ALBERT

Note: Inhaltsverzeichnis: 1. Einleitung. - 2. Atmosphärische Gleichungssysteme. - 2.1. Zur Notation. - 2.2. Geometrie im β-Kanal. - 2.3. Gleichungen in Flussform. - 2.4. Euler-Gleichungen. - 2.4.1. Energiegleichung. - 2.4.2. Bewegungsgleichungen. - 2.4.3. Flussform des gesamten Gleichungssystems. - 2.4.4. Schallgeschwindigkeit. - 2.4.5. Druck und Energie. - 2.4.6. Energie als Erhaltungsvariable. - 2.5. Euler-Gleichungen mit Referenzfeld. - 2.6. Linearisierte Euler-Gleichungen. - 2.7. Flachwassergleichungen. - 2.8. Flachwasseräquivalente Dynamik mit Euler-Gleichungen. - 3. Unstetiges Galerkin-Verfahren. - 3.1. Räumliche Diskretisierung. - 3.1.1. Integralform und numerischer Fluss. - 3.1.2. Koeffizientendarstellung der Gleichungen. - 3.1.3. Koordinatentransformation mit Orographie. - 3.1.4. Quadratur. - 3.1.5. Basisfunktionen im Rechteckgitter. - 3.1.6. Diskretisierung von analytischen Anfangsbedingungen. - 3.2. Zeitliche Diskretisierung. - 3.2.1. Expliziter Zeitschritt. - 3.2.2. Semi-impliziter Zeitschritt. - 3.2.3. Skalierung von Einheiten. - 3.2.4. Zeitschrittbestimmung. - 3.3. Randbedingungen. - 3.3.1. Periodische Randbedingungen. - 3.3.2. Reflektive Randbedingungen. - 3.3.3. Spezifische Randbedingungen für Euler-Gleichungen. - 3.3.4. Absorptionsschicht. - 3.4. Diffusion. - 4. Atmosphärische Gleichgewichtszustände. - 4.1. Anforderungen an stationäre Zustände. - 4.1.1. Verschwindende Advektion von Masse und potentieller Temperatur. - 4.1.2. Stationäre Impulsgleichung. - 4.2. Wind ohne Corioliskraft. - 4.3. Geostrophischer Wind. - 4.4. Vorgegebener Grundstrom mit einstellbarer Baroklinität. - 4.4.1. Lösungsalgorithmus. - 4.4.2. Zulässige Windfelder und ihre Definition außerhalb des Modellgebietes. - 4.4.3. Spezialfall konstanten thermischen Windes. - 4.5. Barotroper Grundstrom als analytischer Spezialfall. - 4.6. Charakterisierung der Baroklinität. - 4.7. Geostrophischer Zustand für Flachwassergleichungen. - 5. Numerische Stabilität von Gleichgewichtszuständen und Erhaltungseigenschaften. - 5.1. Polynomiale Balancierung des DG-Verfahrens. - 5.1.1. Ausgangssituation („low0bal0“). - 5.1.2. Isotrope Reduktion des Polynomgrades der Quellterme („low1bal0“). - 5.1.3. Isotrope Polynomgradreduktion von Quelltermen sowie Projektion der Flussfunktion („low1bal1“). - 5.1.4. Volle Balancierung mit selektiver Polynomgradreduktion und Projektion der Flussfunktion („low2bal1“). - 5.2. Konvergenz. - 5.3. Langzeitstabilität und Erhaltungseigenschaften. - 6. Atmosphärische Testfälle. - 6.1. Aufsteigende warme Blase. - 6.2. Schwerewellen. - 6.3. Bergüberströmung. - 6.4. Barotrope Instabilität. - 7. Atmosphärische Instabilitäten in mittleren Breiten. - 7.1. Barotrope Instabilität mit Euler-Gleichungen in 2D und 3D. - 7.1.1. Wavelet-Spektrum. - 7.2. Barokline Instabilität in Abhängigkeit von statischer Stabilität und thermischem Wind. - 7.2.1. Einfluss der statischen Stabilität. - 7.2.2. Einfluss der vertikalen Diskretisierung. - 7.3. Entstehung zyklonaler Wirbel aus baroklin instabilem Grundstrom. - 7.3.1. Konfiguration. - 7.3.2. Entwicklung von Impulsdifferenz. - 7.3.3. Vorticity im Horizontalschnitt. - 7.3.4. Globale Charakterisierung . - 7.4. Langzeitentwicklung aus baroklinen Zuständen. - 7.4.1. Konfiguration. - 7.4.2. Entwicklung von Impulsdifferenz und Energie. - 7.4.3. Vorticity im Horizontalschnitt. - 7.4.4 Globale Charakterisierung. - 