arXiv subjects

Armin Bunde

Publications and source records attributed to Armin Bunde.

At least 19 recordsLinked to original sources

arXiv2026

Climate network and complexity based ENSO forecast for 2026

The El Ni\~no Southern Oscillation (ENSO) is the dominant driver of interannual global climate variability and can lead to extreme weather events such as droughts or flooding. Recently, we have developed several statistical approaches for early ENSO forecasting, in particular, its El Ni\~no phase. The climate network-based approach allows forecasting the onset of an El Ni\~no event or its absence about 1 year ahead [1]. The complexity-based approach allows additionally to forecast the magnitude of an upcoming El Ni\~no event in the calendar year before the onset [2]. Additionally, we have developed methods for forecasting the type (Eastern Pacific or Central Pacific) of an El Ni\~no [3] and for probabilistic forecasting of La Ni\~na and neutral events [4], also by the end of the calendar year before the event. Here we present the forecasts of these methods for 2026. The climate network and the complexity-based approach do not provide concurring signals for this year. The combined forecast indicates that a neutral event is more likely than an El Ni\~no. If an El Ni\~no develops in 2026, the complexity-based approach predicts a weaker event with a magnitude of $0.84\pm0.36${\deg}C.

physics.ao-ph↗

arXiv2020

Very early warning signal for El Niño in 2020 with a 4 in 5 likelihood

The El Niño Southern Oscillation (ENSO) is the most important driver of climate variability and can trigger extreme weather events and disasters in various parts of the globe. Recently we have developed a network approach, which allows forecasting an El Niño event about 1 year ahead. Here we communicate that since 2012 this network approach, which does not involve any fit parameter, correctly predicted the absence of El Niño events in 2012, 2013 and 2017 as well as the onset of the large El Niño event that started in 2014 and ended in 2016. Our model also correctly forecasted the onset of the last El Niño event in 2018. In September 2019, the model indicated the return of El Niño in 2020 with an 80% probability.

physics.ao-ph↗

arXiv2019

Complexity based approach for El Nino magnitude forecasting before the "spring predictability barrier"

The El Nino Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the ``spring predictability barrier'' (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Nino 3.4 region. When applying this tool to several near surface air-temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Nino and the previous calendar year's SysSampEn (complexity). We show that this correlation allows to forecast the magnitude of an El Nino with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error $=0.23^\circ C$ for the average of the individual datasets forecasts). For the on-going 2018 El Nino event, our method forecasts a weak El Nino with a magnitude of $1.11\pm 0.23^\circ C$. Our framework presented here not only facilitates a long--term forecasting of the El Nino magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.

physics.ao-ph↗

arXiv2018

Forecasting the magnitude and onset of El Nino based on climate network

El Nino is probably the most influential climate phenomenon on interannual time scales. It affects the global climate system and is associated with natural disasters and serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Nino are still not accurate, at least more than half a year in advance. Here, we introduce a new forecasting index based on network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the El Nino 3.4 region. We find that significant upward trends and peaks in this index forecast with high accuracy both the onset and magnitude of El Nino approximately 1 year ahead. The forecasting procedure we developed improves in particular the prediction of the magnitude of El Nino and is validated based on several, up to more than a century long, datasets.

physics.geo-ph↗

arXiv2017

Testing reanalysis datasets in Antarctica: Trends, persistence properties and trend significance

The reanalysis datasets provide very important sources for investigating the climate dynamics and climate changes in Antarctica. In this paper, three major reanalysis data are compared with Antarctic station data over the last 35 years: the National Centers for Environmental Prediction and the National Center for Atmospheric Research reanalysis (NCEP1), NCEP-DOE Reanalysis 2 (NCEP2), and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim). In our assessment, we compare the linear trends, the fluctuations around the trends, the persistence properties and the significance level of warming trends in the reanalysis data with the observational ones. We find that NCEP1 and NCEP2 show spurious warming trends in all parts of Antarctica except the Peninsula, while ERA-Interim is quite reliable except at Amundsen-Scott. To investigate the persistence of the data sets, we consider the lag-1 autocorrelation $C(1)$ and the Hurst exponent. While $C(1)$ varies quite erratically in different stations, the Hurst exponent shows similar patterns all over Antarctica. Regarding the significance of the trends, NCEP1 and NCEP2 differ considerably from the observational datasets by strongly exaggerating the warming trends. In contrast, ERA-Interim gives reliable results at most stations except at Amundsen-Scott where it shows a significant cooling trend.

