Showing papers by "Manfred Mudelsee published in 2010"

Climate Time Series Analysis: Classical Statistical and Bootstrap Methods

Abstract: Part I: Fundamental Concepts.- 1 Introduction.- 2 Persistence Models.- 3 Bootstrap Confidence Intervals.- Part II: Univariate Time Series.- 4 Regression I.- 5 Spectral Analysis.- 6. Extreme Value Time Series.- Part III: Bivariate Time Series.- 7 Correlation.- 8 Regression II.- Part IV: Outlook.- 9 Future Directions.

Climate time series analysis

Abstract: Climate time series analysis , Climate time series analysis , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

Abrupt change of Antarctic moisture origin at the end of Termination II

Abstract: The deuterium excess of polar ice cores documents past changes in evaporation conditions and moisture origin. New data obtained from the European Project for Ice Coring in Antarctica Dome C East Antarctic ice core provide new insights on the sequence of events involved in Termination II, the transition between the penultimate glacial and interglacial periods. This termination is marked by a north-south seesaw behavior, with first a slow methane concentration rise associated with a strong Antarctic temperature warming and a slow deuterium excess rise. This first step is followed by an abrupt north Atlantic warming, an abrupt resumption of the East Asian summer monsoon, a sharp methane rise, and a CO(2) overshoot, which coincide within dating uncertainties with the end of Antarctic optimum. Here, we show that this second phase is marked by a very sharp Dome C centennial deuterium excess rise, revealing abrupt reorganization of atmospheric circulation in the southern Indian Ocean sector.

Limitations of red noise in analysing Dansgaard-Oeschger events

Abstract: . During the last glacial period, climate records from the North Atlantic region exhibit a pronounced spectral component corresponding to a period of about 1470 years, which has attracted much attention. This spectral peak is closely related to the recurrence pattern of Dansgaard-Oeschger (DO) events. In previous studies a red noise random process, more precisely a first-order autoregressive (AR1) process, was used to evaluate the statistical significance of this peak, with a reported significance of more than 99%. Here we use a simple mechanistic two-state model of DO events, which itself was derived from a much more sophisticated ocean-atmosphere model of intermediate complexity, to numerically evaluate the spectral properties of random (i.e., solely noise-driven) events. This way we find that the power spectral density of random DO events differs fundamentally from a simple red noise random process. These results question the applicability of linear spectral analysis for estimating the statistical significance of highly non-linear processes such as DO events. More precisely, to enhance our scientific understanding about the trigger of DO events, we must not consider simple "straw men" as, for example, the AR1 random process, but rather test against realistic alternative descriptions.

Extreme Value Time Series

Abstract: Extreme value time series refer to the outlier component in the climate equation ( Eq. 1.2). Quantifying the tail probability of the PDF of a climate variable—the risk of climate extremes—is of high socioeconomical relevance. In the context of climate change, it is important to move from stationary to nonstationary (time-dependent) models: with climate changes also risk changes may be associated.

Bootstrap Confidence Intervals

Abstract: In statistical analysis of climate time series, our aim (Chapter 1) is to estimate parameters of X trend(T), X out(T), S(T) and X noise(T). Denote in general such a parameter as θ. An estimator, \( \hat{\theta } \), is a recipe how to calculate θ from a set of data. The data, discretely sampled time series \( \{t\left( i \right),x\left( i \right)\}_{{i=1}}^{n} \), are influenced by measurement and proxy errors of x(i), outliers, dating errors of t(i) and climatic noise. Therefore, \( \hat{\theta } \) cannot be expected to equal θ. The accuracy of \( \hat{\theta } \), how close it comes to θ, is described by statistical terms such as standard error, bias, mean squared error and confidence interval (CI). These are introduced in Section 3.1.