7.4.5. Wavelet-Spektrum. - 7.4.6. Zonales Mittel. - 8. Zusammenfassung und Ausblick. - A. Mathematische Aspekte. - A.1. Profilfunktionen. - A.2. Differenzen und Normen. - A.3. Wavelet-Analyse. - A.4. Darstellung aus der Diskretisierung. - A.5. Erhaltungseigenschaften mit Quadratur. - B. Details zu Euler-Gleichungen. - B.1. Vertikale Linearisierung der Euler-Gleichungen für Präkonditionierer des semi-impliziten Zeitschrittes. - B.1.1. Vertikales lineares Gleichungssystem. - B.1.2. Diskretisierung und Matrizen. - B.1.3. Implizites Gleichungssystem. - B.2. Zustände im hydrostatischen Gleichgewicht. - B.2.1. Isotherm. - B.2.2. Polytrop. - B.2.3. Isentrop. - B.2.4. Mehrfach polytrop. - B.2.5. Uniform geschichtet. - B.3. Barokliner Zustand imp-System. - C. Zusätzliche Simulationsdaten. - C.1. Stabilitätskarten zu baroklinen Langzeitsimulationen. - C.2. Wirbelentstehung nahe Oberrand. - C.3. Zusätzliche Horizontalschnitte des baroklinen Langzeitlaufes. - D. Implementierung: Programmpaket Polyflux. - E. Korrekturen zur Veröffentlichung. - Mathematische Definitionen. - Abkürzungen und Begriffe. - Literatur.

Note: Contents Summary 1. Introduction 1.1 Background 1.1.1 Precipitation changes 1.1.2 Large-scale atmospheric patterns 1.2 Objectives and research questions 1.3 Thesis outline and author contribution High spatial and temporal organization of changes inprecipitation over Germany for 1951–2006 2.1 Introduction 2.2 Data 2.3 Methods 2.3.1 Threshold between wet and dry days 2.3.2 Derivation of time series of precipitation characteristics 2.3.3 Trend analyses under consideration of temporal and spatial correlation 2.3.4 Visualization of results 2.4 Results and discussion 2.4.1 Changes in total precipitation 2.4.2 Changes in mean, variability, and heavy precipitation indicators 2.4.3 Transition probabilities 2.4.4 Seven-day precipitation amount with return period 100 years 2.5 Conclusions Can local climate variability be explained by weatherpatterns? A multi-station evaluation for the Rhine basin 3.1 Introduction 3.2 Data 3.3 Methods 3.3.1 Weather pattern classification 3.3.2 Finding optimal classification parameters 3.3.3 Evaluation of classifications 3.4 Results 3.4.1 Stratification of local climate variables 3.4.2 Performance of GCMs 3.5 Discussion 3.5.1 On the optimal classification 3.5.2 On the skill of GCMs 3.6 Conclusions 3.7 Data availability 3.A Appendix Do changing weather types explain observed climatictrends in the Rhine basin? An analysis of within andbetween-type changes 4.1 Introduction 4.2 Data and weather pattern classification 4.3 Methods 4.3.1 Relationship of WPs and large-scale circulation modes 4.3.2 Trend detection methods 4.3.3 Relative share of between- and within-type changes 4.4 Results 4.4.1 Attribution of WPs to large-scale circulation modes 4.4. 2Between-Type Changes 4.4.3 Within-Type Changes 4.4.4 Relative share of between- and within-type changes 4.5 Discussion and conclusions 4.A Appendix 4.S Supplementary Discussion and conclusions 5.1 Main results 5.2 Discussion and directions for further research 5.2.1 Weather pattern classification for downscaling 5.2.2 Limitations for downscaling 5.3 Concluding remarks Bibliography

