This paper describes the HISTALP database, consisting of monthly homogenised records of temperature, pressure, precipitation, sunshine and cloudiness for the ‘Greater Alpine Region’ (GAR, 4–19°E, 43–49°N, 0–3500m asl). The longest temperature and air pressure series extend back to 1760, precipitation to 1800, cloudiness to the 1840s and sunshine to the 1880s. A systematic QC procedure has been applied to the series and a high number of inhomogeneities (more than 2500) and outliers (more than 5000) have been detected and removed. The 557 HISTALP series are kept in different data modes: original and homogenised, gap‐filled and outlier corrected station mode series, grid‐1 series (anomaly fields at 1° × 1°, lat × long) and Coarse Resolution Subregional (CRS) mean series according to an EOF‐based regionalisation. The leading climate variability features within the GAR are discussed through selected examples and a concluding linear trend analysis for 100, 50 and 25‐year subperiods for the four horizontal and two altitudinal CRSs. Among the key findings of the trend analysis is the parallel centennial decrease/increase of both temperature and air pressure in the 19th/20th century. The 20th century increase (+1.2 °C/+ 1.1 hPa for annual GAR‐means) evolved stepwise with a first peak near 1950 and the second increase (1.3 °C/0.6hPa per 25 years) starting in the 1970s. Centennial and decadal scale temperature trends were identical for all subregions. Air pressure, sunshine and cloudiness show significant differences between low versus high elevations. A long‐term increase of the high‐elevation series relative to the low‐elevation series is given for sunshine and air pressure. Of special interest is the exceptional high correlation near 0.9 between the series on mean temperature and air pressure difference (high‐minus low‐elevation). This, further developed via some atmospheric statics and thermodynamics, allows the creation of ‘barometric temperature series’ without use of the measures of temperature. They support the measured temperature trends in the region. Precipitation shows the most significant regional and seasonal differences with, e.g., remarkable opposite 20th century evolution for NW (9% increase) versus SE (9% decrease). Other long‐ and short‐term features are discussed and indicate the promising potential of the new database for further analyses and applications. Copyright © 2006 Royal Meteorological Society.

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world’s water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded datasets (observations, reanalyses and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency towards enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high vs. low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded datasets (CRU, GISTEMP, GPCC, ERA5, CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in mid-latitudes. Agreement on elevation-dependent changes between gridded datasets is weak for temperature but stronger for precipitation.

The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

This paper investigates temperature variability in the Alps and their surroundings based on 97 instrumental series of monthly mean temperatures. A discussion of the initial homogenizing procedure illustrates its advantages and risks. A comparison of the homogenized series with the original series clearly shows the necessity to homogenize. Each of the original series had breaks (an average of five per series) and the mean of all series was systematically biased by non‐climatic noise. This noise has subdued the long‐term amplitude of the temperature evolution in the region by 0.5 K. The relatively high spatial resolution of the data enabled a regionalization within the study area of 680 000 km² into six sub‐regions based on principal component analysis of the monthly series. Long‐term temperature evolution proved to be highly similar across the region—thus making a mean series (averaged over all 97 single series) representative of the study area. Trend analysis (based on progressive forward and backward Mann–Kendall statistics and on progressive analysis of linear regression coefficients) was performed on seasonal and annual series. The results diverge from those of global datasets. This is mainly due to the extension of the 240‐year Alpine dataset by 100 years prior to the mid‐19th century, and also due to the advantages of a dense and homogenized regional dataset. The long‐term features include an initial decrease of the annual and seasonal series to a minimum followed by a positive trend until 1998. The minima are 1890 for the entire year and winter, 1840 for spring and 1920 for summer and autumn, respectively. The initial decreasing trend is more evident in spring and summer, less in autumn and smallest in winter. The mean annual temperature increase since 1890 in the Alps is 1.1 K, which is twice as much as the 0.55 K in the respective grid boxes of the most frequently used global dataset of the Climatic Research Unit (CRU), University of East Anglia. To enable an easier and more systematic handling of the dataset, these data have been interpolated to a 1°×1° longitude–latitude grid. The 105 low‐elevation and 16 high‐elevation grid point series are widely available without restrictions for scientific research and can be obtained from the authors. Copyright © 2001 Royal Meteorological Society

In this paper, we perform a detailed regional analysis of the link between meteorological drought indices and streamflow for a comprehensive Austrian data set of 47 small to medium‐size catchments in humid‐temperate climate. Four drought indices considering different components of the catchment water balance are tested. We assess the quality of the link using rank correlation analysis, and the probability of detecting low‐flow events by hit‐scores. Overall, correlations range between 0.4 and 0.8 and differ significantly between regions. A stratified analysis shows that the link is much stronger (i) for summer low flows and droughts than for anomalies in general, and (ii) for more humid than more arid conditions. Under more humid conditions streamflow droughts of small to medium‐size catchments are to a large extent generated by climate forcing and therefore well represented by a simple meteorological index. Under increasingly dry conditions, the climate signal gets less predictive, and storage properties of the underground become more important. A simple soil moisture accounting scheme (such as those of the Palmer index) can considerably improve the correlations. Overall, we conclude there is a significant link between meteorological drought and streamflow drought, except for catchments where groundwater storage and snow processes are important. The results are encouraging and provide a wealth of information which can profitably be used to set up statistical prediction models to estimate low flows from meteorological time series.