ALBERT

Note: Table of contents Abstract Zusammenfassung Abbreviations and Nomenclature 1. Introduction 1.1 Scientific Background 1.1.1 Climate and Permafrost 1.1.2 Remote Sensing 1.1.3 Research Questions 1.2 General Approach 1.3 Thesis Structure 1.4 Author’ s contributions 1.4.1 Chapter 2 1.4.2 Chapter 3 1.4.3 Chapter 4 1.4.4 Chapter 5 1.4.5 Appendix Paper 1 2. Detection of landscape dynamics in the Arctic Lena Delta withtemporally dense Landsat time-series Stacks 2.1 Abstract 2.2 Introduction 2.3 Study Area and Data 2.3.1 Study Area 2.3.2 Data 2.3.3 Methods/processing 2.4 Results 2.4.1 Regional Scale changes 2.4.2 Local scale changes 2.5 Discussion 2.5.1 Regional scale changes 2.5.2 Local scale changes 2.5.3 Data quality 2.5.4 Data usage and outlook 2.6 Conclusion 2.7 Data Archive 2.8 Acknowledgements 2.9 Appendix A. Supplementary Data 3. Landsat-Based Trend Analysis of Lake Dynamics across NorthernPermafrost Regions 3.1 Abstract 3.2 Introduction 3.3 Study Sites 3.3.1 Alaska North Slope (NSL) 3.3.2 Alaska Kobuk-Selawik Lowlands (AKS) 3.3.3 Central Yakutia (CYA) 3.3.4 Kolyma Lowland (KOL) 3.4 Data and Methods 3.4.1 Data and Trend Analysis 3.4.2 Pixel-Based Machine-Leaming Classification 3.4.3 Object-Based Image Analysis 3.4.4 Data Quality and Post-Processing 3.4.5 Calculation of Lake Change Statistics 3.5 Results 3.5.1 NSL (Alaska North Slope) 3.5.2 AKS (Alaska Kobuk-Selawik Lowlands) 3.5.3 CYA (Central Yakutia) 3.5.4 KOL (Kolyma Lowland) 3.6 Discussion 3.6.1 Data Analysis 3.6.2 Comparison of Sites and Prior Studies 3.7 Conclusions 3.8 Supplementary Materials 3.9 Acknowledgements 3.10 Appendix A 4. Remotely sensing recent permafrost region disturbances across Arcticto Subarctic transects 4.1 Abstract 4.2 Introduction 4.3 Results 4.3.1 Lakes 4.3.2 Retrogressive Thaw Slumps 4.3.3 Wildfire 4.4 Discussion 4.5 Methods 4.5.1 Remote Sensing Data Processing 4.5.2 Auxiliary Data Sources 5. Tundra landform and Vegetation productivity trend maps for theArctic Coastal Plain of northern Alaska 5.1 Abstract 5.2 Background & Summary 5.3 Methods 5.3.1 Polygonal tundra geomorphology mapping 5.3.2 Image processing 5.3.3 Image Classification 5.3.4 Decadal scale NDVI trend analysis 5.4 Data Records 5.5 Technical Validation 5.5.1 Tundra Geomorphology Map 5.5.2 NDVI Trend Map 5.6 Data Citation 6. Discussion/Synthesis 6.1 Landsat-based trend analysis 6.1.1 Spatial Scale 6.1.2 Time series analysis 6.1.3 Model complexity 6.2 Mapping of permafrost landscape dynamics 6.2.1 Lake dynamics 6.2.2 Wildfire 6.2.3 Retrogressive Thaw Slumps 6.3 Pan-arctic scale distribution and consequences of changes inpermafrost 6.4 Outlook Bibliography A-1. Appendix: Reduced arctic tundra productivity linked with landform and climate change interactions A-1.1 Abstract A-1.2 Introduction A-1.3 Methods A-1.4 Results A-1.5 Discussion Danksagung/Acknowledgements Eidesstattliche Erklärung

Note: Contents: 1 General introduction. - 1.1 Challenges of isotope-based temperature reconstructions. - 1.2 Thesis overview. - 1.3 Author contributions. - 2 Theoretical background. - 2.1 The isotopic composition of firn and ice. - 2.1.1 Fractionation of water isotopologues. - 2.1.2 Relationship with temperature. - 2.1.3 Measuring of the isotopic composition. - 2.2 Processes within the firn column. - 2.2.1 The firn column of polar ice sheets. - 2.2.2 The density of firn. - 2.2.3 The temperature profile of firn. - 2.2.4 Vapour diffusion in firn. - 2.3 Internal climate variability. - 3 Regional climate signal vs.local noise: a two-dimensional view of water isotopes. - 3.1 Introduction. - 3.2 Data and methods. - 3.3 Results. - 3.3.1 Trench isotope records. - 3.3.2 Single-profile representativity. - 3.3.3 Mean trench profiles. - 3.3.4 Spatial correlation structure. - 3.3.5 Statistical noise model. - 3.4 Discussion. - 3.4.1 Local noise vs. regional climate signal. - 3.4.2 Representativity