Train, which CALIPSO joined in September 2018 (Atkinson, 2018). Together, the two satellites offer a combined view of aerosols, clouds and precipitation. CloudSat has cloud profiling radar on-board that can sense cloud particles as well as detect precipitation (Stephens et al., 2008). CALIPSO carries the CALIOP lidar (Cloud-Aerosol Lidar with Orthogonal Polarization) and two passive sensors, a visible camera and a three-channel infrared radiometer. Its sensitivity ranges from molecular and aerosol to cloud backscattering (Winker et al., 2010). Because of their joint operations and almost simultaneous time measurements, the two satellites provide novel ways to look at cloud, precipitation and aerosol profiles (Stephens et al., 2018). From the CloudSat and CALIPSO data, Delanoë and Hogan (2010) developed the DARDAR (radar lidar) satellite product that provides cloud classification and ice cloud properties. It was developed further into a DARDAR v2 by Ceccaldi et al. (2013). By combining data from spaceborne radar and lidar they produce an improved and more robust retrieval product, as one instrument’s weaknesses are compensated for by the other instrument. DARDAR data is retrieved at 60 m vertical resolution up to an altitude of 25 km and a horizontal resolution of 1.4 km (DARDAR-CLOUD Documentation). Next to other cloud properties it contains a classification of the layer at each grid point with categories like clear sky, ice, liquid or supercooled clouds, aerosols, etc. as well as the retrieved effective radius of present ice crystals. In this study, DARDAR v2 data (as described in Ceccaldi et al. (2013)) from April 2006 through October 2017 was used. Due to CloudSat’s battery anomaly there is no data between April 2011 and April 2012 and merely Daylight-Only Operations mode data thereafter (Stephens et al., 2008; CloudSat Radar Status 2008; Witkowski et al., 2018).. 1 Python. Software Foundation, www.python.org.

(20) 2.1. Satellite data. 180 49. 150. 120. 47 46. 90. 45. Counts. Latitude (°N). 48. 60. 44 43. 30 3. 4. 5. 6. 7 8 9 Longitude (°E). 10. 11. 12. 13 0. Figure 2.1: Geographical distribution of satellite tracks: number of tracks through each point within the study domain over the whole time period analysed in this study (2006-2017). The grey rectangle displays the study domain surrounding Switzerland (4 °E to 12 °E and 43.5 °N to 48.5 °N).. 2.1.1. Analysis method. In order to evaluate the frequency of seeder-feeder situations four variables were created: frac_cov (−) The fraction of sky covered with a specific combination of cloudtop and cloudbase temperature. icebase (m) The height (altitude above sea level) of the lowest cloud grid point with T < −35 ◦C (lowest base of an ice cloud). dist (m) The distance between the lowest base of the ice cloud and the highest top of the cloud below (in the following called liquid cloud). reff (µm) The effective radius of ice crystals at the lowest ice cloud base. The first variable serves the assessment of the frequency of cloud situations, while the last three are needed to evaluate the survival of ice crystals in the seeder-feeder process. These four variables were derived from the DARDAR data in the following manner. Firstly, all available data files, which each contain one satellite track, were tested to see whether the track went through the chosen domain surrounding Switzerland (4 °E to 12 °E and 43.5 °N to 48.5 °N). This domain was chosen to contain most of the Alps and also because a COSMO model set up was available for this domain for the second part of the study. Only those files that contained a point within the domain were analysed. Figure 2.1 shows the geographic distribution of all the tracks that go through the chosen domain. Secondly, the following analysis was applied to each of these files. A cloud mask was created to mark the occurrence of a cloud in each grid box. Thereby, the DARDAR categories 1, 2, 3 and 4 (ice, ice + supercooled, liquid warm and supercooled) were all taken together to simply signify the presence of a cloud layer. This cloud mask was found to be quite noisy, with single cloud-free pixels inside a cloud layer and single cloud pixels inside cloud free regions. Therefore, the cloud mask was filtered: for each pixel, the pixels in a 7 × 7 box around it were checked. If the majority was cloudy, the pixel was set to cloudy, and vice versa. For the fraction of sky covered, merely the information from the cloud mask on whether there was a cloud in each grid box was sufficient. Grid boxes where there was a cloud in the grid boxes above but not in those below were identified as cloud bases. Conversely, those grid boxes where there was cloud in the grid boxes below but not in those above were identified as cloud tops. The temperatures in these cloud edge grid boxes were then evaluated. In each vertical column, a number of temperature pairs for cloud top and base are now known. Cloud base and top temperature were used as dimensions, so the 8.

