<p>The entry of satellite-derived decadal cloud datasets with homogeneous coverage in time and space enables studies not possible before. This thesis presents two such applications. The first study deals with cloud-aerosol processes and the second with an evaluation of cloud simulations from a regional climate model.</p><p>The first part of the thesis describes the used satellite-derived dataset based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) on the polar orbiting NOAA satellites. A method for cloud retrieval and the compilation of a 1991-2000 Scandinavian cloud climatology are described.</p><p>The second part reveals an intriguing anti-correlation between monthly mean satellite-derived cloudiness and the concentration of the cosmogenetic isotope Beryllium-7 in near-surface aerosol samples for three measurement sites in Sweden. Large-scale transport processes are suggested as the most likely physical mechanism for this behaviour but more complex relations to cloud microphysical processes are not ruled out.</p><p>The final part presents a thorough evaluation of cloud simulations of the SMHI Rossby Centre regional atmospheric model (RCA3). Several model-to-satellite adaptations are applied to avoid artificial biases of results. The study stresses the necessity to account for initial differences between observed and modelled clouds caused by satellite cloud detection limitations. Results show good agreement of modelled and observed cloud amounts while the vertical distribution of clouds appears largely different. RCA3 underestimates medium-level clouds while overestimating low- and high-level clouds. Also, the current use of the Maximum cloud overlap approach in the radiation scheme and an indicated excess of cloud condensate in modelled clouds appear to create excessive cloud optical thicknesses with serious implications for the surface radiation budget.</p><p>Future applications are outlined based on greatly enhanced satellite-derived cloud and radiation budget datasets.</p>

The entry of satellite-derived decadal cloud datasets with homogeneous coverage in time and space enables studies not possible before. This thesis presents two such applications. The first study deals with cloud-aerosol processes and the second with an evaluation of cloud simulations from a regional climate model. The first part of the thesis describes the used satellite-derived dataset based on imagery from the Advanced Very High Resolution Radiometer (AVHRR) on the polar orbiting NOAA satellites. A method for cloud retrieval and the compilation of a 1991-2000 Scandinavian cloud climatology are described. The second part reveals an intriguing anti-correlation between monthly mean satellite-derived cloudiness and the concentration of the cosmogenetic isotope Beryllium-7 in near-surface aerosol samples for three measurement sites in Sweden. Large-scale transport processes are suggested as the most likely physical mechanism for this behaviour but more complex relations to cloud microphysical processes are not ruled out. The final part presents a thorough evaluation of cloud simulations of the SMHI Rossby Centre regional atmospheric model (RCA3). Several model-to-satellite adaptations are applied to avoid artificial biases of results. The study stresses the necessity to account for initial differences between observed and modelled clouds caused by satellite cloud detection limitations. Results show good agreement of modelled and observed cloud amounts while the vertical distribution of clouds appears largely different. RCA3 underestimates medium-level clouds while overestimating low- and high-level clouds. Also, the current use of the Maximum cloud overlap approach in the radiation scheme and an indicated excess of cloud condensate in modelled clouds appear to create excessive cloud optical thicknesses with serious implications for the surface radiation budget. Future applications are outlined based on greatly enhanced satellite-derived cloud and radiation budget datasets.

Cloud drop condensation nuclei (CCN) and ice nuclei (IN) particles determine to a large extent cloud microstructure and, consequently, cloud albedo and the dynamic response of clouds to aerosol-induced changes to precipitation. This can modify the reflected solar radiation and the thermal radiation emitted to space. Measurements of tropospheric CCN and IN over large areas have not been possible and can be only roughly approximated from satellite-sensor-based estimates of optical properties of aerosols. Our lack of ability to measure both CCN and cloud updrafts precludes disentangling the effects ofmeteorology fromthose of aerosols and represents the largest component in our uncertainty in anthropogenic climate forcing.Ways to improve the retrieval accuracy include multiangle and multipolarimetric passive measurements of the optical signal and multispectral lidar polarimetric measurements. Indirect methods include proxies of trace gases, as retrieved by hyperspectral sensors. Perhaps the most promising emerging direction is retrieving the CCN properties by simultaneously retrieving convective cloud drop number concentrations and updraft speeds, which amounts to using clouds as natural CCN chambers. These satellite observations have to be constrained by in situ observations of aerosol-cloud-precipitation-climate (ACPC) interactions, which in turn constrain a hierarchy of model simulations of ACPC. Since the essence of a general circulation model is an accurate quantification of the energy and mass fluxes in all forms between the surface, atmosphere and outer space, a route to progress is proposed here in the form of a series of box flux closure experiments in the various climate regimes. A roadmap is provided for quantifying the ACPC interactions and thereby reducing the uncertainty in anthropogenic climate forcing.

