TY - THES A1 - Donner, Reik Volker T1 - Advanced methods for analysing and modelling multivariate palaeoclimatic time series T1 - Moderne Verfahren zur Analyse und Modellierung multivariater paläoklimatischer Zeitreihen N2 - The separation of natural and anthropogenically caused climatic changes is an important task of contemporary climate research. For this purpose, a detailed knowledge of the natural variability of the climate during warm stages is a necessary prerequisite. Beside model simulations and historical documents, this knowledge is mostly derived from analyses of so-called climatic proxy data like tree rings or sediment as well as ice cores. In order to be able to appropriately interpret such sources of palaeoclimatic information, suitable approaches of statistical modelling as well as methods of time series analysis are necessary, which are applicable to short, noisy, and non-stationary uni- and multivariate data sets. Correlations between different climatic proxy data within one or more climatological archives contain significant information about the climatic change on longer time scales. Based on an appropriate statistical decomposition of such multivariate time series, one may estimate dimensions in terms of the number of significant, linear independent components of the considered data set. In the presented work, a corresponding approach is introduced, critically discussed, and extended with respect to the analysis of palaeoclimatic time series. Temporal variations of the resulting measures allow to derive information about climatic changes. For an example of trace element abundances and grain-size distributions obtained near the Cape Roberts (Eastern Antarctica), it is shown that the variability of the dimensions of the investigated data sets clearly correlates with the Oligocene/Miocene transition about 24 million years before present as well as regional deglaciation events. Grain-size distributions in sediments give information about the predominance of different transportation as well as deposition mechanisms. Finite mixture models may be used to approximate the corresponding distribution functions appropriately. In order to give a complete description of the statistical uncertainty of the parameter estimates in such models, the concept of asymptotic uncertainty distributions is introduced. The relationship with the mutual component overlap as well as with the information missing due to grouping and truncation of the measured data is discussed for a particular geological example. An analysis of a sequence of grain-size distributions obtained in Lake Baikal reveals that there are certain problems accompanying the application of finite mixture models, which cause an extended climatological interpretation of the results to fail. As an appropriate alternative, a linear principal component analysis is used to decompose the data set into suitable fractions whose temporal variability correlates well with the variations of the average solar insolation on millenial to multi-millenial time scales. The abundance of coarse-grained material is obviously related to the annual snow cover, whereas a significant fraction of fine-grained sediments is likely transported from the Taklamakan desert via dust storms in the spring season. N2 - Die Separation natürlicher und anthropogen verursachter Klimaänderungen ist eine bedeutende Aufgabe der heutigen Klimaforschung. Hierzu ist eine detaillierte Kenntnis der natürlichen Klimavariabilität während Warmzeiten unerlässlich. Neben Modellsimulationen und historischen Aufzeichnungen spielt hierfür die Analyse von sogenannten Klima-Stellvertreterdaten eine besondere Rolle, die anhand von Archiven wie Baumringen oder Sediment- und Eisbohrkernen erhoben werden. Um solche Quellen paläoklimatischer Informationen vernünftig interpretieren zu können, werden geeignete statistische Modellierungsansätze sowie Methoden der Zeitreihenanalyse benötigt, die insbesondere auf kurze, verrauschte und instationäre uni- und multivariate Datensätze anwendbar sind. Korrelationen zwischen verschiedenen Stellvertreterdaten eines oder mehrerer klimatologischer Archive enthalten wesentliche Informationen über den Klimawandel auf großen Zeitskalen. Auf der Basis einer geeigneten Zerlegung solcher multivariater Zeitreihen lassen sich Dimensionen schätzen als die Zahl der signifikanten, linear unabhängigen Komponenten des Datensatzes. Ein entsprechender Ansatz wird in der vorliegenden Arbeit vorgestellt, kritisch diskutiert und im Hinblick auf die Analyse von paläoklimatischen Zeitreihen weiterentwickelt. Zeitliche Variationen der entsprechenden Maße erlauben Rückschlüsse auf klimatische Veränderungen. Am Beispiel von Elementhäufigkeiten und Korngrößenverteilungen des Cape-Roberts-Gebietes in der Ostantarktis wird gezeigt, dass die Variabilität der Dimension der untersuchten Datensätze klar mit dem Übergang vom Oligozän zum Miozän vor etwa 24 Millionen Jahren sowie regionalen Abschmelzereignissen korreliert. Korngrößenverteilungen in Sedimenten erlauben Rückschlüsse auf die Dominanz verschiedenen Transport- und Ablagerungsmechanismen. Mit Hilfe von Finite-Mixture-Modellen lassen sich gemessene Verteilungsfunktionen geeignet approximieren. Um die statistische Unsicherheit der Parameterschätzung in solchen Modellen umfassend zu beschreiben, wird das Konzept der asymptotischen Unsicherheitsverteilungen eingeführt. Der Zusammenhang mit dem Überlapp der einzelnen Komponenten und aufgrund des Abschneidens und Binnens der gemessenen Daten verloren gehenden Informationen wird anhand eines geologischen Beispiels diskutiert. Die Analyse einer Sequenz von Korngrößenverteilungen aus dem Baikalsee zeigt, dass bei der Anwendung von Finite-Mixture-Modellen bestimmte Probleme auftreten, die eine umfassende klimatische Interpretation der Ergebnisse verhindern. Stattdessen wird eine lineare Hauptkomponentenanalyse verwendet, um den Datensatz in geeignete Fraktionen zu zerlegen, deren zeitliche