ALBERT

Anmerkung: Dissertation, Universität Potsdam, 2023 (publikationsbasierte Dissertation) , CONTENTS 1 Introduction 1.1 Introduction to climate reconstructions 1.1.1 Radiocarbon as a tracer of time 1.1.2 Environmental information stored in snow 1.2 Challenges of climate reconstructions 1.2.1 The particle flux 1.2.2 Modifications after the initial deposition 1.2.3 Sampling and measurement uncertainty 1.3 Objectives and overview of the thesis 1.4 Author contributions to the Manuscripts 2 Age-heterogeneity in marine sediments revealed by three-dimensional high-resolution radio-carbon measurements 2.1 Introduction 2.2 Methods 2.2.1 Study approach 2.2.2 Core setup and sampling 2.2.3 Estimation of the sediment accumulation rate 2.2.4 Estimation of the sediment mixing strength 2.2.5 Estimation of the net sediment displacement 2.2.6 Visual assessment of mixing 2.3 Results 2.3.1 Radiocarbon measurements 2.3.2 Sediment accumulation rate 2.3.3 Sediment mixing estimates 2.3.4 Spatial structure of sediment mixing 2.3.5 Components of age uncertainty 2.4 Discussion 2.4.1 Spatial scale of sediment heterogeneity 2.4.2 Potential implications for palaeo-reconstructions 2.4.3 Suggested 14C measurement strategy 2.5 Conclusions 2.6 Supplementary Material 2.6.1 Supplementary figures and tables 2.6.2 Supplementary table 3 Local-scale deposition of surface snow on the Greenland ice sheet 3.1 Introduction 3.2 Data and methods 3.2.1 Study site 3.2.2 SfM photogrammetry 3.2.3 Additional snow height and snowfall data 3.2.4 Estimation of surface roughness 3.3 Results 3.3.1 Relative snow heights from DEMs 3.3.2 Temporal snow height evolution 3.3.3 Day-to-day variations of snowfall 3.3.4 Changes in surface roughness 3.3.5 Implied internal structure of the snowpack 3.4 Discussion 3.4.1 Changes of surface structures 3.4.2 Implications for proxy data 3.4.3 Implications for snow accumulation 3.4.4 SfM as an efficient monitoring tool 3.5 Conclusions 3.6 Appendix 3.6.1 Additional information 3.6.2 Accuracy estimates and validation 3.6.3 Validation 3.6.4 Overall snow height evolution 3.6.5 Surface roughness 4 A snapshot on the buildup of the stable water isotopic signal in the upper snowpack at east-grip, Geenland ice sheet 4.1 Introduction 4.2 Methods and data 4.2.1 Study site 4.2.2 DEM generation 4.2.3 Isotope measurements 4.2.4 Simulation of the snowpack layering 4.2.5 Expected uncertainty 4.3 Results 4.3.1 Snow height evolution 4.3.2 Mean isotopic records 4.3.3 Combining isotopic data with snow height information 4.3.4 Observed vs. simulated composition 4.3.5 Changes in the isotope signal over time 4.4 Discussion 4.4.1 Evolution of the snow surface 4.4.2 Two-dimensional view of isotopes in snow 4.4.3 Buildup of the snowpack isotopic signal 4.5 Conclusion 5 General discussion and conclusions 5.1 Heterogeneity in sedimentary archives 5.1.1 Quantifying archive-internal heterogeneity 5.1.2 Relation between signal and heterogeneity 5.2 Methods to improve climate reconstructions 5.3 Implications for climate reconstructions 5.4 Concluding remarks Bibliography A the role of sublimation as a driver of climate signals in the water isotope content of surface snow: laboratory and field experimental results A.1 Introduction A.2 Methods A.2.1 Laboratory experimental methods A.2.2 Field experimental methods A.3 Results A.3.1 Laboratory experiments A.3.2 Field experiments A.4 Discussion A.5 Conclusions B Atmosphere-snow exchange explains surface snow isotope variability Acknowledgments Eidesstattliche Erklärung

Anmerkung: 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.

