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Based on suggested interactions of potential tipping elements in the Earth's climate and in ecological systems, tipping cascades as possible dynamics are increasingly discussed and studied. The activation of such tipping cascades would impose a considerable risk for human societies and biosphere integrity. However, there are ambiguities in the description of tipping cascades within the literature so far. Here we illustrate how different patterns of multiple tipping dynamics emerge from a very simple coupling of two previously studied idealized tipping elements. In particular, we distinguish between a two phase cascade, a domino cascade and a joint cascade. A mitigation of an unfolding two phase cascade may be possible and common early warning indicators are sensitive to upcoming critical transitions to a certain degree. In contrast, a domino cascade may hardly be stopped once initiated and critical slowing down-based indicators fail to indicate tipping of the following element. These different potentials for intervention and anticipation across the distinct patterns of multiple tipping dynamics should be seen as a call to be more precise in future analyses of cascading dynamics arising from tipping element interactions in the Earth system.
What comes NeXT?
(2019)
Here, we report on a new record in the acquisition time for fast neutron tomography. With an optimized imaging setup, it was possible to acquire single radiographic projection images with 10 ms and full tomographies with 155 projections images and a physical spatial resolution of 200 mu m within 1.5 s. This is about 6.7 times faster than the current record. We used the technique to investigate the water infiltration in the soil with a living lupine root system. The fast imaging setup will be part of the future NeXT instrument at ILL in Grenoble with a great field of possible future applications. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Ice-core-based records of isotopic composition are a proxy for past temperatures and can thus provide information on polar climate variability over a large range of timescales. However, individual isotope records are affected by a multitude of processes that may mask the true temperature variability. The relative magnitude of climate and non-climate contributions is expected to vary as a function of timescale, and thus it is crucial to determine those temporal scales on which the actual signal dominates the noise. At present, there are no reliable estimates of this timescale dependence of the signal-to-noise ratio (SNR). Here, we present a simple method that applies spectral analyses to stable-isotope data from multiple cores to estimate the SNR, and the signal and noise variability, as a function of timescale. The method builds on separating the contributions from a common signal and from local variations and includes a correction for the effects of diffusion and time uncertainty. We apply our approach to firn-core arrays from Dronning Maud Land (DML) in East Antarctica and from the West Antarctic Ice Sheet (WAIS). For DML and decadal to multi-centennial timescales, we find an increase in the SNR by nearly 1 order of magnitude (similar to 0.2 at decadal and similar to 1.0 at multi-centennial scales). The estimated spectrum of climate variability also shows increasing variability towards longer timescales, contrary to what is traditionally inferred from single records in this region. In contrast, the inferred variability spectrum for WAIS stays close to constant over decadal to centennial timescales, and the results even suggest a decrease in SNR over this range of timescales. We speculate that these differences between DML and WAIS are related to differences in the spatial and temporal scales of the isotope signal, highlighting the potentially more homogeneous atmospheric conditions on the Antarctic Plateau in contrast to the marine-influenced conditions on WAIS. In general, our approach provides a methodological basis for separating local proxy variability from coherent climate variations, which is applicable to a large set of palaeoclimate records.
What can we learn from climate data? : Methods for fluctuation, time/scale and phase analysis
(2006)
Since Galileo Galilei invented the first thermometer, researchers have tried to understand the complex dynamics of ocean and atmosphere by means of scientific methods. They observe nature and formulate theories about the climate system. Since some decades powerful computers are capable to simulate the past and future evolution of climate. Time series analysis tries to link the observed data to the computer models: Using statistical methods, one estimates characteristic properties of the underlying climatological processes that in turn can enter the models. The quality of an estimation is evaluated by means of error bars and significance testing. On the one hand, such a test should be capable to detect interesting features, i.e. be sensitive. On the other hand, it should be robust and sort out false positive results, i.e. be specific. This thesis mainly aims to contribute to methodological questions of time series analysis with a focus on sensitivity and specificity and to apply the investigated methods to recent climatological problems. First, the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) is studied. It is argued that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established. This requires to investigate the local slopes of the fluctuation function. The variability characteristic for stochastic processes is accounted for by calculating empirical confidence regions. The comparison of a long-memory with a short-memory model shows that the inference of long-range correlations from a finite amount of data by means of DFA is not specific. When aiming to infer short memory by means of DFA, a local slope larger than $\alpha=0.5$ for large scales does not necessarily imply long-memory. Also, a finite scaling of the autocorrelation function is shifted to larger scales in the fluctuation function. It turns out that long-range correlations cannot be concluded unambiguously from the DFA results for the Prague temperature data set. In the second part of the thesis, an equivalence class of nonstationary Gaussian stochastic processes is defined in the wavelet domain. These processes are characterized by means of wavelet multipliers and exhibit well defined time dependent spectral properties; they allow one to generate realizations of any nonstationary Gaussian process. The dependency of the realizations on the wavelets used for the generation is studied, bias and variance of the wavelet sample spectrum are calculated. To overcome the difficulties of multiple testing, an areawise significance test is developed and compared to the conventional pointwise test in terms of sensitivity and specificity. Applications to Climatological and Hydrological questions are presented. The thesis at hand mainly aims to contribute to methodological questions of time series analysis and to apply the investigated methods to recent climatological problems. In the last part, the coupling between El Nino/Southern Oscillation (ENSO) and the Indian Monsoon on inter-annual time scales is studied by means of Hilbert transformation and a curvature defined phase. This method allows one to investigate the relation of two oscillating systems with respect to their phases, independently of their amplitudes. The performance of the technique is evaluated using a toy model. From the data, distinct epochs are identified, especially two intervals of phase coherence, 1886-1908 and 1964-1980, confirming earlier findings from a new point of view. A significance test of high specificity corroborates these results. Also so far unknown periods of coupling invisible to linear methods are detected. These findings suggest that the decreasing correlation during the last decades might be partly inherent to the ENSO/Monsoon system. Finally, a possible interpretation of how volcanic radiative forcing could cause the coupling is outlined.
