Exploring physical processes related to past climate proxies [Elektronische Ressource] : lake sediments and stable water isotopes / Stephan Pfahl

Sujets

• Physik

Informations

• Publié par: johannes_gutenberg-universitat_mainz
• Publié le: 01 janvier 2009
• Nombre de lectures: 14
• Langue: English
• Poids de l'ouvrage: 9 Mo

Extrait

Exploring physical processes related to
past climate proxies: lake sediments and
stable water isotopes
Dissertation
zur Erlangung des Grades
“Doktor der
Naturwissenschaften”
am Fachbereich Physik, Mathematik und Informatik
der Johannes Gutenberg-Universita¨t
in Mainz
STEPHAN PFAHL
geb. am 21. Februar 1980
in Bad Schwalbach
Mainz, Juni 2009ii
Tag der Promotion: 1. Oktober 2009
D77 - Mainzer DissertationContents
Abstract vii
Zusammenfassung ix
1 Introduction and Objectives 1
1.1 Proxy data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Lake sediments and extreme weather events . . . . . . . . . . . . . . . . . . . 4
1.3 Stable water isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Climate and Weather in the Western German Eifel Region 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 Station data from the German Weather Service . . . . . . . . . . . . . 13
2.2.2 Reanalysis data from the ECMWF . . . . . . . . . . . . . . . . . . . . 17
2.2.3 Hydrological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Average Eifel climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Seasonal variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 Spatial variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Statistical comparison of measurement and reanalysis data . . . . . . . . . . . 26
2.4.1 Time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4.2 Probability density functions and quantiles . . . . . . . . . . . . . . . 30
2.5 Hydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5.1 Meteorological droughts . . . . . . . . . . . . . . . . . . . . . . . . . 36
iii2.5.2 River gauges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.6 Spatial correlation of extreme events . . . . . . . . . . . . . . . . . . . . . . . 43
2.7 Proxy calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3 A new Windstorm Proxy from Lake Sediments 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2 Sediment data and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.2.1 Sediment core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.2.2 Age model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.3 Sedimentological windstorm identification . . . . . . . . . . . . . . . 56
3.3 Meteorological windstorm identification . . . . . . . . . . . . . . . . . . . . . 61
3.3.1 Construction of a windstorm index from meteorological data . . . . . . 61
3.3.2 Allocation of windstorms to peaks in the silt curves . . . . . . . . . . . 63
3.3.3 Different peak allocations . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3.4 Grain transport within the lake . . . . . . . . . . . . . . . . . . . . . . 67
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 Air Parcel Trajectory Analysis of Stable Isotopes in Water Vapor in the Eastern
Mediterranean 71
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.1.1 Non-equilibrium fractionation and deuterium excess . . . . . . . . . . 71
4.1.2 Interpretation of atmospheric stable isotope data . . . . . . . . . . . . 73
4.2 Data and method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2.1 Measurements of isotopes in water vapor . . . . . . . . . . . . . . . . 74
4.2.2 Moisture source diagnostic . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2.3 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.3 Results and interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.3.1 Correlation analysis of d-excess with meteorological parameters . . . . 81
4.3.2 Correlation analysis of oxygen and deuterium isotopes . . . . . . . . . 86
4.3.3 Sensitivity of the results to parameter settings in the analysis . . . . . . 88
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
iv4.4.1 Quantitative comparison with other d-excess data . . . . . . . . . . . . 92
4.4.2 Implications for the interpretation of d as temperature proxy . . . . . . 93
4.4.3 Modeling d-excess . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.4.4 Methodical error sources . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5 Lagrangian Modeling of Stable Isotopes in Water Vapor 99
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.2 Data and method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.4.1 General implications and limitations of the approach . . . . . . . . . . 110
5.4.2 Comparison with GCM data and results from other studies . . . . . . . 113
5.4.3 Influence of wind velocity . . . . . . . . . . . . . . . . . . . . . . . . 114
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6 Water Isotopes in the COSMO Model 119
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
6.1.2 Implementation approach . . . . . . . . . . . . . . . . . . . . . . . . . 120
6.2 Water tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.2.1 General aspects of the COSMO model and the tagging implementation 122
6.2.2 Tracer advection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
6.2.3 Turbulent transport and evaporation from the ocean . . . . . . . . . . . 126
6.2.4 Boundary relaxation and Rayleigh damping . . . . . . . . . . . . . . . 128
6.2.5 Cloud microphysics . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6.2.6 Convection parameterization . . . . . . . . . . . . . . . . . . . . . . . 132
6.2.7 Tracer synchronization and diagnostics . . . . . . . . . . . . . . . . . 134
6.2.8 Mass conservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.3 Isotope fractionation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
v6.4 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.4.1 Characterization of the example cases and setup of the simulations . . . 142
6.4.2 Simulated meteorology . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6.4.3 Water tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
6.4.4 Simulated isotope fields . . . . . . . . . . . . . . . . . . . . . . . . . 156
6.4.5 Sensitivity experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 163
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
7 Outlook 167
210A Pb Measurements from the SMf Core 171
B Technical Remarks on COSMO Variables and Namelists 173iso
C Sub-grid Scale Clouds in COSMO 177iso
Danksagung 199
viAbstract
Proxy data are essential for the investigation of climate variability on time scales larger than
the historical meteorological observation period. The potential value of a proxy depends on our
ability to understand and quantify the physical processes that relate the corresponding climate
parameter and the signal in the proxy archive. These processes can be explored under present-
day conditions. In this thesis, both statistical and physical models are applied for their analysis,
focusing on two specific types of proxies, lake sediment data and stable water isotopes.
In the first part of this work, the basis is established for statistically calibrating new proxies from
lake sediments in western Germany. A comprehensive meteorological and hydrological data set
is compiled and statistically analyzed. In this way, meteorological times series are identified that
can be applied for the calibration of various climate proxies. A particular focus is laid on the
investigation of extreme weather events, which have rarely been the objective of paleoclimate
reconstructions so far. In addition, the data set is used for assessing the spatial representativity
of these events. Subsequently, a concrete example of a proxy calibration is presented. Maxima
in the quartz grain concentration from a lake sediment core are compared to recent windstorms.
The latter are identified from the meteorological data with the help of a newly developed wind-
storm index, combining local measurements and reanalysis data. The statistical significance of
the correlation between extreme windstorms and signals in the sediment is verified with the help
of a Monte Carlo method. This correlation is fundamental for employing lake sediment data as
a new proxy to reconstruct windstorm records of the geological past.
The second part of this thesis deals with the analysis and simulation of stable water isotopes
in atmospheric vapor on daily time