(21) Chapter 2. Methods and data. final variable frac_sky is a function of latitude, longitude, cloud base and cloud top temperature, with a resolution of 1 ◦C. frac_sky was set to 1 for every combination of cloud top and base temperature that was present at any given grid point and 0 for the other temperature pairs. For the remaining three variables the temperatures at each grid point were used for masks: Temperature mask 1 keeps only those points where the temperature was lower than or equal to −35 ◦C. Used for icebase, reff and dist. Temperature mask 2 keeps only those points that have a temperature warmer than −35 ◦C. Used for dist. The temperature to which the cloud temperatures were compared to determine their phase was taken to be −35 ◦C. This is because liquid cloud droplets have been found to supercool to this temperature before freezing homogeneously (Murray et al., 2010; Herbert et al., 2015). The temperature for homogeneous freezing of water droplets is also often given as −38 ◦C (Kanji et al., 2017). However, in tests preceding this study, a threshold of −38 ◦C instead of −35 ◦C proved to have no evident impact on the results. In this study, ice clouds are defined as clouds at temperatures lower than the chosen temperature threshold of −35 ◦C, and (potentially) liquid clouds are defined as all clouds at temperatures warmer than −35 ◦C. With a temperature threshold at the point of homogeneous freezing, clouds that are termed ice clouds in the analysis are most probably really cirrus clouds. However, clouds termed liquid could in principle be in the ice phase in reality, depending on their history and the presence of ice nucleating particles. The derived temperature masks were combined with the cloud mask to keep only those values where there was both a cloud and the right temperature for the different variables present. The combined masks were then used to find the lowest ice cloud base (for icebase, reff and dist) and the highest liquid cloud top (for dist). The altitude and the effective radius in the corresponding grid box were used. Prior to this, the effective ice crystal radius was also filtered. Otherwise there would have been a mismatch between the filtered cloud mask and the unfiltered effective radius, resulting e.g. in the selection of an out-of cloud effective radius for a point that the filtered cloud mask saw as in-cloud. As a filter for the effective radius, the vertical median with an extent of four pixels up and four pixels down from the one in question was applied, using only those pixels where the unfiltered cloud mask saw a cloud. For dist, the altitude of the lowest ice cloud base and the highest ice cloud top were subtracted to create the variable at each grid point of the dataset. Only points along the track within the domain were kept, regridded onto a map of the whole domain. The resolution was decreased to 0.05°×0.05° due to data storage constraints. This was achieved by taking the mean of each variable of all the along-track grid points which fell into each coarser resolution grid point. Variables at grid points where there was an observation but no measured value for this variable, e.g. because there was only one cloud layer present and therefore no distance, were set to a mask value of -999. Values at grid points that were part of the domain but without a satellite overpass were set to NaN. This distinction was necessary to derive the total number of observations later on.. 2.2. Ice crystal sublimation calculations. For the seeder-feeder process, seeding ice crystals need to survive the distance to the lower-lying liquid clouds, hereafter termed ∆zil . To determine whether ice crystals from the ice cloud would survive ∆zil as given by the DARDAR data, the distance until complete sublimation of these ice crystals was calculated. During the fall, the particle shrinks in the subsaturated air. At the same time, its size influences its fall speed. Environmental parameters such as the air density, air temperature and the relative humidity determine the sublimation rate and fall velocity. For these parameters, Hall and Pruppacher (1976) used the NACA standard profile, while Vassel (2018) used mean values and Vassel et al. (2019a) used radiosonde profiles. Since the environmental conditions are primary determinants of the sublimation height, the most detailed information available was chosen. Relative humidity and temperature were therefore taken from ERA5 reanalysis data from the European Centre for Medium Range Weather Forecast (ECMWF). From the DARDAR data the ice cloud height, the distance to the lower-lying liquid cloud and the mean effective radius at the ice cloud base were determined. The ERA5 data was regridded prior to the calculations. Vertically, the ERA5 data on model levels was interpolated to height levels with the DARDAR 60 m resolution. Horizontally, the ERA5 grid latitudes and longitudes closest to the points on the DARDAR grid were chosen. As only hourly ERA5 data was available, data from the hour closest to the entry time of the satellites into the study domain was used. 9.