Anmerkung: Dissertation, Universität Potsdam, 2020 , Table of contents Abstract Kurzfassung Table of contents Chapter 1: Introduction 1.1 The challenge of proxy uncertainties 1.2 Aims and approaches 1.3 Thesis outline and author's contributions Chapter 2: Comparing methods for analysing time scale dependent correlations in irregularly sampled time series data 2.1 Abstract 2.2 Introduction 2.3 Methods 2.3.1 Time scale dependency 2.3.2 Irregularity 2.3.3 Surrogate data 2.3.3.1 Construction of surrogate signals 2.3.3.2 Construction of irregular sampling 2.3.4 Evaluation of the estimation methods 2.4 Results 2.4.1 Correlation of red signal - white noise time series 2.4.2 Correlation of white signal - white noise time series 2.5 Discussion 2.5.1 Effect of irregularity and non-simultaneousness in sampling 2.5.2 Choosing the best method 2.5.2.1 Handling irregularity 2.5.2.2 Accounting for time scale dependency 2.5.3 Example application to observed proxy records 2.6 Conclusion 2.7 Computer code availability 2.8 Acknowledgements 2.9 Appendix 2-A. Significance test for time scale dependent correlation estimates Chapter 3: Empirical estimate of the signal content of Holocene temperature proxy records 3.1 Abstract 3.2 Introduction 3.3 Data 3.3,1 Proxy records 3.3.2 Climate model simulations 3.4 Method 3.4.1 Approach and assumptions 3.4.2 Spatial correlation structure of model vs. reanalysis data 3.4.3 Processing steps 3.4.3.1 Estimation of the spatial correlation structure 3.4.3.2 Estimation of the SNRs 3.5 Results 3.5.1 Spatial correlation structure and correlation decay length 3.5.2 SNR estimates 3.6 Discussion 3.6.1 Spatial correlation structure of model simulations 3.6.2 Finite number of proxy records 3.6.3 Proxy-specific recording of climate variables 3.6.4 Time uncertainty and non-climatic components of the proxy signal 3.6.5 Implications and future steps forward 3.7 Conclusion 3.8 Code availability 3.9 Data availability 3.10 Acknowledgements Chapter 4: Testing the consistency of Holocene and Last Glacial Maximum spatial correlations in temperature proxy records 4.1 Abstract 4.2 Introduction 4.3 Data 4.4 Method 4.4.1 Approach and assumptions 4.4.2 Holocene and LGM spatial correlation structure from climate model simulation 4.4.3 Effect of changes in climate variability on the predicted correlations 4.4.4 Effect of changes in time uncertainty on the predicted correlations 4.4.S Estimating the surrogate-based LGM spatial correlation and accounting for parameter uncertainty 4.5 Results 4.6 Discussion 4.6.1 Proxy-specific recording and finite number of records 4.6.2 Time uncertainty of proxy records 4.6.3 Contrary behaviour of U K'37 records 4.6.4 Spatial correlation structure and orbital trends 4.7 Conclusion 4.8 Acknowledgements 4.9 Appendix 4-A. Deriving the effect of a different signal variance on the correlation Chapter 5: Synthesis 5.1 Irregular sampling and time scale dependent correlations 5.2 Spatial correlation structure of proxy records 5.3 Consistency of spatial correlations for different climate states 5.4 Signal content of proxy records 5.5 Concluding remarks and Outlook Chapter A: Supplement of Chapter 3 - Empirical estimate of the signal content of Holocene temperature proxy records A.1 Supplementary Figures A.2 Supplementary Tables Chapter B: Supplement of Chapter 4 - Testing the consistency of Holocene and Last Glacial Maximum spatial correlations of temperature proxy records 8.1 Supplementary Figures 8.2 Supplementary Tables References Danksagung Eidesstattliche Erklärung

