Contributions to surface air temperature trends estimated from climate time series: Medium-term causalities.

Contributions of various natural and anthropogenic factors to trends of surface air temperatures at different latitudes of the Northern and Southern hemispheres on various temporal horizons are estimated from climate data since the 19th century in empirical autoregressive models. Along with anthropogenic forcing, we assess the impact of several natural climate modes including Atlantic Multidecadal Oscillation, El-Nino/Southern Oscillation, Interdecadal Pacific Oscillation, Pacific Decadal Oscillation, and Antarctic Oscillation. On relatively short intervals of the length of two or three decades, contributions of climate variability modes are considerable and comparable to the contributions of greenhouse gases and even exceed the latter.

Relating Granger causality to long-term causal effects.

In estimation of causal couplings between observed processes, it is important to characterize coupling roles at various time scales. The widely used Granger causality reflects short-term effects: it shows how strongly perturbations of a current state of one process affect near future states of another process, and it quantifies that via prediction improvement (PI) in autoregressive models. However, it is often more important to evaluate the effects of coupling on long-term statistics, e.

Time-frequency methods for structural health monitoring.

Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and concrete dams) using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements.

From Granger causality to long-term causality: application to climatic data.

Quantitative characterization of interaction between processes from time series is often required in different fields of natural science including geophysics and biophysics. Typically, one estimates "short-term" influences, e.g.

Explaining the eventual transient saturation of climate-carbon cycle feedback.

Background: Coupled climate-carbon cycle simulations generally show that climate feedbacks amplify the buildup of CO2 under respective anthropogenic emission. The effect of climate-carbon cycle feedback is characterised by the feedback gain: the relative increase in CO2 increment as compared to uncoupled simulations. According to the results of the recent Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP), the gain is expected to increase during the 21st century.