(22) 2.2. Ice crystal sublimation calculations. Table 2.1: Variables used in the sublimation height calculation. Temperature and relative humidity are read from the ERA5 data whereas all other values are derived from them and the initial conditions, which stem from the DARDAR data. Symbol C Dv e esat,i esat,w G Ls m µ NRe p r RH ρair s T T◦C v z. Long Name capacitance of the ice particle diffusivity of water vapour in air saturation of vapour pressure in air saturation vapour pressure with respect to ice saturation vapour pressure with respect to water growth factor latent heat of sublimation mass of the ice particle dynamic viscosity Reynolds number pressure effective radius of the ice particle relative humidity air density supersaturation with respect to ice temperature temperature fall speed of the ice particle height of the ice particle. Units m m2 /s Pa Pa Pa kg/(m s) J/mol kg kg/(m s) − Pa m % kg/m3 Pa K ◦ C m/s m. The sublimation height was calculated for every point in every available track file where there was at least one ice cloud above a liquid/mixed-phase cloud present. The sublimation algorithm was applied in 0.01 s timesteps (dt). It is based on work in Vassel (2018) and consists of the following set of equations. The script was applied to two different shapes of ice crystals, namely spheres and hexagonal plates. These two were chosen to sample ice crystal properties, e.g. to span the possible range of terminal velocities. In particular, ice crystals have been found to evolve into spherical shape while sublimating (Nelson, 1998), which makes this an ideal shape to use. The equations shown in the text refer to the spherical particle. The variables and constants used are given in tables 2.1 and 2.2, and for the computation for the hexagonal plate in tables 2.3 and 2.4. At each timestep i + 1 the barometric formula was applied to find the pressure corresponding to the height of the ice particle: ( p = p0. Tb Tb + Lb · z[i]. air ) gM RL b. (2.1). The initial height of the ice particle was the height of the ice cloud base. From the pressure, the density of the air surrounding the particle was calculated using the ideal gas law: ρair =. p Rs T. (2.2). With the Magnus formula, the saturation vapour pressure of water with respect to ice and water, respectively, was derived: ) ( 22.46 · TC 2 (2.3) esat,i = 6.112 × 10 hPa · exp 272.62 ◦C + TC ( ) 17.62 · TC esat,w = 6.112 × 102 hPa · exp (2.4) 243.12 ◦C + TC 10.

(23) Chapter 2. Methods and data. Table 2.2: Constants used in the sublimation calculations for a sphere. Where they are different for a hexagonal plate, they are given in table 2.4. Symbol α β γ f g kT Lb Mw Mair µ0 p0 R Rs ρair,0 ρi S T0 Tb. Long Name coefficient for the velocity-mass-relation for cloud droplets (Seifert and Beheng, 2006, Table 1) coefficient for the velocity-mass-relation for cloud droplets (Seifert and Beheng, 2006, Table 1) coefficient for the velocity-mass-relation for cloud droplets (Seifert and Beheng, 2006, Table 1) ventilation factor (Field et al., 2008) gravitational constant thermal conductivity of air lapse rate molecular mass of water molecular mass of Earh’s air viscosity of air at T = 273 K and p = 101 325 Pa (Seinfeld and Pandis, 2006, Table A.7, pg, 1178) reference pressure universal gas constant specific gas constant for air reference density of air density of ice Sutherland’s