The formation of cloud droplets on aerosol particles, technically known as the activation of cloud condensation nuclei (CCN), is the fundamental process driving the interactions of aerosols with clouds and precipitation. Knowledge of these interactions is foundational to our understanding of weather and climate. The Intergovernmental Panel on Climate Change (IPCC) and the Decadal Survey (NRC 2007) indicate that the uncertainty in how clouds adjust to aerosol perturbations dominates the uncertainty in the overall quantification of the radiative forcing attributable to human activities. The Clouds, Hazards, and Aerosols Survey for Earth Researchers (CHASER) satellite mission concept responds to the IPCC and Decadal Survey concerns by studying the activation of CCN and their interactions with clouds and storms. The CHASER satellite mission was developed to remotely sense quantities necessary for determining the interactions of aerosols with clouds and storms. The links between the Decadal Survey recommendations and the CHASER goals, science objectives, measurements, and instruments are described in Table 1. Measurements by current satellites allow a rough determination of profiles of cloud particle size but not of the activated CCN that seed them. CHASER will use an innovative technique (Freud et al. 2011; Freud and Rosenfeld 2012; Rosenfeld et al. 2012) and high-heritage (flown in a previous spaceflight mission) instruments to produce satellite-based remotely sensed observations of activated CCN and the properties of the clouds associated with them. CHASER will estimate updraft velocities at cloud base to calculate the number density of activated CCN as a function of the water vapor supersaturation. CHASER will determine the CCN concentration and cloud thermodynamic forcing (i.e., forcing caused by changes in the temperature and humidity of the boundary layer air) simultaneously, allowing their effects to be distinguished. Changes in the behavior of a group of weather systems in which only one of the quantities varies (a partial derivative of the intensity of the weather system with respect to the desirable quantity) will allow the determination of each effect statistically.

Aerosole dienen als Kondensationskerne (CCN) und verändern die Wolkentropfenkonzentration (Nd) und weitere Wolkeneigenschaften. Nd ist daher ein Schlüsselparameter von Aerosol-Wolken-Wechselwirkungen (ACI). ACI sind bezüglich ihrer Klimarelevanz nur unzureichend bestimmt. ACI wurden auf verschiedenen Skalen und mit unterschiedlichen Methoden von unterschiedlichen Perspektiven aus untersucht, was zu einer großen Variabilität von ACI Metriken in der Literatur führt. Für eine genauere Quantifizierung von ACI, wurden in dieser Arbeit damit verbundene, entscheidende Unsicherheiten diskutiert, u.a. das Einmischen trockener Luft, Unsicherheiten von Retrieval-Methoden und die Konsistenz von Schlüsselparametern auf verschiedenen Skalen. Um Nd und weitere Wolkeneigenschaften aus passiven Satellitenbeobachtungen für Flüssigwasserwolken abzuleiten, wird oft das adiabatische Modell herangezogen. Es wurde untersucht, inwiefern dieses Modell reale Bedingungen wiedergibt. Es wurde gezeigt, dass Wolken typischerweise subadiabatisch sind, mit stark reduziertem Flüssigwassergehalt nahe der Wolkenoberkante. Der Einmischprozess variiert zeitlich stark und erschwert die Quantizierung von ACI. Für unterschiedliche Cloudnet-Stationen variieren die Medianwerte des subadiabatischen Faktors von 0.35+/-0.12 bis 0.48+/-0.22. Dieser hängt stark von der geometrischen Wolkendicke ab, wobei dickere Wolken subadiabatischer sind. Die Ableitung von Nd aus bodenbasierten Radar-Radiometer-Beobachtungen ist sehr sensitiv gegenüber a-priori Annahmen. Um zusätzliche Beobachtungen und Unsicherheiten berücksichtigen zu können, wurde ein neuartiges Optimal Estimation (OE) Retrieval entwickelt und mittels synthetischer Wolkenprofile evaluiert. Mittels einer Sensitivitätsstudie konnte die Verbesserung der Retrieval-Genauigkeit für die OE Methode gezeigt werden, wenn die Beobachtungen der optischen Dicke mit berücksichtigt werden. Die Annahme einer typischen Breite der Tropfengrößenverteilung ist dabei eine der größten Unsicherheiten von Nd (Unsicherheit für die OE Methode etwa 150%, für die Radar-Radiometer-Methode etwa 200%). Desweiteren wurden aus unterschiedlichen Perspektiven abgeleitete wolkenmikrophysikalische Größen hinsichtlich ihrer Konsistenz untersucht. Beim Vergleich von Nd und des Effektivradius (re) vom bodengebundenen Retrieval mit in-situ Beobachtungen für einen Falltag wurde eine gute Übereinstimmung gefunden. Beim Vergleich der von SEVIRI und Bodenstationen abgeleiteten Wolkeneigenschaften haben sich mittlere quadratische Abweichungen des Flüssigwasserpfads und der optischen Dicke von jeweils 65 gm2 und 14 ohne signifikanten Bias gezeigt. Damit zeigt sich SEVIRI für großskalige, statistische ACI-Untersuchungen repräsentativ zur Bodenperspektive. Für individuelle Falltage traten jedoch teils größere Unterschiede auf, welche durch Inhomogenitäten und Auflösungseffekte erklärt werden können. Mit SEVIRI Beobachtungen und aus der MACC Reanalyse abgeleiteten CCN Konzentrationen wurde eine Quantifizierung von ACI für das Jahr 2012 durchgeführt. Dabei wurde für Europa ein deutlicher, mikrophysikalischer Effekt gefunden, d.h. eine Abnahme von re und eine Zunahme von Nd mit zunehmender CCN Konzentration. Im Gegensatz dazu wurde für die Wolkenalbedo kein eindeutiger Anstieg mit der CCN-Konzentration gefunden. Verwendet man die Aerosol-optische Dicke (AOD) anstelle der CCN-Konzentration, sind die Werte der ACI Metrik nur halb so groß. Dieses Ergebnis deutet darauf hin, dass die AOD kein optimaler CCN-Proxy ist.

