@article{MaraunKurths2005,
  author    = {Maraun, Douglas and Kurths, J{\"u}rgen},
  title     = {Epochs of phase coherence between El Nino/Southern Oscillation and Indian monsoon},
  issn      = {0094-8276},
  year      = {2005},
  abstract  = {We present a modern method used in nonlinear time series analysis to investigate the relation of two oscillating systems with respect to their phases, independently of their amplitudes. We study the difference of the phase dynamics between El Nino/Southern Oscillation (ENSO) and the Indian Monsoon on inter-annual time scales. We identify distinct epochs, especially two intervals of phase coherence, 1886 - 1908 and 1964 - 1980, corroborating earlier findings from a new point of view. A significance test shows that the coherence is very unlikely to be the result of stochastic fluctuations. We also detect so far unknown periods of coupling which are invisible to linear methods. These findings suggest that the decreasing correlation during the last decades might be a typical epoch of the ENSO/ Monsoon system having occurred repeatedly. The high time resolution of the method enables us to present an interpretation of how volcanic radiative forcing could cause the coupling},
  language  = {en}
}
@article{MaraunKurths2004,
  author    = {Maraun, Douglas and Kurths, J{\"u}rgen},
  title     = {Cross wavelet analysis: significance testing and pitfalls},
  issn      = {1023-5809},
  year      = {2004},
  abstract  = {In this paper, we present a detailed evaluation of cross wavelet analysis of bivariate time series. We develop a statistical test for zero wavelet coherency based on Monte Carlo simulations. If at least one of the two processes considered is Gaussian white noise, an approximative formula for the critical value can be utilized. In a second part, typical pitfalls of wavelet cross spectra and wavelet coherency are discussed. The wavelet cross spectrum appears to be not suitable for significance testing the interrelation between two processes. Instead, one should rather apply wavelet coherency. Furthermore we investigate problems due to multiple testing. Based on these results, we show that coherency between ENSO and NAO is an artefact for most of the time from 1900 to 1995. However, during a distinct period from around 1920 to 1940, significant coherency between the two phenomena occurs},
  language  = {en}
}
@article{MaraunRustTimmer2004,
  author    = {Maraun, Douglas and Rust, H. W. and Timmer, Jens},
  title     = {Tempting long-memory : on the interpretation of DFA results},
  issn      = {1023-5809},
  year      = {2004},
  abstract  = {We study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue 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 the investigation of the local slopes. We account for the variability characteristic for stochastic processes by calculating empirical confidence regions. Comparing 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. We remark that scaling cannot be concluded from a straight line fit to the fluctuation function in a log-log representation. Furthermore, we show that a local slope larger than alpha=0.5 for large scales does not necessarily imply long memory. We also demonstrate, that it is not valid to conclude from a finite scaling region of the fluctuation function to an equivalent scaling region of the autocoffelation function. Finally, we review DFA results for the Prague temperature data set and show that long-range correlations cannot not be concluded unambiguously},
  language  = {en}
}
@phdthesis{Maraun2006,
  author    = {Maraun, Douglas},
  title     = {What can we learn from climate data? : Methods for fluctuation, time/scale and phase analysis},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-9047},
  school      = {Universit{\"a}t Potsdam},
  year      = {2006},
  abstract  = {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.},
  subject      = {Spektralanalyse <Stochastik>},
  language  = {en}
}
@article{MaraunRustOsborn2009,
  author    = {Maraun, Douglas and Rust, Henning W. and Osborn, Tim J.},
  title     = {The annual cycle of heavy precipitation across the United Kingdom : a model based on extreme value statistics},
  issn      = {0899-8418},
  doi       = {10.1002/Joc.1811},
  year      = {2009},
  abstract  = {The annual cycle of extreme I-day precipitation events across the UK is investigated by developing a statistical model and fitting it to data from 689 rain gauges A generalized extrerne-value distribution (GEV) is fit to the time series of monthly maxima, across all months of the year simultaneously, by approximating, the annual cycles of the location and scale parameters by harmonic functions, while keeping the shape parameter constant throughout the year We average the shape parameter of neighbouring rain gauges to decrease uncertainties. and also Interpolate values of all model parameters to give complete coverage of (lie UK. The model reveals distinct spatial patterns the estimated parameters The annual mean of the location and scale parameter is highly correlated with orography. The annual cycle of the location parameter is strong in the northwest UK (peaking in late autumn or winter) and in East Anglia (where it peaks HI late summer), and low in the Midlands The annual cycle of the scale parameter exhibits a similar pattern with strongest amplitudes in East Anglia The spatial patterns of the annual cycle phase suggest that they are linked to the dominance of frontal precipitation for generating extreme precipitation in the west and convective precipitation in the southeast of the UK The shape parameter shows a gradient from Positive Values in the east to negative values in some areas of the west We also estimate 10-year and 100-year return levels at each rain gauge, and interpolated across the UK.},
  language  = {en}
}