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200 CHAPTER 6. FORSCHUNGSSTELLE “RADIOMETRIE” 6.1.10 Chronostratigraphy of the Nasca culture and palaeoclimate reconstruction of the Palpa region (Peru) by AMS- 14 C dating Participating scientist Ingmar Unkel, B. Kromer, G. Wagner, B. Eitel, L. Wacker (Zürich) Abstract Research on the enigmatic Nasca culture in South-Peru is driven by two major questions: when did the Nasca people live, and did changing climate have an influence on the rise and fall of this Pre-Columbian civilisation? AMS- 14 C-dating and investigations in an multi-disciplinary project help to get the first answers. Figure 6.10: (left) Preparation line for AMS- 14 C-targets built at the IUP during this project. (right) A trapezoid, one of the famous Nasca-geoglyphs, in the research area near Palpa (S-Peru). Background The chronology of the Nasca culture, which created the world famous geoglyphs (giant diagrams etched into the desert ground) and spanning from approximately 200 BC to 600 AD, in the plain between the Peruvian Andes and the Pacific Ocean, is presently based almost exclusively on a ceramic typology without any extensive chronometric dating. An absolute chronology of this culture had to be created via radiocarbon dating on organic samples from settlement and tomb relics as well as on organic material derived from geoglyph sites in the Nasca/Palpa region. Furthermore, the question is under investigation, if changing climate had influence on the rise and fall of the Pre-Columbian cultures in South-Peru. Funding BMBF focus programme ”New technologies in humanities”, project No. 03WAX3VP - title: ”Nasca: development and adaptation of archaeometric techniques for the investigation of the cultural history of the Palpa region, S-Peru” Methods and results The main focus of the archaeological investigations was on the Nascaperiod settlement centres near Palpa, Los Molinos und La Mua. In co-operation with the geomorphological investigations within the parent project, palaeoclimatic reconstruction of the re- spective region was undertaken. The material to be dated consisted mainly of loess molluscs and charred plant remains, sampled from river terraces and debris flows which originated in more humid phases in this presently hyper-arid area. During the course of the project a semi-automated AMS-target preparation line was built in the radiocarbon laboratory. Using AMS (Accelerator Mass Spectrometry), dating of extremely small amounts of archaeological material is possible. For the first time in Peruvian archaeology straw fragments found within adobes (clay bricks), which do not provide a sufficient amount of material for conventional measurement, have been dated. The dating of the samples appears to confirm so far the archaeological timeframe of the Nasca culture. The chronology will be completed and statistically analysed towards the end of this year. Some surprising new evidence of climate change has been found in the studies of the river sediments, showing a pronounced wet phase from 15 th to 17 th century in that region. The analysis is still in progress. Outlook/Future work none in this case Main publication in preparation
6.1. RADIOMETRIC DATING OF WATER AND SEDIMENTS 201 6.1.11 Nonlinear time-series analysis of nonstationary signals Participating scientists Pablo Verdes, A. Mangini Abstract The purpose of my research is to develop new tools for the analysis of nonstationary complex systems. I explore them using a natural decomposition into intrinsic dynamics and external component, using a previously developed technique to estimate the latter from measured data. In particular, I investigate how modeling and forecasting can be improved in this framework. Background One of the main goals of Physics has always been prediction. The most satisfactory situation arises when the system of interest can be completely understood and modeled from first principles, so that its equations of motion can be derived and solved for all times. However, real-world dynamical systems have often so many degrees of freedom that the traditional approach proves intractable. In such a case, an alternative path is to measure the evolution of any given system property by recording time series, which are a set of observations collected at regular intervals of time, and then recurr to the theory of time-series analysis. The vast literature on this subject mostly rely on the condition of stationarity, because a solid mathematical foundation can be built in this case. As a result, nonstationary time-series analysis is an almost unexplored branch of science. However, the stationarity condition is very stringent because most real-world time series have some degree of nonstationarity due to external perturbations acting on the observed system. Funding Alexander von Humboldt postdoctoral fellowship. Methods and results I have investigated the problem of modeling and forecasting of nonstationary time series. More precisely, I have studied an over-embedding method that constitutes a general framework for modeling nonstationary systems, basically enlarging the standard time-delay embedding space by inclusion of the unknown slow driving signal. I have developed a variant in which this external driving force can be estimated simultaneously with the intrinsic stationary dynamics. This method is general and can be implemented with any modeling tool; using in particular artificial neural networks, I have found that it is highly efficient both on synthetic and real-world time series. In particular, in noiseless artificial cases this approach is more than one order of magnitude better than the second best method. As a realworld application, I have considered the problem of modeling the sunspot number record. In this case a 30% error reduction was obtained, which is a remarkable number within the vast literature on this benchmark, particularly for an already very competitive modeling methodology like ensembles of artificial neural networks. In the case of nstep-ahead predictions, this approach also leads to much more accurate results and longer prediction horizons than other existing overembedding methods in the literature. We have also studied a time series of temperatures in the Central Alps during the past 2000 years reconstructed from a δ 18 O stalagmite record. The isotopic composition of this stalagmite could be precisely dated and translated into a highlyresolved record of temperature during the last 2 millenia. The variability of this time series was analysed and compared to other reconstructions of past temperatures in the literature, and the role of the solar signal as a possible driver of its dynamics was assessed in comparison with carbon dioxide. The fractions of temperature variance that are explained by each of these forcing factors were analysed in a nonlinear framework, by studying the relevance of the solar and greenhouse gas inputs in a feed-forward neural network model of the temperature behaviour. I have also participated in a time series prediction competition organised by the International Joint Conference on Neural Networks. The goal of this competition was to provide a new benchmark for the problem of time series prediction and to compare the different methods or models that can be used for the prediction. An artificial time series with 5000 data was given and, within those, 100 values were missing and had to be predicted. Selected papers were considered for publication in Neurocomputing. Main publications Verdes et al. [pub.]; Mangini et al. [2005]; Verdes et al. [in press]
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CONTENTS 7 3.2.1 Glaciological pilo
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