Auf der Azoreninsel Graciosa, etwa 1600 km westlich von Portugal, betreibt das U.S. Department of Energy eine American Radiation Measurement (ARM) Station, welche mit einer Vielzahl von Messgeräten ausgestattet ist. Durch ihre Lage im subtropischen Hochdruckgürtel eignet sich diese Station gut zur Erforschung von mariner Stratocumulusbewölkung. Für eine Klimatologie der Eigenschaften dieser Bewölkung wurden Messwerte von Radiosonden, einem Ceilometer, einem Distrometer und einem Mikrowellenradiometer aus dem Zeitraum vom 01. März 2014 bis 31. August 2018 verwendet. Daraus wurde der Tages- und Jahresgang der Wolkenhöhe und -dicke, des Flüssigwasserpfades, des Entkopplungs- und Niederschlagsverhaltens und der Inversionsstärke abgeleitet. Die Auswertung basiert auf einer Methode von Zhang et al. (2010) zur Detektion von Wolkenschichten aus Radiosondenmessungen, die für die Abgrenzung mariner Stratocumulusbewölkung von anderen Wolkenarten angepasst und anschließend mit den Messungen eines Ka-Band-Wolkenradars evaluiert wurde. Während die Parameter im Tagesgang nur geringe Änderungen zwischen 00 UTC und 12 UTC aufwiesen, konnten im Jahresgang deutliche Variationen bei Wolkenhöhe, Niederschlagsverhalten, Entkopplung und Inversionstärke gezeigt werden. Die beobachteten Variationen wurden mit den Jahresgängen der solaren Einstrahlung, der Temperatur sowie der synoptischen Variabilität über den Azoren verglichen. / The U.S. Department of Energy operates an American Radiation Measurement (ARM) station on Graciosa (Azores), an island located approximately 1600 km west of Portugal. Due to the wide variety of installed measuring devices and its location in the subtropical belt of high pressure the station is well suited for studying marine stratocumulus. To derive a climatology of the properties of these clouds, data from radiosondes, a ceilometer, a disdrometer and a microwave radiometer were used for the period from 1st March 2014 to 31st August 2018 to get the daily and annual cycle of cloud height and thickness, liquid water path, precipitation, inversion strength and the presence of decoupling. The evaluation is based on a method developed by Zhang et al. (2010) to detect cloud layers from moisture profiles in the sounding data. The method was adapted for the distinction between marine stratocumulus and other cloud types. For the evaluation of the method data from a Ka-band cloud radar were used. While only minor changes occurred in the daily variability of the properties, a clear annual cycle was observed for cloud height, precipitation, inversion strength and decoupling of the stratocumulus. The observed variations were compared with the annual cycles of solar irradiance, temperature and the synoptic patterns over the Azores.