physics.ao-ph↗

arXiv2014

The significance of trends in long-term correlated records

We study the distribution $P(x;α,L)$ of the relative trend $x$ in long-term correlated records of length $L$ that are characterized by a Hurst-exponent $α$ between 0.5 and 1.5 obtained by DFA2. The relative trend $x$ is the ratio between the strength of the trend $Δ$ in the record measured by linear regression, and the standard deviation $σ$ around the regression line. We consider $L$ between 400 and 2200, which is the typical length scale of monthly local and annual reconstructed global climate records. Extending previous work by Lennartz and Bunde \cite{Lennartz2011} we show explicitely that $P$ follows the student-t distribution $P\propto [1+(x/a)^2/l]^{-(l+1)/2}$, where the scaling parameter $a$ depends on both $L$ and $α$, while the effective length $l$ depends, for $α$ below 1.15, only on the record length $L$. From $P$ we can derive an analytical expression for the trend significance $S(x;α, L)=\int_{-x}^x P(x';α,L)dx'$ and the border lines of the $95\%$ percent significance interval. We show that the results are nearly independent of the distribution of the data in the record, holding for Gaussian data as well as for highly skewed non-Gaussian data. For an application, we use our methodology to estimate the significance of Central West Antarctic warming.

physics.data-an↗

arXiv2013

Improved El Niño-Forecasting by Cooperativity Detection

Although anomalous episodical warming of the eastern equatorial Pacific, dubbed El Niño by Peruvian fishermen, has major (and occasionally devastating) impacts around the globe, robust forecasting is still limited to about six months ahead. A significant extension of the pre-warming time would be instrumental for avoiding some of the worst damages such as harvest failures in developing countries. Here we introduce a novel avenue towards El Niño-prediction based on network methods inspecting emerging teleconnections. Our approach starts from the evidence that a large-scale cooperative mode - linking the El Niño-basin (equatorial Pacific corridor) and the rest of the ocean - builds up in the calendar year before the warming event. On this basis, we can develop an efficient 12 months-forecasting scheme, i.e., achieve some doubling of the early-warning period. Our method is based on high-quality observational data as available since 1950 and yields hit rates above 0.5, while false-alarm rates are below 0.1.

physics.ao-ph↗

arXiv2012

Complex networks embedded in space: Dimension and scaling relations between mass, topological distance and Euclidean distance

Many real networks are embedded in space, where in some of them the links length decay as a power law distribution with distance. Indications that such systems can be characterized by the concept of dimension were found recently. Here, we present further support for this claim, based on extensive numerical simulations for model networks embedded on lattices of dimensions $d_e=1$ and $d_e=2$. We evaluate the dimension $d$ from the power law scaling of (a) the mass of the network with the Euclidean radius $r$ and (b) the probability of return to the origin with the distance $r$ travelled by the random walker. Both approaches yield the same dimension. For networks with $δ< d_e$, $d$ is infinity, while for $δ> 2d_e$, $d$ obtains the value of the embedding dimension $d_e$. In the intermediate regime of interest $d_e \leq δ< 2 d_e$, our numerical results suggest that $d$ decreases continously from $d = \infty$ to $d_e$, with $d - d_e \sim (δ- d_e)^{-1}$ for $δ$ close to $d_e$. Finally, we discuss the scaling of the mass $M$ and the Euclidean distance $r$ with the topological distance $\ell$. Our results suggest that in the intermediate regime $d_e \leq δ< 2 d_e$, $M(\ell)$ and $r(\ell)$ do not increase with $\ell$ as a power law but with a stretched exponential, $M(\ell) \sim \exp [A \ell^{δ' (2 - δ')}]$ and $r(\ell) \sim \exp [B \ell^{δ' (2 - δ')}]$, where $δ' = δ/d_e$. The parameters $A$ and $B$ are related to $d$ by $d = A/B$, such that $M(\ell) \sim r(\ell)^d$. For $δ< d_e$, $M$ increases exponentially with $\ell$, as known for $δ=0$, while $r$ is constant and independent of $\ell$. For $δ\geq 2d_e$, we find power law scaling, $M(\ell) \sim \ell^{d_\ell}$ and $r(\ell) \sim \ell^{1/d_{min}}$, with $d_\ell \cdot d_{min} = d$.