constant for air (Chapman and Cowling, 1960, Table 15), in a temperature range from 0 to 300 ◦C reference temperature reference temperature in the barometric formula. Value 3.75 × 105 m/s/kgβ 2/3 1 2.6 9.81 m/s2 0.024 J/(m s K) −0.0065 K/m 18.02 × 10−3 kg/mol 28.9644 × 10−3 kg/mol 1.72 × 10−5 kg/(m s) 101 325 Pa 8.314 J/(K mol) 287.06 J/(kg K) 1.225 kg/m3 0.92 × 103 kg/m3 114(24) 273.15 K 288.15 K. Table 2.3: Equations used in the sublimation calculations for a hexagonal plate. The other equations used are the same as for a sphere and given in the text. Equation for hexagonal plates C = 2r/π (Pruppacher and Klett, 2010, eq. 13-77) (X ) ( X )2 ( X )3 ( X )4 f = 1.0 − 0.6042 · 10 + 2.79820 · 10 − 0.31933 · 10 − 0.06247 · 10 where 1 3 X = 0.632 · NRe (Pruppacher and Klett, 2010, eq. 13-90b) and (Ji and Wang, 1999) m = ρi · 9.17 × 10−3 · (2r)2.475 (Pruppacher and Klett, 2010, Table 2.2a). Replaces eq. 2.10 2.11. 2.18 and 2.21. Table 2.4: Same as table 2.2 but for hexagonal plates. Only those constants that differ from table 2.2 are shown. Symbol α β γ ρi. 11. Long name coefficient for the velocity-mass-relation (Seifert and Beheng, 2006, Table 1) coefficient for the velocity-mass-relation (Seifert and Beheng, 2006, Table 1) coefficient for the velocity-mass-relation (Seifert and Beheng, 2006, Table 1) density of ice (Pruppacher and Klett, 2010,. for cloud ice. Value 317 m/s/kgβ. for cloud ice. 0.363. for cloud ice. 0.5. Table 2.3). 0.9 × 103 kg/m3.

(24) 2.2. Ice crystal sublimation calculations. Together with the relative humidity from the ERA5 data (given with respect to water), the saturation vapour pressure in air and the supersaturation with respect to ice was then calculated: RH · esat,w 100 e s= −1 esat,i. (2.5). e=. (2.6). The diffusivity of water vapour in air was calculated following Hall and Pruppacher (1976, Eq. 13): ( )1.94 T p0 Dv = 0.211 × 10−4 (2.7) T0 p Furthermore, the growth factor was determined following Lamb and Verlinde (2011, pg. 328): G=. 1 ρi RT Mw Dv esat,i. +. ρi Ls Mw kT T. ·. ( Ls RT. ) −1. (2.8). which uses the latent heat of sublimation (valid between 236 and 273.16 K, Lohmann et al. (2016b)): 2. Ls = 46 782.5 + 35.8925 · T − 0.074 14 · T 2 + 541.5e−( 123.75 ) T. (2.9). For a sphere, the capacitance of an ice particle is simply equal to the radius at timestep i (Lohmann et al., 2016b, pg. 240): C = r[i] and the ventilation factor is given by (Pruppacher and Klett, 2010, eq. 13-61): ( )2 X f = 1.0 + 0.108 · 10. (2.10). (2.11). where 1. X = 0.71 3 · NRe U∞ dρair NRe = µ. (2.12) (2.13). (Lohmann et al., 2016b, eq. 7.36). For the terminal velocity, U∞ , v was used. The dynamic viscosity µ can be derived from Sutherland’s formula (Chapman and Cowling, 1960, eq. 12.32-2): ( ) 32 T0 + S T (2.14) µ = µ0 T0 T +S which can be rewritten and expanded to: 3. µ=B µ= with B =. µ0 ·(T0 +S) 3. T2 T +S. 3 √ BT02 B T0 (3S + T0 )(T − T0 ) + S + T0 2(S + T0 )2. (2.15) (2.16). . Inserting the values from table 2.2, this results in:. T02. µ = 1.72 × 10−5 kg/(m s) + 5.01 × 10−8 kg/(m s K)(T − 273 K). (2.17). With this, the mass and radius of the ice particle, its fall velocity and height could be timestepped: 4 m[0] = r[0]3 ρi π (2.18) 3 (2.19) dm = 4πCρi Gsf dt m[i + 1] = m[i] + dm (2.20) √ 3m[i + 1] (2.21) r[i + 1] = 3 4ρi π ( )γ ρair,0 β v[i + 1] = αm[i + 1] (2.22) ρair z[i + 1] = z[i] − v[i + 1] · dt. (2.23) 12.

