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.Comment: 5 pages, 6 figures. to appear in Phys. Rev.

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

Power-Law Persistence in the Atmosphere: Analysis and Applications

We review recent results on the appearance of long-term persistence in climatic records and their relevance for the evaluation of global climate models and rare events.The persistence can be characterized, for example, by the correlation C(s) of temperature variations separated by s days.We show that, contrary to previous expectations, C(s) decays for large s as a power law, C(s) ~ s^(-gamma). For continental stations, the exponent gamma is always close to 0.7, while for stations on islands gamma is around 0.4. In contrast to the temperature fluctuations, the fluctuations of the rainfall usually cannot be characterized by long-term power-law correlations but rather by pronounced short-term correlations. The universal persistence law for the temperature fluctuations on continental stations represents an ideal (and uncomfortable) test-bed for the state of-the-art global climate models and allows us to evaluate their performance. In addition, the presence of long-term correlations leads to a novel approach for evaluating the statistics of rare events.Comment: 12 pages, 6 included EPS figures, added chapter

Phase Synchronization in Temperature and Precipitation Records

We study phase synchronization between atmospheric variables such as daily mean temperature and daily precipitation records. We find significant phase synchronization between records of Oxford and Vienna as well as between the records of precipitation and temperature in each city. To find the time delay in the synchronization between the records we study the time lag phase synchronization when the records are shifted by a variable time interval of days. We also compare the results of the method with the classical cross-correlation method and find that in certain cases the phase synchronization yields more significant results.Comment: 11 pages including 8 figure

Gesetzmäßigkeiten im Chaos : Über das „Langzeitgedächtnis“ und seine Folgen in Klima, Physiologie und auf den Finanzmärkten

Zeitreihen geben oft wichtige Auskünfte über komplexe Systeme in der Natur: Aus Temperatur- und Niederschlagsreihen erhofft man sich Aufschluss über globale Klimaänderungen, aus medizinischen Zeitreihen kann man oft Rückschlüsse auf den Gesundheitszustand des Patienten ziehen und aus der Analyse von Finanzreihen eine Maximierung der Gewinnchancen und eine Minimierung des Verlustrisikos erschließen. In solchen Zeitreihen sind die Schwankungen der einzelnen Werte nicht unabhängig, sondern über sehr lange Zeitspannen hinweg untereinander gekoppelt, d. h. die zugrunde liegenden Prozesse besitzen ein oft überraschend langes Gedächtnis

Comment on "Scaling of atmosphere and ocean temperature correlations in observations and climate models"

In a recent letter [K. Fraedrich and R. Blender, Phys. Rev. Lett. 90, 108501 (2003)], Fraedrich and Blender studied the scaling of atmosphere and ocean temperature. They analyzed the fluctuation functions F(s) ~ s^alpha of monthly temperature records (mostly from grid data) by using the detrended fluctuation analysis (DFA2) and claim that the scaling exponent alpha over the inner continents is equal to 0.5, being characteristic of uncorrelated random sequences. Here we show that this statement is (i) not supported by their own analysis and (ii) disagrees with the analysis of the daily observational data from which the grid monthly data have been derived. We conclude that also for the inner continents, the exponent is between 0.6 and 0.7, similar as for the coastline-stations.Comment: 1 page with 2 figure