Note: TABLE OF CONTENTS Abstract Kurzfassung Table of Contents List of Figures List of Tables List of Abbreviations List of Symbols 1 INTRODUCTION 1.1 Background and Scientific Setting 1.2 Motivation and Research Questions 1.3 Structure of Thesis 2 FUNDAMENTALS OF HYPERSPECTRAL AND SPECTRO-DIRECTIONAL REMOTE SENSING 2.1 Hyperspectral Remote Sensing of Vegetation 2.2 Spectro-Directional Remote Sensing of Vegetation 2.3 The EnMAP Satellite System 2.4 Spectro-Goniometer Systems for the Ground-Based Measurement of BRDF Effects 3 THE TUNDRA PERMAFROST STUDY LOCATIONS AND THEIR ENVIRONMENT 3.1 The Eurasia Arctic Transect (EAT) 3.1.1 Geological and Climatic Setting 3.1.2 Vegetation 3.2 The North American Arctic Transect (NAAT) 3.2.1 Geological and Climatic Setting 3.2.2 Vegetation 4 OBSERVATIONS AND METHODOLOGY 4.1 Observations Used for this Study 4.1.1 The ECI-GOA-Yamal 2011 Expedition 4.1.2 The EyeSight- NAAT-Alaska 2012 Expedition 4.1.3 Data Used for Hyperspectral Characterization of Arctic Tundra 4.1.4 Data Used for Spectro-Directional Characterization of Arctic Tundra 4.2 Methodology Used for Field Work and Data Analysis 4.2.1 Field Spectroscopy and Hyperspectral Data Analysis 4.2.2 Considerations for the Field Spectro-Goniometer Measurements and the Spectro-Directional Data Analysis 5 DEVELOPMENT AND PRECOMMISSIONING INSPECTION OF THE MANTIS FIELD SPECTRO-GONIOMETER 5.1 Introduction 5.2 Theoretical Background 5.3 Description of the Field Spectro-Goniometer System 5.3.1 Construction Schedule 5.3.2 Description of the Field Spectro-Goniometer Platform (ManTIS) 5.3.3 Sensor Configuration of the AWI ManTIS Field Spectro-Goniometer 5.3.4 Measurement Strategy 5.3.5 Software for Semi-Automatic Control 5.4 Error Assessment 5.4.1 Radiometrical Accuracy 5.4.2 Pointing Accuracy 5.4.3 Ground Instantaneous Field of View and Sensor Self-Shadowing 5.4.4 Temporal Illumination Changes and Environmental Influences 5.5 Data Analysis 5.5.1 Data Processing 5.5.2 Data Visualization 5.6 Performance of ManTIS Field Spectro-Goniometer in the Field 5.6.1 Test Site and Experiment Setup 5.6.2 Results and Discussion 5.7 Conclusions and Outlook 6 HYPERSPECTRAL REFLECTANCE CHARACTERIZATION OF LOW ARCTIC TUNDRA VEGETATION 6.1 Introduction 6.2 Material & Methods 6.2.1 Study Area 6.2.2 Environmental Gradients/Zones and Vegetation Description 6.2.3 Data Acquisition and Pre-Processing 6.2.4 Data Analysis 6.3 Results 6.3.1 The Zonal Climate Gradient 6.3.2 Acidic Versus Non-Acidic Tundra (Soil pH Zones) 6.3.3 The Toposequence at Happy Valley (Subzone E) 6.3.4 The Soil Moisture Gradient at Franklin Bluffs (Subzone D) 6.4 Discussion 6.4.1 Overview of Field Characterization and Spectral Properties along the Gradients 6.4.2 Performance of Spectral Metrics and Vegetation Indices 6.5 Conclusions 7 RESULTS OF THE SPECTRO-DIRECTIONAL REFLECTANCE INVESTIGATIONS 7.1 Overview of the Spectro-Directional Reflectance Characteristics of Low Arctic Tundra Vegetation 7.1.1 Representativeness of the Study Plots Representing Tundra Vegetation 7.1.2 Vaskiny Dachi – Bioclimate Subzone D 7.1.3 Happy Valley – Bioclimate Subzone E 7.1.4 Franklin Bluffs – Bioclimate Subzone D 7.2 Influence of High Sun Zenith Angles on the Reflectance Anisotropy 7.2.1 MAT (Happy Valley) 7.2.2 MNT (Franklin Bluffs) 7.3 Variability in Multi-Angular Remote Sensing Products of Low Arctic Tundra Environments 7.3.1 Spectro-Directional Variability of Different Low Arctic Plant Communities 7.3.2 Spectro-Directional Variability under Varying Sun Zenith Angles 8 DISCUSSION 8.1 The Hyperspectral Reflectance