(25) Chapter 2. Methods and data. Equation 2.19 follows Lamb and Verlinde (2011), using the ventilation factor f determined from equation 2.11. The fall speed is calculated in equation 2.22 following Seifert and Beheng (2006), with coefficients given in table 2.2. In the calculations, radiative heat transfer to and from the ice particles was ignored since Hall and Pruppacher (1976) found that it “is only of secondary importance in determining [an ice particle’s] survival distance in subsaturated air”. While the calculations are based on a scheme developed in Vassel (2018), here additional factors such as the ventilation factor and the temperature dependency in the dynamic viscosity were added. Furthermore, Vassel et al. (2019a) used mass-diameter relations and fall speed derived in Mitchell (1996), which in this study are taken from Pruppacher and Klett (2010) and Seifert and Beheng (2006). The timestepping script was stopped when the particle had reached Earth’s surface. Zero mass or a radius below 1 × 10−8 m also stopped the script, meaning that the particle had sublimated. If not stopped by these conditions, the timestepping continued for one day. The sublimation height was returned and compared to the height of the liquid cloud top, which was derived from the height of the ice cloud base and the distance to the liquid cloud in the DARDAR data. When the sublimation height was lower than the height of that cloud top, the ice crystals at that grid point were marked as having the sufficient falling distance. These calculations present a conservative estimate. In reality, ice crystals have a size distribution. The large ice crystals within this distribution survive longer distances than the ones with the effective radius, for which the survival is calculated. Also, the effective radius of ice crystals is underestimated in DARDAR v2 compared to the newer version v3 (which is not available yet), by 5 % to as much as 40 % (Cazenave et al., 2019).. 2.3. Modelling with COSMO. The second part of this study seeks to assess how the seeder-feeder process is represented in the regional weather and climate model Consortium of Small-Scale Modeling (COSMO) and how the process influences the microphysics of simulated clouds. COSMO was first developed by the German National Weather Service DWD (Steppeler et al., 2003) and is still used for weather prediction by Meteo Swiss today. Of particular interest for this study, COSMO has recently been used to study mixed-phase clouds in the alpine region (Henneberg et al., 2017; Lohmann et al., 2016a). It uses a 2-moment cloud microphysics scheme that calculates size and number of six hydrometeor classes: ice crystals, snow flakes, cloud droplets, rain, graupel and hail (Seifert and Beheng, 2006). The scheme also contains ice crystal sedimentation and sublimation as well as riming with cloud and rain droplets and vapour deposition growth, which is important for the seeder-feeder process. In terms of secondary ice production, COSMO only contains rime splintering„ i.e. the HallettMossop process (Hallett and Mossop, 1974). In the current study, COSMO version 5.4.1b is used. It is based off the official release version COSMO 5.03, which for example has been used in Sullivan et al. (2018). Continuing to version 5.04b, there were changes in the assimilation and dynamics (version 5.04), and a new boundary condition module and data format for the convection scheme were implemented (version 5.04b), as described in the COSMO Release Notes (2019). The COSMO-ICON Version of the Prognostic TKE Turbulence Scheme that was implemented for version 5.04a is not used in this study. The output of ice, snow, graupel and hail precipitation fluxes was newly implemented especially for this study in order to witness the sedimentation of these hydrometeors as needed for the seeder-feeder process. In the present study, COSMO is computed on a rotated latitude longitude grid (with the pole at 43 °N and −170 °E), with a time stepping of 10 s. The horizontal resolution is 0.01°, which corresponds to 1.1 km in mid-latitudes. 80 vertical hybrid layers up to an altitude of 23 km are used. The domain in this study includes most of the alpine ridge, spanning from 4 to 11 °N and 43.5 to 48.24 °E. This is almost identical to the domain used in the first part of this study (see section 2.1.1 and Figure 2.1). Input for the initial and boundary conditions was generated from COSMO-7 hourly analysis fields from Meteo Swiss using the COSMO interpolation utility INT2LM (version 1.10). To pick the case study date, various constraints needed to be taken into account: the length of the satellite track within the domain should be large; there should be many points with a wide range of ∆zil and a high cloud cover; the COSMO-7 hourly analysis data from 2016 through 2017 is readily available, so ideally the case study should be within this time. With the satellite overflight on the 18.05.2016, which entered the domain at around 12:30pm, a case study was found that fulfilled all conditions. Therefore, all simulations within this study were run for this date. 13.

