Changes in air temperature, but not in precipitation, determine long-term trends in water chemistry of high mountain lakes of the Alps with and without rock glacier influence.

Climate change has strongly affected lakes around the world, but the relative effects of warmer air temperatures and changing precipitation on the water chemistry of alpine systems are not well understood. Here we tested the effect of monthly and seasonal climate on the water chemistry of six high mountain lakes located in the Alps. From 1982 to 2020, water samples were collected annually from different depths during the autumn mixing. We observed a simultaneous increase in electrical conductivity, ionic content, and pH with air temperature. In lakes with rock glacier influence, the increase in conductivity, ionic content, and especially in sulfate was even more pronounced, but accompanied by a strong decrease in pH. These differences are attributed to the direct influence of acidic meltwater from active rock glaciers in catchments with acidic bedrock. We then analyzed changes in lake chemistry, taking into account seasonal trends in air temperature and precipitation, using redundancy analysis. Temperature increase significantly affected water chemistry in five of the six lakes, especially at times of ice breakup. Increasing warming explained 17% to 32% of the changes in electrical conductivity, alkalinity, pH, major ions, and nitrogen. In contrast, precipitation had little effect on the changes of those parameters. Nevertheless, late spring snowfall and high snowfall in early fall, which result in prolonged ice cover, had a dampening effect on the impact of climate warming on lake chemistry. Our results confirm that climate warming remains a major driver of chemical changes in alpine lakes, but provide new evidence that late spring temperatures are the most important triggers.

The global historical climate database HCLIM.

There is a growing need for past weather and climate data to support science and decision-making. This paper describes the compilation and construction of a global multivariable (air temperature, pressure, precipitation sum, number of precipitation days) monthly instrumental climate database that encompasses a substantial body of the known early instrumental time series. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate dataset for the preindustrial period so far.

Evaluation of homogenization methods for seasonal snow depth data in the Austrian Alps, 1930-2010.

Despite the importance of snow in alpine regions, little attention has been given to the homogenization of snow depth time series. Snow depth time series are generally characterized by high spatial heterogeneity and low correlation among the time series, and the homogenization thereof is therefore challenging. In this work, we present a comparison between two homogenization methods for mean seasonal snow depth time series available for Austria: the standard normal homogeneity test (SNHT) and HOMOP. The results of the two methods are generally in good agreement for high elevation sites. For low elevation sites, HOMOP often identifies suspicious breakpoints (that cannot be confirmed by metadata and only occur in relation to seasons with particularly low mean snow depth), while the SNHT classifies the time series as homogeneous. We therefore suggest applying both methods to verify the reliability of the detected breakpoints. The number of computed anomalies is more sensitive to inhomogeneities than trend analysis performed with the Mann-Kendall test. Nevertheless, the homogenized dataset shows an increased number of stations with negative snow depth trends and characterized by consecutive negative anomalies starting from the late 1980s and early 1990s, which was in agreement with the observations available for several stations in the Alps. In summary, homogenization of snow depth data is possible, relevant and should be carried out prior to performing climatological analysis.