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Start date: 2000 × End date: 2009 × search.filters.applied.f.series: Nonlinear Processes in Geophysics 12 (2005), Nr. 2 ×

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Now showing 1 - 3 of 3
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    Kinetic slow mode-type solitons
    (Göttingen : Copernicus GmbH, 2005) Baumgärtel, K.; Sauer, K.; Dubinin, E.
    One-dimensional hybrid code simulations are presented, carried out in order both to study solitary waves of the slow mode branch in an isotropic, collisionless, medium-β plasma (βi=0.25) and to test the fluid based soliton interpretation of Cluster observed strong magnetic depressions (Stasiewicz et al., 200; Stasiewicz, 2004) against kinetic theory. In the simulations, a variety of strongly oblique, large amplitude, solitons are seen, including solitons with Alfvenic polarization, similar to those predicted by the Hall-MHD theory, and robust, almost non-propagating, solitary structures of slow magnetosonic type with strong magnetic field depressions and perpendicular ion heating, which have no counterpart in fluid theory. The results support the soliton-based interpretation of the Cluster observations, but reveal substantial deficiencies of Hall-MHD theory in describing slow mode-type solitons in a plasma of moderate beta.
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    Trend assessment: Applications for hydrology and climate research
    (Göttingen : Copernicus GmbH, 2005) Kallache, M.; Rust, H.W.; Kropp, J.
    The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.
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    Forced versus coupled dynamics in Earth system modelling and prediction
    (Göttingen : Copernicus GmbH, 2005) Knopf, B.; Held, H.; Schellnhuber, H.J.
    We compare coupled nonlinear climate models and their simplified forced counterparts with respect to predictability and phase space topology. Various types of uncertainty plague climate change simulation, which is, in turn, a crucial element of Earth System modelling. Since the currently preferred strategy for simulating the climate system, or the Earth System at large, is the coupling of sub-system modules (representing, e.g. atmosphere, oceans, global vegetation), this paper explicitly addresses the errors and indeterminacies generated by the coupling procedure. The focus is on a comparison of forced dynamics as opposed to fully, i.e. intrinsically, coupled dynamics. The former represents a particular type of simulation, where the time behaviour of one complex systems component is prescribed by data or some other external information source. Such a simplifying technique is often employed in Earth System models in order to save computing resources, in particular when massive model inter-comparisons need to be carried out. Our contribution to the debate is based on the investigation of two representative model examples, namely (i) a low-dimensional coupled atmosphere-ocean simulator, and (ii) a replica-like simulator embracing corresponding components. Whereas in general the forced version (ii) is able to mimic its fully coupled counterpart (i), we show in this paper that for a considerable fraction of parameter- and state-space, the two approaches qualitatively differ. Here we take up a phenomenon concerning the predictability of coupled versus forced models that was reported earlier in this journal: the observation that the time series of the forced version display artificial predictive skill. We present an explanation in terms of nonlinear dynamical theory. In particular we observe an intermittent version of artificial predictive skill, which we call on-off synchronization, and trace it back to the appearance of unstable periodic orbits. We also find it to be governed by a scaling law that allows us to estimate the probability of artificial predictive skill. In addition to artificial predictability we observe artificial bistability for the forced version, which has not been reported so far. The results suggest that bistability and intermittent predictability, when found in a forced model set-up, should always be cross-validated with alternative coupling designs before being taken for granted.