physics.soc-ph↗

arXiv2008

Structural properties of spatially embedded networks

We study the effects of spatial constraints on the structural properties of networks embedded in one or two dimensional space. When nodes are embedded in space, they have a well defined Euclidean distance $r$ between any pair. We assume that nodes at distance $r$ have a link with probability $p(r) \sim r^{- δ}$. We study the mean topological distance $l$ and the clustering coefficient $C$ of these networks and find that they both exhibit phase transitions for some critical value of the control parameter $δ$ depending on the dimensionality $d$ of the embedding space. We have identified three regimes. When $δ 2d$ the networks are ``large'' worlds $l \sim N^{1/d}$ with high clustering. Our results indicate that spatial constrains have a significant impact on the network properties, a fact that should be taken into account when modeling complex networks.

physics.soc-ph↗

arXiv2007

Improving immunization strategies

We introduce an immunization method where the percentage of required vaccinations for immunity are close to the optimal value of a targeted immunization scheme of highest degree nodes. Our strategy retains the advantage of being purely local, without the need of knowledge on the global network structure or identification of the highest degree nodes. The method consists of selecting a random node and asking for a neighbor that has more links than himself or more than a given threshold and immunizing him. We compare this method to other efficient strategies on three real social networks and on a scale-free network model, and find it to be significantly more effective.

physics.soc-ph↗

arXiv2007

Ising-like dynamics and frozen states in systems of ultrafine magnetic particles

We use Monte-Carlo simulations to study aging phenomena and the occurence of spinglass phases in systems of single-domain ferromagnetic nanoparticles under the combined influence of dipolar interaction and anisotropy energy, for different combinations of positional and orientational disorder. We find that the magnetic moments oriente themselves preferably parallel to their anisotropy axes and changes of the total magnetization are solely achieved by 180 degree flips of the magnetic moments, as in Ising systems. Since the dipolar interaction favorizes the formation of antiparallel chain-like structures, antiparallel chain-like patterns are frozen in at low temperatures, leading to aging phenomena characteristic for spin-glasses. Contrary to the intuition, these aging effects are more pronounced in ordered than in disordered structures.

cond-mat.dis-nn↗

arXiv2006

Spreading and localization of information in scale-free networks with communication constraints

We study localization of information on scale free networks with communication constraints when, for some reason, information can propagate only between ``mutually trusted nodes'' (MTN). We propose an algorithm to construct the MTN network and show that there is a critical value of trusted nodes below which information localizes. This critical value increases drastically if a fraction p of nodes does not transfer information at all. We study the fraction of initial messangers needed to inform a desired fraction of the network as a function of the average number of trusted nodes and discuss possible applications of the model to marketing, to the spreading of risky information and to the spreading of a disease with very short incubation time.

physics.soc-ph↗

arXiv2006

Frozen metastable states in ordered systems of ultrafine magnetic particles

For studying the interplay of dipolar interaction and anisotropy energy in systems of ultrafine magnetic particles we consider simple cubic systems of magnetic dipoles with anisotropy axes pointing into the $z$-direction. Using Monte Carlo simulations we study the magnetic relaxation from several initial states. We show explicitely that, due to the combined influence of anisotropy energy and dipole interaction, magnetic chains are formed along the $z$-direction that organize themselves in frozen metastable domains of columnar antiferromagnetic order. We show that the domains depend explicitely on the history and relax only at extremely large time scales towards the ordered state. We consider this as an indication for the appearence of frozen metastable states also in real sytems, where the dipoles are located in a liquid-like fashion and the anisotropy axes point into random directions.

cond-mat.dis-nn↗

arXiv2005

Stability and topology of scale-free networks under attack and defense strategies

We study tolerance and topology of random scale-free networks under attack and defense strategies that depend on the degree k of the nodes. This situation occurs, for example, when the robustness of a node depends on its degree or in an intentional attack with insufficient knowledge on the network. We determine, for all strategies, the critical fraction p_c of nodes that must be removed for disintegrating the network. We find that for an intentional attack, little knowledge of the well-connected sites is sufficient to strongly reduce p_c. At criticality, the topology of the network depends on the removal strategy, implying that different strategies may lead to different kinds of percolation transitions.