Anmerkung: Dissertation, Universität Potsdam, 2021 , Contents List of Figures List of Tables I Preamble 1 Introduction 1.1.1 The Journey from Weather to Climate 1.1.2 The Climate Background 1.1.3 Pollen as Quantitative Indicators of Past Changes 1.2 Overview and Aims of Manuscripts 1.2.1 List of Manuscripts 1.2.2 Short Summaries of the Manuscripts 1.3 Author Contributions to the Manuscripts II Manuscripts 2 Comparing estimation of techniques for temporal Scaling 2.1 Introduction 2.2 Data and Methods 2.2.1 Scaling estimation methods 2.2.2 Evaluation of the estimators 2.2.3 Data 2.3 Results 2.3.1 Effect of Regular and Irregular Sampling 2.3.2 Effect of Time series length 2.3.3 Application to database 2.4 Discussion 2.5 Conclusions 3 Land temperature variability driven by oceans at millennial timescales 4 Variability of surface climate in simulations of past and future 4.1 Introduction 4.2 Data and Method 4.2.1 Model simulations 4.2.2 The Last Glacial Maximum experiment 4.2.3 The mid Holocene experiment (midHolocene) 4.2.4 The warming experiments 1pctCO2 and abrupt4xCO2 4.2.5 Preprocessing of model simulations 4.2.6 Comparisons across the ensemble 4.2.7 Diagnosing variability changes 4.2.8 Changes in precipitation extremes 4.2.9 Timescale-dependence of the variability changes 4.3 Results 4.3.1 Hydrological sensitivity across the ensemble 4.3.2 Changes in local interannual variability 4.3.3 Changes in modes of variability 4.3.4 Circulation patterns underlying extratropical precipitation extremes 4.3.5 Changes in. the spectrum of variability 4.4 Discussion 4.4.1 Changes in climate variability with global mean temperature 4.4.2 Temperature vs. precipitation scaling 4.4.3 Comparison to climate reconstructions and observations 4.4.4 Limitations 4.5 Conclusions 5 Holocene vegetation variability in the Northern Hemisphere 5.1 Introduction 5.2 Data and Methods 5.2.1 Pollen Database 5.2.2 Principal Component Analysis 5.2.3 Timescale-dependent Estimates of Variability 5.2.4 Biome Classification 5.3 Results 5.3.1 General Vegetation Variability Analysis 5.3.2 Comparison of Forested and Open Land Vegetations 5.3.3 Comparison of Broadleaf and Needleleaf Fore ts 5.3.4 Comparison of Temperate and Boreal Coniferous Forests 5.3.5 Comparison of Evergreen and Deciduous Boreal Forests 5.4 Discussion 5.5 Conclusion III Postamble 6 General discussion and conclusion 6.1 Overview 6.2 Timescale-Dependent Estimates of Variability 6.3 Climate and Vegetation Variabilities in the Holocene 6.4 Implications for the 21th Century 6.5 Outlook IV Appendix A Supplementary figures from "Comparing estimation techniques for temporal scaling in paleo-climate timeseries" A.1 Block Average Results A.2 First-Order Correction for the Effect of Interpolation A.3 Change in Bias and Standard Deviation B Methods and supplementary information from "Land temperature variability driven by oceans at millennial timescales" B.1 Methods B.1.1 Reconstructions B.1.2 Significance Testing B.1.3 Testing for Anthropogenic Impacts B.1.4 Instrumental Data B.1.5 Model Data B.1.6 Spectral Estimates B.1.7 Variance Ratios B.1.8 Sub-Decadal Variability Binning B.1.9 Correlation B.1.10 Moran's I B.2 Supplementary Information B.2.1 Tree Ring Data Analysis B.2.2 Energy-Balance Equations B.3 Extended Data Figures C Supplementary figures from "Variability of surface climate in simulations of past and future" D Supplementary figures from "Characterization of holocene vegetation variability in the Northern Hemisphere" Bibliography