2006, Venema, V., Bachner, S., Rust, H.W., Simmer, C.

In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales.

2006, Knopf, B., Flechsig, M., Zickfeld, K.

Parameter uncertainty analysis of climate models has become a standard approach for model validation and testing their sensitivity. Here we present a novel approach that allows one to estimate the robustness of a bifurcation point in a multi-parameter space. In this study we investigate a box model of the Indian summer monsoon that exhibits a saddle-node bifurcation against those parameters that govern the heat balance of the system. The bifurcation brings about a change from a wet summer monsoon regime to a regime that is characterised by low precipitation. To analyse the robustness of the bifurcation point itself and its location in parameter space, we perform a multi-parameter uncertainty analysis by applying qualitative, Monte Carlo and deterministic methods that are provided by a multi-run simulation environment. Our results show that the occurrence of the bifurcation point is robust over a wide range of parameter values. The position of the bifurcation, however, is found to be sensitive on these specific parameter choices.