of isotope signals. - 3.4.3 Implications. - 3.5 Conclusions. - 3.6 Appendix A: Derivation of noise model. - 3.6.1 Definitions. - 3.6.2 Derivation of model correlations. - 3.6.3 Estimation of parameters. - 3.7 Appendix B: Noise level after diffusion. - 4 Constraints on post-depositional isotope modifications in east antarctic firn. - 4.1 Introduction. - 4.2 Data and methods. - 4.2.1 Sampling and measurements. - 4.2.2 Trench depth scale. - 4.2.3 Spatial variability of trench profiles. - 4.2.4 Quantification of downward advection, densification and diffusion. - 4.2.5 Statistical tests. - 4.3 Results. - 4.3.1 Comparison of T15 and T13 isotope data. - 4.3.2 Expected isotope profile changes. - 4.3.3 Temporal vs. spatial variability. - 4.4 Discussion. - 4.4.1 Densification, diffusion and stratigraphic noise. - 4.4.2 Additional post-depositional modifications. - 4.5 Conclusions. - 5 On the similarity and apparent cycles of isotope variations. - 5.1 Introduction. - 5.2 Data and Methods. - 5.2.1 Data. - 5.2.2 Spectral analysis. - 5.2.3 Rice’s formula. - 5.2.4 Cycle length and amplitude estimation. - 5.2.5 Model for vertical isotope profiles. - 5.3 Results. - 5.3.1 Spectral analysis of isotope profiles. - 5.3.2 Theoretical and observed cycle length. - 5.3.3 Illustrative examples. - 5.3.4 Depth dependency of cycle length. - 5.3.5 Simulated vs. observed isotope variations. - 5.4 Discussion and summary. - 5.5 Conclusions. - 5.6 Appendix A: Input sensitivity. - 5.7 Appendix B: Additional results. - 5.8 Appendix C: Spectral significance testing. - 6 Timescale-dependency of antarctic isotope variations. - 6.1 Introduction. - 6.2 Data and methods. - 6.2.1 DML and WAIS isotope records. - 6.2.2 Spectral model. - 6.2.3 Timescale-dependent signal-to-noise ratio. - 6.2.4 Effects of diffusion and time uncertainty. - 6.2.5 Present-day temperature decorrelation. - 6.3 Results. - 6.3.1 Illustration of model approach. - 6.3.2 DML and WAIS isotope variability. - 6.4 Discussion. - 6.4.1 Interpretation of noise spectra. - 6.4.2 Interpretation of signal spectra. - 6.4.3 Signal-to-noise ratios. - 6.4.4 Differences between DML and WAIS. - 6.5 Conclusions. - 7 Declining temperature variability from LGM to holocene. - 8 General discussion and conclusions. - 8.1 Short-scale spatial and temporal isotope variability. - 8.1.1 Local spatial variability. - 8.1.2 Seasonal to interannual variability. - 8.1.3 Spatial vs. temporal variability. - 8.2 Extension to longer scales. - 8.2.1 Spatial vs. temporal variability on interannual timescales. - 8.2.2 Holocene and longer timescales. - 8.3 Concluding remarks and outlook. - Bibliography. - A Methods to: declining temperature variability from lgm to holocene. - A.1 Temperature proxy data. - A.2 Model-based temperature and variability change. - A.3 Temperature recalibration of proxy records. - A.3.1 Recalibration of ice-core records. - A.3.2 Recalibration of marine records. - A.4 Variance and variance ratio estimation. - A.5 Noise correction. - A.5.1 Testing effect of noise correction. - A.6 Effect of ecological adaption and bioturbation. - A.7 Effect of proxy sampling locations. - B Layering of surface snow and firn: noise or seasonal signal?. - B.1 Introduction. - B.2 Materials and methods. - B.2.1 Firn-core density profiles. - B.2.2 Trench density profiles. - B.2.3 Dielectric profiling and density estimates. - B.2.4 Comparison of DEP and CT density. - B.2.5 Ion measurements. - B.3 Results. - B.3.1 2-D trench density data. - B.3.2 Spatial correlation structure. - B.3.3 Comparison of mean density, isotope and impurity profiles. - B.3.4 Spectral analysis of vertical density data. - B.4 Discussion. - B.4.1 Spatial variability. - B.4.2 Representativeness of single profiles. - B.4.3 Seasonal cycle in snow density. - B.4.4 Density layering in firn and impurities. - B.5 Conclusions. - Acknowledgements - Danksagung.