C.-D. Schönwiese

Assessments of the global anthropogenic greenhouse and sulfate signal using different types of simplified climate models

SummaryThe problem of global climate change forced by anthropogenic emissions of greenhouse gases (GHG) and sulfur components (SU) has to be addressed by different methods, including the consideration of concurrent forcing mechanisms and the analysis of observations. This is due to the shortcoming and uncertainties of all methods, even in case of the most sophisticated ones. In respect to the global mean surface air temperature, we compare the results from multiple observational statistical models such as multiple regression (MRM) and neural networks (NNM) with those of energy balance (EBM) and general circulation models (GCM) where, in the latter case, we refer to the recent IPCC Report. Our statistical assessments, based on the 1866–1994 period, lead to a GHG signal of 0.8–1.3 K and a combined GHG-SU signal of 0.5–0.8 K detectable in observations. This is close to GCM simulations and clearly larger than the volcanic, solar and ENSO (El Niño/southern oscillation) signals also considered.

A generalized method of time series decomposition into significant components including probability assessments of extreme events and application to observational German precipitation data

The analysis of climate variability realized in time series of observational data needs adequate statistical methods. In particular, it is important to estimate reliably significant structured components like the annual cycle, trends, the episodic component and extreme events including variations of these components. In this issue estimators are called reliable, if a priori assumed statistical assumptions are fulfilled. However, climate change concerns not only the mean value of meteorological variables, but all parameters of any related frequency distribution. In consequence, a generalized time series decomposition technique is presented allowing a free choice of the underlying probability density function (PDF). The signal (structured components like trends etc.) is detected in two instead of one parameter of a PDF, which can be chosen without any further restriction. So, the scale parameter of any PDF is no longer seen as a constant but rather affected by a deterministic process. The trend and seasonal component reflected in both parameters under consideration are estimated simultaneously in a modified stepwise regression. To deal also with superposed polynomial components and extreme events an iterative procedure is applied that converges to robust estimates of all the components. In particular, the method allows a consistent decomposition of precipitation time series into a statistical and a deterministic component. It arises, that in the special case of 132 time series of monthly precipitation totals 1901-2000, from German stations, the interpretation as a realization of a Gumbel-distributed random variable with time-dependent scale and location parameter reveals a complete analytical description of the time series. In addition to the detection of the components mentioned above, now it is possible to quantify the probability of exceeding optional upper or lower thresholds, respectively, for any time step of the observation period.

Extreme Value and Trend Analysis Based on Statistical Modelling of Precipitation Time Series

Application of a generalized time series decomposition technique shows that observed German monthly precipitation time series can be interpreted as a realization of a Gumbel-distributed random variable with time-dependent location parameter and time-dependent scale parameter. The achieved complete analytical description of the series, that is, the probability density function (PDF) for every time step of the observation period, allows probability assessments of extreme values for any threshold at any time. So, we found in the western part of Germany that climate is getting more extreme in winter. Both the probability for exceeding the 95th percentile and the probability for falling under the 5th percentile are increasing. Contrary results are found in summer. The spread of the distribution is shrinking. But in the south, relatively high precipitation sums become more likely and relatively low precipitation sums become more unlikely in turn of the twentieth century.