cond-mat.dis-nn↗

arXiv2004

A new interpretation of the dynamic structure model of ion transport in molten and solid glasses

We explore progress in understanding the behaviour of cation conducting glasses, within the context of an evolving ''dynamic structure model'' (DSM). This behaviour includes: in single cation glasses a strong dependence of ion mobility on concentration, and in mixed cation glasses a range of anomalies known collectively as the mixed alkali effect. We argue that this rich phenomenology arises from the emergence during cooling of a well-defined structure in glass melts resulting from the interplay of chemical interactions and thermally driven ionic motions. The new DSM proposes the existence of a new site relaxation process, involving the shrinkage of empty $\bar A$ sites (thus tailored to the needs of $A^+$ ions), and the concurrent emergence of empty $C'$sites, which interrupt the conduction pathways. This reduction of $\bar A$ sites is responsible in the molten glass for the sharp fall in conductivity as temperature drops towards $T_g$. The $C'$ sites play an important role also in the mixed alkali effect, especially in regard to the pronounced asymmetries in diffusion behaviour of dissimilar cations.

cond-mat.mtrl-sci↗

arXiv2004

Volcanic forcing improves Atmosphere-Ocean Coupled General Circulation Model scaling performance

Recent Atmosphere-Ocean Coupled General Circulation Model (AOGCM) simulations of the twentieth century climate, which account for anthropogenic and natural forcings, make it possible to study the origin of long-term temperature correlations found in the observed records. We study ensemble experiments performed with the NCAR PCM for 10 different historical scenarios, including no forcings, greenhouse gas, sulfate aerosol, ozone, solar, volcanic forcing and various combinations, such as it natural, anthropogenic and all forcings. We compare the scaling exponents characterizing the long-term correlations of the observed and simulated model data for 16 representative land stations and 16 sites in the Atlantic Ocean for these scenarios. We find that inclusion of volcanic forcing in the AOGCM considerably improves the PCM scaling behavior. The scenarios containing volcanic forcing are able to reproduce quite well the observed scaling exponents for the land with exponents around 0.65 independent of the station distance from the ocean. For the Atlantic Ocean, scenarios with the volcanic forcing slightly underestimate the observed persistence exhibiting an average exponent 0.74 instead of 0.85 for reconstructed data.

physics.ao-ph↗

arXiv2004

Long-term persistence and multifractality of river runoff records: Detrended fluctuation studies

We study temporal correlations and multifractal properties of long river discharge records from 41 hydrological stations around the globe. To detect long-term correlations and multifractal behaviour in the presence of trends, we apply several recently developed methods [detrended fluctuation analysis (DFA), wavelet analysis, and multifractal DFA] that can systematically detect and overcome nonstationarities in the data at all time scales. We find that above some crossover time that usually is several weeks, the daily runoffs are long-term correlated, being characterized by a correlation function C(s) that decays as C(s) ~ s^(gamma). The exponent gamma varies from river to river in a wide range between 0.1 and 0.9. The power-law decay of C(s) corresponds to a power-law increase of the related fluctuation function F_2(s) ~ s^H where H = 1-gamma/2. We also find that in most records, for large times, weak multifractality occurs. The Renyi exponent tau(q) for q between -10 and +10 can be fitted to the remarkably simple form tau(q) = -ln(a^q+b^q) /ln 2, with solely two parameters a and b between 0 and 1 with a+b >= 1. This type of multifractality is obtained from a generalization of the multiplicative cascade model.

physics.geo-ph↗

arXiv2003

Power-law persistence and trends in the atmosphere: A detailed study of long temperature records

We use several variants of the detrended fluctuation analysis to study the appearance of long-term persistence in temperature records, obtained at 95 stations all over the globe. Our results basically confirm earlier studies. We find that the persistence, characterized by the correlation C(s) of temperature variations separated by s days, decays for large s as a power law, C(s) ~ s^(-gamma). For continental stations, including stations along the coastlines, we find that gamma is always close to 0.7. For stations on islands, we find that gamma ranges between 0.3 and 0.7, with a maximum at gamma = 0.4. This is consistent with earlier studies of the persistence in sea surface temperature records where gamma is close to 0.4. In all cases, the exponent gamma does not depend on the distance of the stations to the continental coastlines. By varying the degree of detrending in the fluctuation analysis we obtain also information about trends in the temperature records.

physics.ao-ph↗