Instrumental observations and reconstructions of global and hemispheric temperature evolution reveal a pronounced warming during the past approximately 150 years. One expression of this warming is the observed increase in the occurrence of heatwaves. Conceptually this increase is understood as a shift of the statistical distribution towards warmer temperatures, while changes in the width of the distribution are often considered small. Here we show that this framework fails to explain the record-breaking central European summer temperatures in 2003, although it is consistent with observations from previous years. We find that an event like that of summer 2003 is statistically extremely unlikely, even when the observed warming is taken into account. We propose that a regime with an increased variability of temperatures (in addition to increases in mean temperature) may be able to account for summer 2003. To test this proposal, we simulate possible future European climate with a regional climate model in a scenario with increased atmospheric greenhouse-gas concentrations, and find that temperature variability increases by up to 100%, with maximum changes in central and eastern Europe.

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Observational errors may have a serious impact on objective analyses. Before conducting an objective analysis, that is, interpolating irregularly spaced observations to a uniform grid, the data should be checked thoroughly for errors. For this procedure a piecewise functional fitting approach is proposed, which is based on a variational algorithm. As for thin-plate splines, an integral of squares of second temporal and/or spatial derivatives is minimized. The second derivatives are obtained from overlapping finite elements using a polynomial approach. In a slightly different mode, the same approach may also be used to interpolate the observational data to a regular grid. The method is formulated for and applied to scalar and vector quantities in a one-and a twodimensional domain. The basic advantages of the method are on the one hand the fact that no first guess or (prognostic) model field is necessary and on the other hand that no a priori knowledge about structure or weighting functions is required. Furthermore the spatial anisotropy of meteorological fields may be treated explicitly. One of the most valuable features of the method is its simplicity. For a single station it is possible to recalculate by hand each step, which may make the procedure transparent. The comparatively inexpensive computational effort renders it especially well suited to model-independent quality assessment procedures and mesoscale objective analyses. It is presently used within the framework of the Mesoscale Alpine Programme.

This paper summarizes the findings of seven years of research on föhn conducted within the project 'Föhn in the Rhine Valley during MAP' (FORM) of the Mesoscale Alpine Programme (MAP). It starts with a brief historical review of föhn research in the Alps, reaching back to the middle of the 19th century. Afterwards, it provides an overview of the experimental and numerical challenges identified before the MAP field experiment and summarizes the key findings made during MAP in observation, simulation and theory. We specifically address the role of the upstream and cross-Alpine flow structure on föhn at a local scale and the processes driving föhn propagation in the Rhine Valley. The crucial importance of interactions between the föhn and cold-air pools frequently filling the lower Rhine Valley is highlighted. In addition, the dynamics of a low-level flow splitting occurring at a valley bifurcation between the Rhine Valley and the Seez Valley are examined. The advances in numerical modelling and forecasting of föhn events in the Rhine Valley are also underlined. Finally, we discuss the main differences between föhn dynamics in the Rhine Valley area and in the Wipp/Inn Valley region and point out some open research questions needing further investigation.