(26) 2.3. Modelling with COSMO. 20.0. 1.0. 17.5 0.8. 12.5. 0.6. 10.0 0.4. 7.5. Cloud area fraction. Height (km). 15.0. 5.0 0.2 2.5 0.0. 45.0. 45.5. 46.0 46.5 47.0 Latitude (°N). 47.5. 48.0. 0.0. Figure 2.2: Vertical and latitudinal cross section of the cloud cover within the COSMO simulation on 18.05.2016 at 12 am. The longitude is fixed at 5.13 °E. This was identified as a seeder-feeder situation, because ice crystals were falling from the upper cloud layer into the lower one between 45.5 °N and 46 °N. The orange dots mark the starting positions of the Lagrangian trajectories. The topography is displayed in white.. 2.3.1. Trajectory calculations using LAGRANTO. In the standard Eulerian framework of model output, it is difficult to discern the effect of, for example, seeding ice crystals on single clouds and their microphysics. This is because the clouds may be advected from time step to time step in the model and this could be different at each model level. It is therefore difficult to follow them in time, especially when the clouds are changing their extent or are merging. On the contrary, the Lagrangian framework follows air parcels in time and space. This way, the temporal evolution of their properties can be observed. In this study, the Lagrangian analysis tool LAGRANTO, as described by Sprenger and Wernli (2015), was used to compute such Lagrangian trajectories. LAGRANTO uses the horizontal and vertical wind and the surface pressure from the COSMO model output as input (U and V in m/s, OMEGA in Pa/s and PS in hPa). It then solves the trajectory equation D⃗x = ⃗u(x) Dt. (2.24). where ⃗x is the position vector and ⃗u the wind vector. Furthermore, it can select values of chosen variables at the calculated trajectory positions and thereby trace them. In this study, trajectories were started from points that had been identified as feeder cloud tops in seeder-feeder situations in the Eulerian view of COSMO output. For this, first cloud tops and cloud bases were identified: points with a cloud cover, cloud cover at the level below and no cloud cover at the level above were marked as cloud tops; points with a cloud cover, cloud cover at the level above and no cloud cover at the level below were marked as cloud bases. Then, those cloud tops that had a sedimentation flux of ice crystals > 1 × 10−9 kg/(m2 s) at all levels above them, up to the next cloud base, were identified as seeded cloud tops. Assuming ρi = 0.92 × 103 kg/m3 and ri = 40 µm, a sedimentation flux of 1 × 10−9 kg/(m2 s) is equivalent to 4 ice crystals /(m2 s). This threshold was chosen to exclude noise and to only retain seeding cases with a significant number of ice crystals falling into the cloud top. From these identified seeder-feeder events, a case in the south west of the domain (to be able to follow it longer along the south-westerly flow) with a large horizontal extent of seeding and a clear distinction between the seeder and the feeder cloud was selected visually. The event that fulfilled these prerequisites happened on 18.05.2016 at 12 am. 43 forward and backward trajectories were started from the feeder cloud top positions at the time of the identified seeder-feeder event. Their starting positons are displayed in Figure 2.2 on top of a cross section of the cloud cover in the COSMO simulation.. 2.3.2. Sensitivity study. To quantify the impact that natural cloud seeding has in the COSMO simulations, 2 different sensitivity studies were performed. In the “no seeding” case study (hereafter termed NoSed ), all ice crystal, snow, 14.

