Ziel dieser Dissertation ist es, mithilfe zweier Herangehensweisen, das Verständnis der Zusammenhänge verschiedener physikalischer Prozesse des Klimasystems zu verbessern. Im ersten Teil verwende ich Klimanetzwerke zu die gemeinsame Abhängigkeit von Meeresoberflächentemperaturen (SSTs) und Niederschlägen in Hinsicht auf globale charakteristiken und räumlichen Muster untersuchen. In diesem Kontext ist die El Niño Southern Oscillation (ENSO) das wichtigste Phänomen, welches großskalig SSTs und Niederschläge beeinflusst. Durch meine Analyse decke ich kurz und weitreichende Verbindungen auf und zeige deren Abhängigkeit von der jeweiligen ENSO-Phase (El Nino, La Nina, neutrale Phase). Darüber hinaus werden durch die Kombination einer diskreten Wavelet-Transformation mit dem Konzept der gekoppelten Klimanetzwerkanalyse die skalenspezifischen Verbindungen aufgelöst, die bei der ursprünglichen Auflösung der Daten oft übersehen werden. Im zweiten Teil der Arbeit verwende ich Simulationen des COnsortium for Small scale MOdeling (COSMO) Climate Limited-area Modell (CCLM) und untersuche die Auswirkungen des Sobradinho-Stausees. In dieser Arbeit benutzen ich das Flake Modell, um das vertikale Temperaturprofil des Sees zu bestimmen. Durch die Einbettung des Flake Modells in das CCLM konnte ich den Sobradinho-Stausee untersuchen. Dabei simuliere ich zwei verschiedene Szenarien. Die Simulationsergebnisse verifiziere ich mithilfe meteorologischer Daten von Oberflächen- und Satellitenmessungen. Die Ergebnisse zeigen, dass der See sowohl die bodennahe Temperatur als auch Wind- und Luftfeuchtigkeitsmuster der Umgebung beeinflusst. Zudem wird die Luftfeuchtigkeit durch den See erhöht und bewirkt Seewinde. Die Effekte des Sees auf die Luftfeuchtigkeit und temperaturen beschränken sich nicht nur auf die Nähe des Sees, sondern auch auf relativ weit entfernte Gebiete. / This dissertation aims at improving our understanding of the mechanisms of interactions between physical processes within the climate system via two different approaches. In the first part, I have utilized climate networks to understand the mutual interdependence between sea surface temperatures (SST) and precipitation (PCP) in terms of global characteristics and spatial patterns. In this context, the globally most relevant phenomenon is the El Niño Southern Oscillation (ENSO), which strongly affects large-scale SST variability as well as PCP patterns all around the globe. My analysis uncovers both local and remote statistical connections and demonstrates their dependence on the current ENSO phase (El Niño, La Niña or neutral phase). Furthermore by combining time-scale decomposition by means of a discrete wavelet transform with the concept of coupled climate network analysis unravel the scale-specific connections that are often overlooked at the original resolution of the data. In the second part of this thesis, I have focused on simulations with the COnsortium for Small scale MOdeling (COSMO) Climate Limited-area Model (CCLM) and investigate the effects of Lake Sobradinho, a large reservoir in Northeastern Brazil, on the local near-surface atmospheric and boundary layer conditions. In this thesis, the FLake model (Freshwater Lake model) is applied for obtaining the lake’s vertical temperature profile. I have simulated two alternative scenarios. The performance of the simulation is compared with data from surface meteorological stations as well as satellite data. The obtained results demonstrate that the lake affects the near-surface air temperature of the surrounding area as well as its humidity and wind patterns. Moreover, the humidity is significantly increased as a result of the lake’s presence and causes a lake breeze. The observed effects on humidity and air temperature also extend over areas relatively far away from the lake.

Current climate models often predict fractional cloud cover on the basis of a diagnostic probability density function (PDF) describing the subgrid-scale variability of the total water specific humidity, qt, favouring schemes with limited complexity. Standard shapes are uniform or triangular PDFs the width of which is assumed to scale with the gridbox mean qt or the grid-box mean saturation specific humidity, qs. In this study, the qt variability is analysed from large-eddy simulations for two stratocumulus, two shallow cumulus, and one deep convective cases. We find that in most cases, triangles are a better approximation to the simulated PDFs than uniform distributions. In two of the 24 slices examined, the actual distributions were so strongly skewed that the simple symmetric shapes could not capture the PDF at all. The distribution width for either shape scales acceptably well with both the mean value of qt and qs, the former being a slightly better choice. The qt variance is underestimated by the fitted PDFs, but overestimated by the existing parameterisations. While the cloud fraction is in general relatively well diagnosed from fitted or parameterised uniform or triangular PDFs, it fails to capture cases with small partial cloudiness, and in 10 – 30% of the cases misdiagnoses clouds in clear skies or vice-versa. The results suggest choosing a parameterisation with a triangular shape, where the distribution width would scale with the grid-box mean qt using a scaling factor of 0.076. This, however, is subject to the caveat that the reference simulations examined here were partly for rather small domains and driven by idealised boundary conditions.

