UID:
kobvindex_GFZ1030123993
Format:
xxi, 197 Seiten
,
Illustrationen, Diagramme
Content:
Earth's climate varies continuously across space and time, but humankind has witnessed only a small snapshot of its entire history, and instrumentally documented it for a mere 200 years. Our knowledge of past climate changes is therefore almost exclusively based on indirect proxy data, i.e. on indicators which are sensitive to changes in climatic variables and stored in environmental archives. Extracting the data from these archives allows retrieval of the information from earlier times. Obtaining accurate proxy information is a key means to test model predictions of the past climate, and only after such validation can the models be used to reliably forecast future changes in our warming world. The polar ice sheets of Greenland and Antarctica are one major climate archive, which record information about local air temperatures by means of the isotopic composition of the water molecules embedded in the ice. However, this temperature proxy is, as any indirect climate data, not a perfect recorder of past climatic variations. Apart from local air temperatures, a multitude of other processes affect the mean and variability of the isotopic data, which hinders their direct interpretation in terms of climate variations. This applies especially to regions with little annual accumulation of snow, such as the Antarctic Plateau. While these areas in principle allow for the extraction of isotope records reaching far back in time, a strong corruption of the temperature signal originally encoded in the isotopic data of the snow is expected. This dissertation uses observational isotope data from Antarctica, focussing especially on the East Antarctic low-accumulation area around the Kohnen Station ice-core drilling site, together with statistical and physical methods, to improve our understanding of the spatial and temporal isotope variability across different scales, and thus to enhance the applicability of the proxy for estimating past temperature variability. The presented results lead to a quantitative explanation of the local-scale (1–500 m) spatial variability in the form of a statistical noise model, and reveal the main source of the temporal variability to be the mixture of a climatic seasonal cycle in temperature and the effect of diffusional smoothing acting on temporally uncorrelated noise. These findings put significant limits on the representativity of single isotope records in terms of local air temperature, and impact the interpretation of apparent cyclicalities in the records. Furthermore, to extend the analyses to larger scales, the timescale-dependency of observed Holocene isotope variability is studied. This offers a deeper understanding of the nature of the variations, and is crucial for unravelling the embedded true temperature variability over a wide range of timescales.
Note:
Kumulative Dissertation
,
Dissertation Universität Potsdam 2018
,
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.
Language:
English
Keywords:
Hochschulschrift
URL:
https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-414963
Author information:
Laepple, Thomas
Bookmarklink
