Many natural time series exhibit long range temporal correlations that may be characterized by power-law scaling exponents. However, in many cases, the time series have uneven time intervals due to, for example, missing data points, noisy data, and outliers. Here we study the effect of randomly...

We study the temporal correlations in the sea surface temperature (SST) fluctuations around the seasonal mean values in the Atlantic and Pacific Oceans. We apply a method that systematically overcome possible trends in the data. We find that the SST persistence, characterized by the correlation...

Using detrended fluctuation analysis, we study the scaling properties of the volatility time series Vi=|Ti+1−Ti| of daily temperatures Ti for 10 chosen sites around the globe. We find that the volatility is long-range power-law correlated with an exponent γ close to 0.8 for all sites...

The persistence of the weather is a well-known phenomenon. If, for example, one day is sunny and warm, there is a high tendency that the next day remains similar. In this paper, we review recent results showing that the long-term persistence, characterized by the correlation C(s) of temperature...

We study trends and temporal correlations in the monthly mean temperature data of Prague and Melbourne derived from four state-of-the-art general circulation models that are currently used in studies of anthropogenic effects on the atmosphere: GFDL-R15-a, CSIRO-Mk2, ECHAM4/OPYC3 and HADCM3. In...

We study two aspects of the detrended fluctuation analysis (DFA) method, namely the scaling behavior of the leading terms of the best-fit polynomials and the detection of trends. We show analytically and numerically that the standard deviation of the leading terms of the best-fit polynomials...