Current climate models often predict fractional cloud cover on the basis of a diagnostic probability density function (PDF) describing the subgrid-scale variability of the total water specific humidity, qt, favouring schemes with limited complexity. Standard shapes are uniform or triangular PDFs the width of which is assumed to scale with the gridbox mean qt or the grid-box mean saturation specific humidity, qs. In this study, the qt variability is analysed from large-eddy simulations for two stratocumulus, two shallow cumulus, and one deep convective cases. We find that in most cases, triangles are a better approximation to the simulated PDFs than uniform distributions. In two of the 24 slices examined, the actual distributions were so strongly skewed that the simple symmetric shapes could not capture the PDF at all. The distribution width for either shape scales acceptably well with both the mean value of qt and qs, the former being a slightly better choice. The qt variance is underestimated by the fitted PDFs, but overestimated by the existing parameterisations. While the cloud fraction is in general relatively well diagnosed from fitted or parameterised uniform or triangular PDFs, it fails to capture cases with small partial cloudiness, and in 10 – 30% of the cases misdiagnoses clouds in clear skies or vice-versa. The results suggest choosing a parameterisation with a triangular shape, where the distribution width would scale with the grid-box mean qt using a scaling factor of 0.076. This, however, is subject to the caveat that the reference simulations examined here were partly for rather small domains and driven by idealised boundary conditions.

Diese Dissertation beschäftigt sich mit regionaler Klimamodellierung über Südamerika, der Analyse von Modellsensitivitäten bezüglich Wolkenparametrisierungen und der Entwicklung neuer Methoden zur Modellevaluierung mithilfe von Klimanetzwerken. Im ersten Teil untersuchen wir Simulationen mit dem COnsortium for Small scale MOdeling model in CLimate Mode (COSMO-CLM) und stellen die erste umfassende Evaluierung dieses dynamischen regionalen Klimamodells über Südamerika vor. Dabei untersuchen wir insbesondere die Abhängigkeit simulierter tropischer Niederschläge von Parametrisierungen subgitterskaliger cumuliformer und stratiformer Wolken und finden starke Sensitivitäten bezüglich beider Wolkenparametrisierungen über Land. Durch einen simultanen Austausch der entsprechenden Schemata gelingt uns eine beträchtliche Reduzierung von Fehlern in klimatologischen Niederschlags- und Strahlungsmitteln, die das COSMO-CLM über tropischen Regionen für lange Zeit charakterisierten. Im zweiten Teil führen wir neue Metriken für die Evaluierung von Klimamodellen bezüglich räumlicher Kovariabilitäten ein. Im Kern bestehen diese Metriken aus Unähnlichkeitsmaßen für den Vergleich von simulierten mit beobachteten Klimanetzwerken. Wir entwickeln lokale und globale Unähnlichkeitsmaße zum Zwecke der Darstellung lokaler Unähnlichkeiten in Form von Fehlerkarten sowie der Rangordnung von Modellen durch Zusammenfassung lokaler zu globalen Unähnlichkeiten. Die neuen Maße werden dann für eine vergleichende Evaluierung regionaler Klimasimulationen mit COSMO-CLM und dem Statistical Analogue Resampling Scheme über Südamerika verwendet. Dabei vergleichen wir die sich ergebenden Modellrangfolgen mit solchen basierend auf mittleren quadratischen Abweichungen klimatologischer Mittelwerte und Varianzen und untersuchen die Abhängigkeit dieser Rangfolgen von der betrachteten Jahreszeit, Variable, dem verwendeten Referenzdatensatz und Klimanetzwerktyp. / This dissertation is about regional climate modeling over South America, the analysis of model sensitivities to cloud parameterizations, and the development of novel model evaluation techniques based on climate networks. In the first part we examine simulations with the COnsortium for Small scale MOdeling weather prediction model in CLimate Mode (COSMO-CLM) and provide the first thorough evaluation of this dynamical regional climate model over South America. We focus our analysis on the sensitivity of simulated tropical precipitation to the parameterizations of subgrid-scale cumuliform and stratiform clouds. It is shown that COSMO-CLM is strongly sensitive to both cloud parameterizations over tropical land. Using nondefault cumulus and stratus parameterization schemes we are able to considerably reduce long-standing precipitation and radiation biases that have plagued COSMO-CLM across tropical domains. In the second part we introduce new performance metrics for climate model evaluation with respect to spatial covariabilities. In essence, these metrics consist of dissimilarity measures for climate networks constructed from simulations and observations. We develop both local and global dissimilarity measures to facilitate the depiction of local dissimilarities in the form of bias maps as well as the aggregation of those local to global dissimilarities for the purposes of climate model intercomparison and ranking. The new measures are then applied for a comparative evaluation of regional climate simulations with COSMO-CLM and the STatistical Analogue Resampling Scheme (STARS) over South America. We compare model rankings obtained with our new performance metrics to those obtained with conventional root-mean-square errors of climatological mean values and variances, and analyze how these rankings depend on season, variable, reference data set, and climate network type.