Die Arktis erwärmt sich schneller als der Rest der Erde. Die Auswirkungen manifestieren sich unter Anderem in einer verstärkten Erwärmung der arktischen Grenzschicht. Diese Arbeit befasst sich mit Wechselwirkungen zwischen synoptischen Zyklonen und der arktischen Atmosphäre auf lokalen bis überregionalen Skalen. Ausgangspunkt dafür sind Messdaten und Modellsimulationen für den Zeitraum der N-ICE2015 Expedition, die von Anfang Januar bis Ende Juni 2015 im arktischen Nordatlantiksektor stattgefunden hat.
Anhand von Radiosondenmessungen lassen sich Auswirkungen von synoptischen Zyklonen am deutlichsten im Winter erkennen, da sie durch die Advektion warmer und feuchter Luftmassen in die Arktis den Zustand der Atmosphäre von einem strahlungs-klaren in einen strahlungs-opaken ändern. Obwohl dieser scharfe Kontrast nur im Winter existiert, zeigt die Analyse, dass der integrierte Wasserdampf als Indikator für die Advektion von Luftmassen aus niedrigen Breiten in die Arktis auch im Frühjahr geeignet ist. Neben der Advektion von Luftmassen wird der Einfluss der Zyklonen auf die statische Stabilität charakterisiert. Beim Vergleich der N-ICE2015 Beobachtungen mit der SHEBA Kampagne (1997/1998), die über dickerem Eis stattfand, finden sich trotz der unterschiedlichen Meereisregime Ähnlichkeiten in der statischen Stabilität der Atmosphäre. Die beobachteten Differenzen in der Stabilität lassen sich auf Unterschiede in der synoptischen Aktivität zurückführen. Dies lässt vermuten, dass die dünnere Eisdecke auf saisonalen Zeitskalen nur einen geringen Einfluss auf die thermodynamische Struktur der arktischen Troposphäre besitzt, solange eine dicke Schneeschicht sie bedeckt. Ein weiterer Vergleich mit den parallel zur N-ICE2015 Kampagne gestarteten Radiosonden der AWIPEV Station in Ny-Åesund, Spitzbergen, macht deutlich, dass die synoptischen Zyklonen oberhalb der Orographie auf saisonalen Zeitskalen das Wettergeschehen bestimmen.
Des Weiteren werden für Februar 2015 die Auswirkungen von in der Vertikalen variiertem Nudging auf die Entwicklung der Zyklonen am Beispiel des hydrostatischen regionalen Klimamodells HIRHAM5 untersucht. Es zeigt sich, dass die Unterschiede zwischen den acht Modellsimulationen mit abnehmender Anzahl der genudgten Level zunehmen. Die größten Differenzen resultieren vornehmlich aus dem zeitlichen Versatz der Entwicklung synoptischer Zyklonen. Zur Korrektur des Zeitversatzes der Zykloneninitiierung genügt es bereits, Nudging in den unterstem 250 m der Troposphäre anzuwenden. Daneben findet sich zwischen den genudgten HIRHAM5-Simulation und den in situ Messungen der gleiche positive Temperaturbias, den auch ERA-Interim besitzt. Das freie HIRHAM hingegen reproduziert das positive Ende der N-ICE2015 Temperaturverteilung gut, besitzt aber einen starken negativen Bias, der sehr wahrscheinlich aus einer Unterschätzung des Feuchtegehalts resultiert. An Beispiel einer Zyklone wird gezeigt, dass Nudging Einfluss auf die Lage der Höhentiefs besitzt, die ihrerseits die Zyklonenentwicklung am Boden beeinflussen. Im Weiteren wird mittels eines für kleine Ensemblegrößen geeigneten Varianzmaßes eine statistische Einschätzung der Wirkung des Nudgings auf die Vertikale getroffen. Es wird festgestellt, dass die Ähnlichkeit der Modellsimulationen in der unteren Troposphäre generell höher ist als darüber und in 500 hPa ein lokales Minimum besitzt.
