Reconstruction of possible realisations of the Late Glacial and Holocene near surface climate in Central Europe [Elektronische Ressource] / vorgelegt von Daniel Simonis

Daniel Simonis

Informations

• Publié par: rheinische_friedrich-wilhelms-universitat_bonn
• Publié le: 01 janvier 2010
• Nombre de lectures: 4
• Langue: English
• Poids de l'ouvrage: 9 Mo

Extrait

Reconstruction of possible realisations of the Late
Glacial and Holocene near surface climate in Central
Europe
Dissertation
zur
Erlangung des Doktorgrades (Dr. rer. nat.)
der
Mathematisch-Naturwissenschaftlichen Fakultat¨
der
Rheinischen Friedrich-Wilhelms-Universitat Bonn¨
vorgelegt von
Daniel Simonis
aus
Sigmaringen
Bonn, Juli 20091
AngefertigtmitGenehmigungderMathematisch-Naturwissenschaftlichen
Fakult¨at der Rheinischen Friedrich-Wilhelms-Universita¨t Bonn
1. Gutachter: Prof. Dr. Thomas Litt
2. Gutachter: Prof. Dr. Andreas Hense
Tag der Promotion: 18.12.2009
Erscheinungsjahr: 2010
Hiermit versichere ich, dass ich die vorliegende Arbeit selbststandig verfasst,¨
keine anderen als die angegebenen Quellen und Hilfsmittel benutzt sowie
Zitate kenntlich gemacht habe.Abstract
This thesis presents several aspects of reconstructing physical consistent re-
alisations of past climatological fields. Local climate reconstructions are ob-
tained by a method, which is based on the idea of indicator taxa and uses
presenceoftaxaasproxyvariable. Inpreviousstudies, theindicatortaxaap-
proachhasbeenenhancedtoaprobabilisticBayesianreconstructionmethod,
whichprovidesconditionalprobabilitydensityfunctionsasreconstructionre-
sult.
Up to now bivariate normal distributions or mixture models have been ap-
pliedforreconstructingJanuaryandJulytemperatures. Athreedimensional
copula approach exists for the incorporation of annual precipitation. Now
mixture models are embedded into this approach and a new set of three di-
mensionaltransferfunctionsisestimated. Thedifferencestotwodimensional
mixture models are examined.
The local climate reconstruction results are interpolated in a dynamically
consistent way by applying a variational analysis with weak physical con-
straint. For reconstructing fields of annual precipitation, a different physical
constraint is implemented into the analysis.
A new point of view for the interpretation of climate reconstruction results
is proposed. It emphasizes, that the analysis result has to be seen as con-
ditional expectation of the desired climatological field. This expectation is
only the mean of all possible realisations of the past climate. In this work,
possible realisations are presented and it becomes clear, that these can differ
considerably from the mean field. The realisations are obtained by resam-
pling from the analysis error covariance matrix of the variational analysis.
Reconstructions of near surface January and July temperature anomalies for
two Late Glacial (13000 and 12000 cal. BP) and two Holocene (8000 and
6000 cal. BP) time slices are provided, based on pollen and macrofossil data
from 85 different locationsin Europe. The variational analysis is for the first
time applied for reconstructing a cold climate state. It becomes clear that
the sensitivity of the botanical proxies, which are used in this work, is too
low for capturing the difference between 13000 and 12000 BP. Both time
slices are reconstructed significantly colder than the Holocene. The results
for the Holocene time slices agree well with results from other studies. No
significant differences to the modern 1961-90 climate can be found.
A successful reconstruction of fields of annual precipitation anomalies is not
possible. Apparently the botanical proxies, applied in this work, are not
sensitive enough for this purpose. Both, the results for the Late Glacial and2
the results for annual precipitation call for the incorporation of other proxy
data and a multiproxy approach.Contents
1 Introduction 3
1.1 A brief overview of the Quaternary climate . . . . . . . . . . . 4
1.2 Methods for climate reconstructions . . . . . . . . . . . . . . . 6
1.3 Fundamentals and motivation for this work . . . . . . . . . . . 8
2 Transfer functions and local reconstructions 13
2.1 Important distributions and statistics . . . . . . . . . . . . . . 13
2.1.1 The normal distribution . . . . . . . . . . . . . . . . . 13
2.1.2 The gamma distribution . . . . . . . . . . . . . . . . . 14
2.1.3 Mixture models . . . . . . . . . . . . . . . . . . . . . . 14
2.1.4 Distributions with mixed marginals . . . . . . . . . . . 15
2.2 Estimation of statistical transfer functions . . . . . . . . . . . 17
2.2.1 The need for statistics . . . . . . . . . . . . . . . . . . 17
2.2.2 Definition of transfer functions . . . . . . . . . . . . . . 19
2.2.3 Mixture models as transfer functions . . . . . . . . . . 21
