Abstract. An Analysis of Covariance (ANCOVA) was used to derive the influence of the meteorological variability on the daily maximum ozone concentrations at 12 low-elevation sites north of the Alps in Switzerland during the four seasons in the 1992-2002 period. The afternoon temperature and the morning global radiation were the variables that accounted for most of the meteorological variability in summer and spring, while other variables that can be related to vertical mixing and dilution of primary pollutants (afternoon global radiation, wind speed, stability or day of the week) were more significant in winter. In addition, the number of days after a frontal passage was important to account for ozone build-up in summer and ozone destruction in winter. The statistical model proved to be a robust tool for reducing the impact of the meteorological variability on the ozone concentrations. The explained variance of the model, averaged over all stations, ranged from 60.2% in winter to 71.9% in autumn. The year-to-year variability of the seasonal medians of daily ozone maxima was reduced by 85% in winter, 60% in summer, and 50% in autumn and spring after the meteorological adjustment. For most stations, no significantly negative trends (at the 95% confidence level) of the summer medians of daily O 3 or O x (O 3 +NO 2 ) maxima were found despite the significant reduction in the precursor emissions in Central Europe. However, significant downward trends in the summer 90th percentiles of daily O x maxima were observed at 6 sites in the region around Zürich (on average −0.73 ppb yr −1 for those sites). The lower effect of the titration by NO as a consequence of the reduced emissions could partially explain the significantly positive O 3 trends in the cold seasons (on average 0.69 ppb yr −1 in winter and 0.58 ppb yr −1 in autumn). The increase of O x found for most stations in autumn (on average 0.23 ppb yr −1 ) and winter (on average 0.39 ppb yr −1 ) could be due to increasing European background ozone levCorrespondence to: A. S. H. Prévôt (andre.prevot@psi.ch) els, in agreement with other studies. The statistical model was also able to explain the very high ozone concentrations in summer 2003, the warmest summer in Switzerland for at least ∼150 years. On average, the measured daily ozone maximum was 15 ppb (nearly 29%) higher than in the reference period summer 1992-2002, corresponding to an excess of 5 standard deviations of the summer means of daily ozone maxima in that period.

Accurate assessment of the magnitude and frequency of extreme wind speed is of fundamental importance for many safety, engineering and reinsurance applications. We utilize the spatial and temporal consistency of the European Centre for Medium Range Forecasts ERA-40 reanalysis data to determine the frequency of extreme winds associated with wind storms over the eastern North Atlantic and Europe. Two parameters are investigated: 10-m wind gust and 10-m wind speed. The analysis follows two different view-points: In a spatially distributed view, wind-storm statistics are determined individually at each grid-point. In an integral, more generalized view, the wind-storm statistics are determined from extreme wind indices (EWI) that summarize storm magnitude and spatial extent. We apply classical peak over threshold (POT) extreme value analysis techniques (EVA) to the EWI and grid-point wind data. As a reference, a catalogue of the 200 most prominent European storms has been compiled based on available literature. The EWI-based return periods (RP) estimates of catalogue wind storms range from approximately 0.1 to 300 years, whereas grid-point-based RP estimates range from 0.1 to 500+ years. EWIs sensitive to the absolute magnitude of wind speed rank the RP of wind storms in the 1989/1990 and 1999/2000 extended winter season similarly to the RP derived from the distributed approach. The RP estimates derived from EWIs are generally higher when calculated using only land grid-points compared to RPs derived using whole domain. Both the uncertainties in EWIs and grid-point-based RPs show a greater dependence on the wind parameter used than on the uncertainty associated with the EVA for RPs less than 10 years, whereas for RPs greater than 10 years the effect of the different datasets is lower. The EWIs share up to approximately 50% of the variability of the local grid-point RPs.

This paper investigates the improvement of seasonal forecasts by including realistically varying greenhouse gas (GHG) concentrations. Forecasts starting every May and November are compared over the period 1958 until 2001. One set has constant GHG concentrations while an other has a realistic GHG trend. The large scale temperature trends derived at different lead times are compared between the forecast sets and observations over the entire 44 years. It is shown that after a few months the anthropogenic climate change signal is lost by up to 70% although it was present in the initial conditions. The differences in trends vary with lead times, seasons and regions. Strongest effects are found in the Tropics and the Summer Hemispheres, in particular the Northern one. On the local scale, the improvement is not widespread in trends and very weak in predicting detrended interannual variability. Both sets exhibit a strong absolute temperature bias.