Im letzten Teil der Analyse wird die Wechselwirkung der oberen und unteren Stratosphäre anhand zuvor betrachteter Zyklonen mit Daten der ERA-Interim Reanalyse untersucht. Lage und Ausrichtung des Polarwirbels erzeugten ab Anfang Februar 2015 eine ungewöhnlich große Meridionalkomponente des Tropopausenjets, die Zugbahnen in die zentrale Arktis begünstigte. Am Beispiel einer Zyklone wird die Übereinstimmung der synoptischen Entwicklung mit den theoretischen Annahmen über den abwärts gerichteten Einfluss der Stratosphäre auf die Troposphäre hervorgehoben. Dabei spielt die nicht-lineare Wechselwirkung zwischen der Orographie Grönlands, einer Intrusion stratosphärischer Luft in die Troposphäre sowie einer in Richtung Arktis propagierender Rossby-Welle eine tragende Rolle. Als Indikator dieser Wechselwirkung werden horizontale Signaturen aus abwechselnd aufsteigender und absinkender Luft innerhalb der Troposphäre identifiziert.
We study by Monte Carlo simulations a kinetic exchange trading model for both fixed and distributed saving propensities of the agents and rationalize the person and wealth distributions. We show that the newly introduced wealth distribution – that may be more amenable in certain situations – features a different power-law exponent, particularly for distributed saving propensities of the agents. For open agent-based systems, we analyze the person and wealth distributions and find that the presence of trap agents alters their amplitude, leaving however the scaling exponents nearly unaffected. For an open system, we show that the total wealth – for different trap agent densities and saving propensities of the agents – decreases in time according to the classical Kohlrausch–Williams–Watts stretched exponential law. Interestingly, this decay does not depend on the trap agent density, but rather on saving propensities. The system relaxation for fixed and distributed saving schemes are found to be different.
We introduce and study a family of lattice equations which may be viewed either as a strongly nonlinear discrete extension of the Gardner equation, or a non-convex variant of the Lotka-Volterra chain. Their deceptively simple form supports a very rich family of complex solitary patterns. Some of these patterns are also found in the quasi-continuum rendition, but the more intriguing ones, like interlaced pairs of solitary waves, or waves which may reverse their direction either spontaneously or due a collision, are an intrinsic feature of the discrete realm.
Projection methods based on wavelet functions combine optimal convergence rates with algorithmic efficiency. The proofs in this paper utilize the approximation properties of wavelets and results from the general theory of regularization methods. Moreover, adaptive strategies can be incorporated still leading to optimal convergence rates for the resulting algorithms. The so-called wavelet-vaguelette decompositions enable the realization of especially fast algorithms for certain operators.
In recent years, complex network analysis facilitated the identification of universal and unexpected patterns in complex climate systems. However, the analysis and representation of a multiscale complex relationship that exists in the global climate system are limited. A logical first step in addressing this issue is to construct multiple networks over different timescales. Therefore, we propose to apply the wavelet multiscale correlation (WMC) similarity measure, which is a combination of two state-of-the-art methods, viz. wavelet and Pearson’s correlation, for investigating multiscale processes through complex networks. Firstly we decompose the data over different timescales using the wavelet approach and subsequently construct a corresponding network by Pearson’s correlation. The proposed approach is illustrated and tested on two synthetics and one real-world example. The first synthetic case study shows the efficacy of the proposed approach to unravel scale-specific connections, which are often undiscovered at a single scale. The second synthetic case study illustrates that by dividing and constructing a separate network for each time window we can detect significant changes in the signal structure. The real-world example investigates the behavior of the global sea surface temperature (SST) network at different timescales. Intriguingly, we notice that spatial dependent structure in SST evolves temporally. Overall, the proposed measure has an immense potential to provide essential insights on understanding and extending complex multivariate process studies at multiple scales.
Contents: 1 Introduction 1.1 Tikhanov-Phillips Regularization of Ill-Posed Problems 1.2 A Compact Course to Wavelets 2 A Multilevel Iteration for Tikhonov-Phillips Regularization 2.1 Multilevel Splitting 2.2 The Multilevel Iteration 2.3 Multilevel Approach to Cone Beam Reconstuction 3 The use of approximating operators 3.1 Computing approximating families {Ah}