2.2.4 The optimal number of components . . . . . . . . . . . 22
2.3 Local climate reconstructions . . . . . . . . . . . . . . . . . . 27
3 Reconstruction of fields 31
3.1 Variational Analysis . . . . . . . . . . . . . . . . . . . . . . . 32
3.1.1 Specification of the cost function . . . . . . . . . . . . 33
3.1.2 Vegetational costs . . . . . . . . . . . . . . . . . . . . . 34
3.1.3 Advection-diffusion model . . . . . . . . . . . . . . . . 35
3.1.4 A constraint for precipitation . . . . . . . . . . . . . . 38
3.2 Discretisation of the analysis . . . . . . . . . . . . . . . . . . . 40
3.3 Reducing the dimension . . . . . . . . . . . . . . . . . . . . . 48
3.4 The analysis error . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.4.1 Resampling from the analysis error covariance matrix . 53CONTENTS 2
4 Data 55
4.1 Data for estimating transfer functions . . . . . . . . . . . . . . 55
4.1.1 Modern vegetation data . . . . . . . . . . . . . . . . . 55
4.1.2 Climatological data . . . . . . . . . . . . . . . . . . . . 57
4.2 Data for climate reconstructions . . . . . . . . . . . . . . . . . 61
4.2.1 Paleobotanical data . . . . . . . . . . . . . . . . . . . . 61
4.2.2 Solar insolation . . . . . . . . . . . . . . . . . . . . . . 65
5 Results 67
5.1 Transfer functions . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.1.1 Differences between 3d and 2d . . . . . . . . . . . . . . 70
5.1.2 The smoothing criterion . . . . . . . . . . . . . . . . . 71
5.2 Analysis areas . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.3 Reconstruction results . . . . . . . . . . . . . . . . . . . . . . 75
5.3.1 Important aspects for the analysis . . . . . . . . . . . . 75
5.3.2 Reconstruction of the modern climate . . . . . . . . . . 76
5.3.3 Results for 13000 BP . . . . . . . . . . . . . . . . . . . 83
5.3.4 Results for 12000 BP . . . . . . . . . . . . . . . . . . . 89
5.3.5 Results for 8000 BP - July . . . . . . . . . . . . . . . . 94
5.3.6 Results for 8000 BP - January . . . . . . . . . . . . . . 97
5.3.7 Results for 6000 BP . . . . . . . . . . . . . . . . . . . 100
6 Discussion 102
6.1 Comparison of the different time slices . . . . . . . . . . . . . 102
6.2 Missing difference between Alleroed and
Younger Dryas . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2.1 Would a reconstruction of -20 to -30 K be possible? . . 106
6.3 The Holocene results . . . . . . . . . . . . . . . . . . . . . . . 107
6.3.1 High costs in Southern Europe. . . . . . . . . . . . . . 109
6.4 The problem of reconstructing precipitation . . . . . . . . . . 110
7 Concluding remarks 114
7.1 Summary of important results . . . . . . . . . . . . . . . . . . 114
7.2 Suggestions for future research . . . . . . . . . . . . . . . . . . 117
A Additional figures 120
B Fossil sites and present taxa 131
List of abbreviations 152Chapter 1
Introduction
The period of the early 21st century is a very interesting one in the field
of climatology. Most likely due to the increase of greenhouse gases, mainly
CO , the climate system has already warmed significantly and is expected2
to reach a temperature level out of the range mankind has ever experienced.
Thisstatementwouldneverbepossiblewithoutpaleoclimatologicalresearch.
Instrumental measurements of climate parameters only reach back to 1850
(Brohan et al., 2006). For receiving information about the climate beyond
that date, the field of paleoclimatology was created.
It was stated in the fourth assessment report of the Intergovernmental Panel
on Climate Change (IPCC), that in 1990 “...many climatic variations prior
to the instrumental record were not that well known or understood. Fifteen
years later, understanding is much improved, more quantitative and better
integrated with respect to observations and modelling” (Jansen et al., 2007).
In these recent years the knowledge has improved a lot, concerning the vari-
ability of the past climate. However, there still is a large uncertainty in
answers to questions as e.g. what were the absolute differences in the global
temperature between maxima of glaciation and deglaciation or what was the
regional impact of these transitions of the climate state.
Especially the role of internal climate variability is a key topic of current
research. The fact that the global mean temperature increased strongly in
the 1990th but nearly remained constant during the first decade of the 21st
century, raised the question how large the influence of internal variability
in a warming climate is (Easterling and Wehner, 2009; Swanson and Tso-
nis, 2009). It is also a matter of discussion if climate anomalies or cycles
were driven by external forcing or internal variability. Examples for that are
the Little Ice Age and Medieval Climate Anomaly (Trouet et al., 2009) or
Dansgaard-Oeschger events during glacial