Variabilität stark mit den Schwankungen der mittleren Sonneneinstrahlung auf der Zeitskala von Jahrtausenden bis Jahrzehntausenden korreliert. Die Häufigkeit von grobkörnigem Material hängt offenbar mit der jährlichen Schneebedeckung zusammen, während feinkörniges Material möglicherweise zu einem bestimmten Anteil durch Frühjahrsstürme aus der Taklamakan-Wüste herantransportiert wird. KW - Zeitreihenanalyse KW - Paläoklimatologie KW - Multivariate Statistik KW - Korngrößenverteilungen KW - Time Series Analysis KW - Palaeoclimatology KW - Multivariate Statistics KW - Grain-size distributions Y1 - 2006 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-12560 ER - TY - GEN A1 - Siegmund, Jonatan Frederik A1 - Sanders, Tanja G. M. A1 - Heinrich, Ingo A1 - Maaten, Ernst van der A1 - Simard, Sonia A1 - Helle, Gerhard A1 - Donner, Reik Volker T1 - Meteorological drivers of extremes in daily stem radius variations of beech, oak, and pine in Northeastern Germany BT - an event coincidence analysis T2 - Frontiers in plant science N2 - Observed recent and expected future increases in frequency and intensity of climatic extremes in central Europe may pose critical challenges for domestic tree species. Continuous dendrometer recordings provide a valuable source of information on tree stem radius variations, offering the possibility to study a tree's response to environmental influences at a high temporal resolution. In this study, we analyze stem radius variations (SRV) of three domestic tree species (beech, oak, and pine) from 2012 to 2014. We use the novel statistical approach of event coincidence analysis (ECA) to investigate the simultaneous occurrence of extreme daily weather conditions and extreme SRVs, where extremes are defined with respect to the common values at a given phase of the annual growth period. Besides defining extreme events based on individual meteorological variables, we additionally introduce conditional and joint ECA as new multivariate extensions of the original methodology and apply them for testing 105 different combinations of variables regarding their impact on SRV extremes. Our results reveal a strong susceptibility of all three species to the extremes of several meteorological variables. Yet, the inter-species differences regarding their response to the meteorological extremes are comparatively low. The obtained results provide a thorough extension of previous correlation-based studies by emphasizing on the timings of climatic extremes only. We suggest that the employed methodological approach should be further promoted in forest research regarding the investigation of tree responses to changing environmental conditions. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 456 KW - dendrometer measurements KW - event coincidence analysis KW - climate extremes KW - growth response Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407943 ER - TY - GEN A1 - Siegmund, Jonatan Frederik A1 - Wiedermann, Marc A1 - Donges, Jonathan Friedemann A1 - Donner, Reik Volker T1 - Impact of temperature and precipitation extremes on the flowering dates of four German wildlife shrub species T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Ongoing climate change is known to cause an increase in the frequency and amplitude of local temperature and precipitation extremes in many regions of the Earth. While gradual changes in the climatological conditions have already been shown to strongly influence plant flowering dates, the question arises if and how extremes specifically impact the timing of this important phenological phase. Studying this question calls for the application of statistical methods that are tailored to the specific properties of event time series. Here, we employ event coincidence analysis, a novel statistical tool that allows assessing whether or not two types of events exhibit similar sequences of occurrences in order to systematically quantify simultaneities between meteorological extremes and the timing of the flowering of four shrub species across Germany. Our study confirms previous findings of experimental studies by highlighting the impact of early spring temperatures on the flowering of the investigated plants. However, previous studies solely based on correlation analysis do not allow deriving explicit estimates of the strength of such interdependencies without further assumptions, a gap that is closed by our analysis. In addition to direct impacts of extremely warm and cold spring temperatures, our analysis reveals statistically significant indications of an influence of temperature extremes in the autumn preceding the flowering. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 497 KW - event coincidence analysis KW - climate-change KW - weather extremes KW - plant phenology KW - interannual variability KW - air-temperature KW - responses KW - drought KW - trends KW - summer Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-408352 SN - 1866-8372 IS - 497 ER - TY - GEN A1 - Baumbach, Lukas A1 - Siegmund, Jonatan Frederik A1 - Mittermeier, Magdalena A1 - Donner, Reik Volker T1 - Impacts of temperature extremes on European vegetation during the growing season T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 642 KW - event coincidence analysis KW - Central Great-Plains KW - climate-change KW - precipitation extremes KW - weather extremes KW - soil-moisture KW - time-series KW - NDVI KW - phenology KW - trends Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-418018 SN - 1866-8372 IS - 642 SP - 4891 EP - 4903 ER - TY - GEN A1 - Ciemer, Catrin A1 - Rehm, Lars A1 - Kurths, Jürgen A1 - Donner, Reik Volker A1 - Winkelmann, Ricarda A1 - Boers, Niklas T1 - An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic sea surface temperatures T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1207 KW - complex networks KW - droughts KW - prediction KW - Amazon rainforest Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-525863 SN - 1866-8372 IS - 9 ER -