(27) Chapter 2. Methods and data. hail, and graupel sedimentation fluxes outside of clouds were set to 0 (in the following fluxes always means these ice sedimentation fluxes). Here, the classification of grid points as out of cloud was based on a saturation ratio with respect to ice lower than 1. This sensitivity study removed all ice sedimenting out of clouds, and thereby would also remove e.g. mixed-phase cloud to mixed-phase cloud seeding and especially all precipitation that originates from the ice phase. To target only the seeder-feeder situations that this study is focused on, namely ice crystals falling from an ice cloud into a liquid (liquid, mixedphase or ice) cloud below, a second sensitivity study was performed: in this sensitivity study, termed NoSed35, all ice crystal, snow, hail and graupel sedimentation fluxes outside of clouds (defined as in NoSed ) were set to 0 only at temperatures colder than −35 ◦C. The simulations for both sensitivity studies were started at 2 am on 18.05.2016. In order to evaluate the significance of their deviations from the control simulation, an ensemble of control simulations was created. This is necessary to display the range of valid model results resulting from the uncertainty in initial conditions. When the results from the sensitivity simulations lie within the spread of the control ensemble members, the deviations are not significant. To create such an ensemble, simulations were started every hour, at 0, 1, 2, 3 and 4 am. In the following, these simulations are termed ctrl00, ctrl01, ctrl02, ctrl03, ctrl04, and their mean is ctrl_mean. The ice crystal size was computed from the model output, namely from the ice crystal mass and number concentrations, assuming spherical crystals for the mass-volume relationship (as in equation 2.18). To differentiate between the direct and the indirect effect from the perturbations in the analysis, a mask was prepared to the variables in the model output before any further analysis was applied. In simulation NoSed in-cloud and out-of cloud values of the variables were computed by setting the variables to zero either where there was a cloud present (relative humidity with respect to ice larger than 1) or where there was no cloud present (relative humidity with respect to ice smaller than 1), respectively. In simulation NoSed35 variables were set to zero where there was no cloud at temperatures below −35 ◦C, because this is where perturbations to the precipitation fluxes were applied.. 15.

(28) Chapter 3. Results and discussion In the following, results from the two parts of this work are presented, firstly the satellite retrieval data analysis and secondly the modelling case study.. 3.1. DARDAR data. From the DARDAR satellite data product, the distance between ice and liquid cloud layers (∆zil ) and the size of the ice crystals at the ice cloud base was extracted. The results and their analysis are presented in the following. Afterwards, results from the sublimation script computing whether an ice crystal would survive the vertical distance from the ice to the liquid cloud layer are shown. Distribution of distances between ice and liquid cloud layer The seeder-feeder process requires an ice cloud layer above a liquid cloud layer (as described in Section 2.1.1, any cloud at temperatures > −35 ◦C is termed liquid in this study). The distance between both clouds, ∆zil , needs to be small enough for the ice crystals to survive it. Figure 3.1 shows the average frequency of those distances between ice and liquid cloud. It takes into account data from the whole time period and domain of this study. In Figure 3.1a, the distribution of ∆zil normalized by the number of distance measurements is shown. It can therefore be understood as the average distribution of ice-liquid cloud distances within a unit area. Firstly, note that 67% of all measurements do not show an ice-liquid cloud distance at all. In those cases, either only clouds of one category were present, or none at all. 33% of the measurements have both an ice and a liquid cloud simultaneously. In about half of these cases (18%), ∆zil is less than 100 m. This may either be the case when the ice and the liquid cloud are truly separated by a small distance, or when the two differently classified layers are actually part of the same cloud. From the construction of the classification algorithm, this would be the case when the −35 ◦C isotherm goes through the cloud. That case is illustrated in Figure 3.2b. Our algorithm does not require a cloud-free layer to be in between the liquid and ice cloud. This is in contrast to Mace et al. (2009), who require 4 layers (960 m) between two cloud layers to classify them as two separate layers. Of course, those cases with the −35 ◦C isotherm within the cloud could also have another liquid cloud underneath so that there would also be a physical distance to a lower-lying separate liquid cloud. However, these distances do not appear in our data and analysis. On the other hand, in observational studies of the seeder-feeder process, for example Ansmann et al. (2009) reported seeing ice virga between the seeder and the feeder cloud. In our analysis, these would also be seen as an ice cloud with a very small or no distance to the next liquid cloud, and thus also fall in the < 100 m frequency bin. This increases the number of cases which really are a case of two separate cloud layers, but which fall into the < 100 m bin. Mace et al. (2009, pg. 9) also mentioned this as a cause of misclassification in their study, namely that sedimenting particles cause separate cloud layers to be seen as one in the radar data. Vassel et al. (2019a) addressed that problem by excluding cases from their analysis where the two clouds are not clearly separated in the radar signal. The other half of the cases represents the classical two cloud seeder-feeder situation, with an ice cloud clearly separated from a liquid cloud below. This is illustrated in Figure 3.2a. Here, ice to liquid cloud distances (∆zil ) are distributed equally between 100 m and 8000 m. No ∆zil seems to be preferred or less likely than the others. For ∆zil > 8000 m, the frequency tapers off. This is likely because there is an.