Characteristics of Tundra Vegetation in Context of the Spectro-Goniometer Measurements 8.2 Applicability of the ManTIS Field Spectro-Goniometer System 8.3 The Spectro-Directional Reflectance Characteristics of Tundra Vegetation 8.4 Variability in Reflectance Anisotropy at High Sun Zenith Angles 8.5 Applicability of Multi- Angular Remote Sensing Products for Arctic Tundra Environments 9 CONCLUSIONS & OUTLOOK Acknowledgments References Appendix Table of Contents of the Appendix References of the Appendix Statutory Declaration / Eidesstattliche Erklärung

Note: Table of Contents Abstract Kurzfassung Abbreviations and nomenclature 1. Introduction 2. Scientific Background 2.1. Permafrost 2.2.Retrogressive Thaw Slumps 2.3. Inputs of Freshwater, Sediment and Carbon into the Canadian Beaufort Sea 3. Study Area 3.1. Regional Setting: Yukon Coast and Herschel Island 3.2. Retrogressive Thaw Slumps 4. Material and Methods 4.1. Field Work 4.1.1. Terrain Photography 4.1.2. Differential Global Positioning System (DGPS) 4.1.3. Light Detection And Ranging (LiDAR) and Digital Elevation Model (DEM) 4.1.4. Micrometeorology 4.1.5. Discharge Measurement 4.1.6. Multiple Regression-Statistical Relationships between Micrometeorological Variables and Discharge 4.1.7. Sampling 4.2. Laboratory Analyses 4.2.1. Sedimentological Analyses 4.2.2. Hydrochemical Analyses 4.3. Fluxes of Sediment and (In-) Organic Matter 5. Results 5.1. Field Work 5.1.1. Terrain Photography 5.1.2. Differential Global Positioning System (DGPS) 5.1.3. Light Detecting And Ranging (LiDAR) and Digital Elevation Model (DEM) 5.1.4. Micrometeorology 5.1.5. Discharge 5.1.6. Multiple Regression - Statistical Relationships between Micrometeorology and Discharge 5.2. Laboratory Analyses 5.2.1. Sedimentological Analyses 5.2.2. Hydrochemical Analyses 5.3. Fluxes of Sediment-meltwater 6. Discussion 6.1. Microclimatological and Geomorphological Factors Controlling Discharge 6.1.1. Diurnal Variations 6.1.2. Seasonal Variations 6.2. Contribution of Retrogressive Thaw Slumps to the Sediment Budget of the Yukon Coast 6.2.1. Origin of Outflow Material 6.2.2. Slump D in the Regional Context 6.2.3. Seasonal Sediment Budget Compilation for Slump D 6.2.4. Retrogressive Thaw Slump Occurrence along the Yukon Coast 6.2.5. Input to the Beaufort Sea 6.3. Projected Climatic Change and its Impact on Retrogressive Thaw Slump Outflow 6.4. Uncertainties and Limitations 6.5. Future Research 7. Conclusion 8. Appendix 8.1. Field Work 8.1.1. Slump D's northern headwall profile 8.1.2. Collinson Head slump 8.1.3. Herschel Island West Coast slump 8.1.4. Roland Bay slump 8.1.5. Kay Point slump 8.2. Laboratory Work 8.2.1. Volumetric Ice Content 8.2.2. Grain Size 8.3. Evolution of Slump D 8.3.1. Geo Eye satellite of Slump D 8.3.2. Aerial Oblique Photography of Slump D 8.3.3. LiDAR of Slump D 8.3.4. Time Lapse Photography of Slump D's Headwall 9. References 10. Financial and technical support 11. Acknowledgement - Danksagung

