Spatially and temporally high-resolution estimates of past natural climate variability are important to assess recent significant climate trends. The mid-latitude atmospheric circulation is the dominant factor for regional changes in temperature, rainfall, and other climatic variables. Here we present reconstructions of gridded monthly sea level pressure (SLP) fields back to 1659 and seasonal reconstructions from 1500-1658 for the eastern North Atlantic-European region (30°W to 40°E; 30°N to 70°N). These were developed using principal component regression analysis based on the combination of early instrumental station series (pressure, temperature and precipitation) and documentary proxy data from Eurasian sites. The relationships were derived over the 1901-1960 calibration period and verified over 1961-1990. Under the assumption of stationarity in the statistical relationships, a transfer function derived over the 1901-1990 period was used to reconstruct the 500-year largescale SLP fields. Systematic quality testing indicated reliable winter reconstructions throughout the entire period. Lower skill was obtained for the other seasons, although meaningful monthly reconstructions were available from around 1700 onwards, when station pressure series became available. The quality and the reconstructed SLP fields for two exceptionally cold years (1573, 1740) are discussed and climatologically interpreted. An EOF analysis of the 1500-1999 winter SLP revealed, firstly, a zonal flow pattern with pronounced decadal to centenial time scale variations, secondly, a monopole pattern over northwest Europe and thirdly, a pattern modulating the meridional flow component over Europe. These 500year SLP reconstructions should be useful for modelling studies, particulary for analyses of low-frequency atmospheric variability and for circulation dynamics.

Abstract. Instrumental station pressure, temperature and precipitation measurements and proxy data were used to statistically reconstruct monthly time series of the North Atlantic Oscillation (NAO) and the Eurasian (EU) circulation indices back to 1675. Systematic testing of the reconstruction procedure indicated generally reliable reconstructions throughout the entire period, except for summertime before about 1750. Predictive skill varied for different sub-periods depending on data availability. It was highest for autumn and winter and was generally better for the EU than for the NAO index. Wavelet analysis suggested significant low-frequency variability, especially for the spring, summer and annual averaged indices. The co-variability between the NAO and EU indices was found to exhibit large decadal to century timescale variations, indicating that climate variability over the continent is temporarily decoupled from the NAO.

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The purpose of this study is to construct and evaluate a new gridded analysis of precipitation that covers the entire region of the European Alps (43.2-48.8°N, 3.2-16.2°E), resolves the most prominent mesoscale variations (grid spacing 25 km) and extends with a monthly time-resolution over most of the 20th century . The analysis is based on a reconstruction using the reduced-space optimal interpolation technique. It combines data from a high-resolution network over a restricted time period (1971-90) with homogeneous centennial records from a sparse sample of stations. The reconstructed fields account for 78% of the total variance in a cross-validation with independent data. The explained variance for individual grid points varies between 60 and 95%, with lower skills over the southern and western parts of the domain. For averages over 100 × 100 km 2 subdomains, the explained variance increases to 90-99%. Comparison of the reconstruction with the CRU05 global analysis reveals good agreement with respect to the interannual variations of large subdomain averages (10 000-50 000 km 2 ), some differences in decadal variations, especially for recent decades, and physically more plausible spatial patterns in the present analysis.The new dataset is exploited to depict 20th century precipitation variations and their correlations with the North Atlantic oscillation (NAO). A linear trend analysis (1901-90) reveals an increase of winter precipitation by 20-30% per 100 years in the western part of the Alps, and a decrease of autumn precipitation by 20-40% to the south of the main ridge. Correlations with the NAO index (NAOI) are weak and highly intermittent to the north and weak and more robust to the south of the main Alpine crest, indicating that changes in the NAOI in recent decades are not of primary importance in explaining observed precipitation changes.

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Abstract.Atmospheric circulation determines to a considerable extent the variability of lower stratospheric ozone and can modulate its long-term trends in Europe and the North Atlantic Region. Due to dynamical stratospheretroposphere coupling, important features of the variability of the surface pressure field are reflected in the long-term total ozone record from Arosa, Switzerland. Significant (p<0.01) correlations between total ozone and different atmospheric

Abstract-A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.