To date, no observation-based proxy for climate change has been successful in quantifying the feedbacks between clouds and climate. The most promising, yet demanding, avenue to gain confi dence in cloud–climate feedback estimates is to utilize observations and large-eddy simulations (LES) or cloud-resolving modeling (CRM) to improve cloud process parameterizations in large-scale models. Sustained and improved satellite observations are essential to evaluate large-scale models. A reanalysis of numerical prediction models with assimilation of cloud, aerosol, and precipitation observations would provide a valuable dataset for examining cloud interactions. The link between climate modeling and numerical weather prediction (NWP) may be exploited by evaluating how accurate cloud characteristics are represented by the parameterization schemes in NWP models. A systematic simplifi cation of large-scale models is an important avenue to isolate key processes linked to cloud–climate feedbacks and would guide the formulation of testable hypotheses for fi eld studies. Analyses of observation-derived correlations between cloud and aerosol properties in combination with modeling studies may allow aerosol–cloud interactions to be detected and quantifi ed. Reliable representations of cloud dynamic and physical processes in large-scale models are a prerequisite to assess aerosol indirect effects on a large scale with confi dence. To include aerosol indirect effects in a consistent manner, we recommend that a “radiative fl ux perturbation” approach be considered as a complement to radiative forcing.

With a nearly twice as strongly pronounced temperature increase compared to that of the Northern Hemisphere, the Arctic is especially susceptible to global climate change. The effect of clouds on the Arctic warming is especially uncertain, which is caused by misrepresented cloud microphysical processes in atmospheric models. This thesis aims at employing a scale- and definition-aware comparison of models and observations and will propose changes how to better parameterize Arctic clouds in atmospheric models. In the first part of this thesis, ECHAM6, which is the atmospheric component of the MPI-ESM global climate model, is compared to spaceborne lidar observations of clouds from the CALIPSO satellite. This comparison shows that ECHAM6 overestimates Arctic low-level, liquid containing clouds over snow- and ice-covered surfaces, which consequently leads to an overestimated amount of radiative energy received by the surface. Using sensitivity studies, it is shown that the probable cause of the model biases in cloud amount and phase is related to misrepresented cloud microphysical parameterization (i.e., parameterization of the Wegener-Bergeron-Findeisen process and of the cloud cover scheme) in ECHAM6. By revising those processes, a better representation of cloud amount and cloud phase is achieved, which helps to more accurately simulated the amount of radiative energy received by the Arctic in ECHAM6. The second part of this thesis will focus on a comparison of kilometer-scale simulation with the ICON model to aircraft observations from the ACLOUD campaign that took place in May/June 2017 over the sea ice-covered Arctic Ocean north of Svalbard, Norway. By comparing measurements of solar and terrestrial surface irradiances during ACLOUD flights to the respective quantities in ICON, it is shown that the model systematically overestimates the transmissivity of the mostly liquid clouds during the campaign. This model bias is traced back to the way cloud condensation nuclei get activated into cloud droplets in the two-moment, bulk microphysical scheme used. By parameterizing subgrid-scale vertical motion as a function of turbulent kinetic energy, a more realistic CCN activation into cloud droplets is achieved. This consequently results in an improved representation of cloud optical properties in the ICON simulations. Furthermore, the results of two studies to which contributions have been made during the Ph.D. will be summarized. In Petersik et al. 2018, the impact of subgrid-scale variability in clear-sky relative humidity on hygroscopic growth of aerosols in the aerosol-climate model ECHAM6-HAM2 has been explored. It was shown that the revised parameterization of hygroscopic growth of aerosols resulted in a stronger swelling of aerosol particles, which consequently causes an increased backscattering of solar radiation. In the study of Costa-Suros et al. 2019, it is explored whether it is possible to detect and attribute aerosol-cloud interactions in large-eddy simulation over Germany. It was shown that an increase in cloud droplet number concentration could be attributed to an increased aerosol load, while such an attribution was not possible for other cloud micro- and macrophysical variables.