Note: Dissertation, Universität Potsdam, 2019 , Table of contents Abstract Zusammenfassung Abbreviations 1 Introduction 1.1 Scientific background 1.1.1 Permafrost in the Northern Hemisphere 1.1.2 The permafrost carbon climate feedback 1.1.3 Rapidly changing, deep permafrost environments 1.2 Aims of this dissertation 1.3 Investigated study areas 1.4 Basic method overview 1.4.1 Field work in the Arctic 1.4.2 Laboratory procedure 1.4.3 Analysis ofl andscape-scale carbon and nitrogen stocks 1.5 Thesis organization 1.6 Overview of publications 1.6.1 Publication#1 - Yedoma landscape publication 1.6.2 Publication#2 - Thermokarst lake sequence publication 1.6.3 Publication#3 - North Alaska Arctic river delta publication 1.6.4 Extended Abstract - Western Alaska river delta study 1.6.5 Appendices - Supplementary material and paper in preparation II Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia 2.1 Abstract 2.2 Introduction 2.3 Material and methods 2.3.1 Study area 2.3.2 Field Work 2.3.3 Laboratory analysis 2.3.4 Landform classification and upscaling C and N pools 2.4 Results 2.4.1 Sedimentological results 2.4.2 Sampling site SOC and N stocks 2.4.3 Upscaling: Landscape SOC and N stocks 2.4.4 Radiocarbon dates 2.5 Discussion 2.5.1 Site specific soil organic C and N stock characteristics 2.5.2 Upscaling of C and N pools 2.5.3 Sediment and organic C accumulation rates 2.5.4 Characterizing soil organic carbon 2.5.5 The fate of organic carbon in thermokarst-affected yedoma in Siberia 2.6 Conclusions III Impacts of successive thermokarst lake stages on soil organic matter, Arctic Alaska 3.1 Abstract 3.2 Plain language summary 3.3 Introduction 3.4 Study site 3.5 Methods 3.5.1 Core collection 3.5.2 Biogeochemical analyses 3.5.3 Study area OC and N calculation 3.6 Results 3.6.1 Biogeochemistry 3.6.2 Sediment organic carbon and nitrogen stocks 3.6.3 Radiocarbon dates and carbon accumulation rates 3.6.4 Landscape C and N budget 3.7 Discussion 3.7.1 Impact of thermokarst lake dynamics on organic matter storage 3.7.2 High organic C and N stocks on the ACP 3.7.3 Landscape chronology 3.7.4 Organic matter accumulation 3.7.5 Future development 3.8 Conclusions IV Sedimentary and geochemical characteristics of two small permafrost-dominated Arctic river deltas in northern Alaska 4.1 Abstract 4.2 Introduction 4.3 Study area 4.4 Material and Methods 4.4.1 Soil organic carbon and soil nitrogen storage 4.4.2 Radiocarbon dating and organic carbon accumulation rates 4.4.3 Grain size distribution 4.4.4 Scaling carbon and nitrogen contents to landscape level 4.5 Results 4.5.1 Carbon and nitrogen contents 4.5.2 Radiocarbon dates and accumulation rates 4.5.3 Grain size distribution 4.5.4 Arctic river delta carbon and nitrogen storage 4.6. Discussion 4.6.1 Significance of carbon and nitrogen stocks in Arctic river deltas 4.6.2 SOC and SN distribution with depth 4.6.3 Sedimentary characteristics 4.6.3.1 Accumulation rates 4.6.3.2 Sediment distribution 4.6.4 Impacts of future changes 4.6.5 Significance of remotely sensed upscaling results 4.7 Conclusions V Soil carbon and nitrogen stocks in Arctic river deltas - New data for three Western Alaskan deltas 5.1 Abstract 5.2 Introduction 5.3 Study sites 5.4 Methods 5.5 Results and discussion 5.5 Conclusions VI Discussion 6.1 Interregional comparison 6.2 Changing thermokarst landscapes and their global impact 6.3 A growing C and N data base 6.4 Outlook - potential follow-up projects VII Synthesis VIII References Appendix A Synthesis of SOC and N inventories Appendix B Supplementary material to Chapter II Appendix C Supplementary material to Chapter III Appendix D Supplementary material to Chapter IV Appendix E Supplementary material to Chapter V Appendix F Arctic river delta data set - Version 1.0 Acknowledgements - Danksagung