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Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

The vitality of Scots pine (Pinus sylvestris L.) is declining since the 1990s in many European regions. This was mostly attributed to the occurrence of hotter droughts, other climatic changes and secondary biotic stressors. However, it is still not well understood which specific atmospheric trends and extremes caused the observed spatio-temporal dieback patterns. In the Swiss Rhone valley, we identified negative precipitation anomalies between midsummer and early autumn as the main driver of sudden vitality decline and dieback events. Whereas climate change from 1981 to 2018 did not lead to a reduced water input within this time of the year, the potential evapotranspiration strongly increased in spring and summer. This prolonged and intensified the period of low soil moisture between midsummer and autumn, making Scots pines critically dependent on substantial precipitation events which temporarily reduce the increased water stress. Thus, local climate characteristics (namely midsummer to early autumn precipitation minima) are decisive for the spatial occurrence of vitality decline events, as the lowest minima outline the most affected regions within the Swiss Rhone valley. Mortality events will most likely spread to larger areas and accelerate the decline of Scots pines at lower elevations, whereas higher altitudes may remain suitable Scots pine habitats. The results from our regional study are relevant on larger geographic scales because the same processes seem to play a key role in other European regions increasingly affected by Scots pine dieback events., Frontiers in Forests and Global Change, 5, ISSN:2624-893X

Near-surface seasonal and annual mean wind speed in Switzerland is investigated using homogenized observations, Twentieth Century Reanalysis (20CRv2c) data and raw model output of a 75 member EURO-COoRdinated Downscaling EXperiment regional climate model (RCM) ensemble for present day and future scenarios. The wind speed observations show a significant decrease in the Alps and on the southern Alpine slopes in the period 1981–2010. However, the 20CRv2c data reveal that the recent trends lie well within the decadal variability over longer time periods and no clear signs of a systematic wind stilling can be found for Switzerland. The ensemble of RCMs shows large biases in the annual mean wind speed over the Jura mountains, and some members also show large biases in the Alps compared to station observations. The spatial distribution of the model biases varies strongly between the RCMs, while the resolution and the driving global model have less impact on the pattern of the model bias. The RCMs are mostly able to represent the seasonality of wind speed on the Plateau but miss important details in complex terrain related to local wind systems. Most models show no significant changes in near-surface mean wind speed until the end of the 21st century. The model ensemble changes range from a 7% decrease to a 6% increase with an ensemble mean decrease of 1 to 2%. Due to model biases, the scale mismatch between model grid and station observations and the missing representation of local winds in the simulations, the changes need to be interpreted with utmost care. Future assessments might lead to major revisions even for the sign of the projected changes, in particular over complex terrain.

Relative homogenization methods assume that measurements of nearby stations experience similar climate signals and rely therefore on dense station networks with high-temporal correlations. In developing countries such as Peru, however, networks often suffer from low-station density. The aim of this study is to quantify the influence of network density on homogenization. To this end, the homogenization method HOMER was applied to an artificially thinned Swiss network. Four homogenization experiments, reflecting different homogenization approaches, were examined. Such approaches include diverse levels of interaction of the homogenization operators with HOMER, and different application of metadata. To evaluate the performance of HOMER in the sparse networks, a reference series was built by applying HOMER under the best possible conditions. Applied in completely automatic mode, HOMER decreases the reliability of temperature records. Therefore, automatic use of HOMER is not recommended. If HOMER is applied in interactive mode, the reliability of temperature and precipitation data may be increased in sparse networks. However, breakpoints must be inserted conservatively. Information from metadata should be used only to determine the exact timing of statistically detected breaks. Insertion of additional breakpoints based solely on metadata may lead to harmful corrections due to the high noise in sparse networks. Por pares

The Greater Alpine Region (the GAR) covering the area between 4-19°E and 43-50°N and and altitude range between = and more than 4000 m asl. offers a challenging climate worth to be studied in any detail. However, it is surprising that up to now no comprehensive Alpine Temperature Climatology covering the whole region is existing. To overcome this deficiency as a first step we want to produce monthly temperature maps for this region in spatial resolution as high as possible. The period under investigation will be 1961-1990. In this paper we will describe the first steps of our initiative as well as further plans.

In the final phase of EU-project ALPCLIM (1999-2002) and as a pre-condition for EU-project ALP-IMP (2003-2006) and Austrian national funded project CLIVALP (2002-2005) a new instrumental precipitation dataset was created. It covers the Alps and its wider surroundings (4 to 19 deg E, 43 to 49 deg N), has a high spatial resolution (apprx. 180 single site series of monthly precipitation sums), covers the entire instrumental period (the longest series starting in the year 1800), meets high quality standards due to an extensive quality and homogeneity testing, correcting and adjusting procedure and has been interpolated to a regular grid of 1 deg long-lat. It supplements the already available and published similar temperature dataset for the region and it will be followed by similar datasets for other climate elements (work in progress already for air-pressure and sunshine/cloudiness, other elements to be started). Unlike temperature, whose long-term variability in time turned out to be rather homogeneous in space, precipitation variability shows interesting spatial variability features. One dominant feature seems to be a dipole-like long-term structure from a mode of high precipitation S and SE of the Alps and low precipitation NW and N of the Alps in the 19th century which has changed now to the inverse mode (wet NW-N versus dry S-SE) which dominates the recent decades. Another topic of the presentation will be the spatial structure and the variability of the seasonal precipitation patterns. The study region shows steep spatial gradients between different seasonal types (dominating summer precipitation, spring and/or autumn maximum, flat annual course… ) which are not stable in time. Last but not least the quality test procedure (based on GIS-sustained single month spatial comparisons) allows the data-set also to be used for extreme event studies (both for months with excessive precipitation as well as dry months). First such long-term trends of extreme precipitation will be shown.

Das Klimaelement Niederschlag zeigt auf allen der raum-zeitlichen Skalen starke Variabilität, auch im langfristigen Trendverhalten. Ein neuer, räumlich dichter Datensatz von 192 homogenisierten instrumentellen Reihen in monatlicher Auflösung im Großraum Alpen wird hier vorgestellt , der bis zum Jahr 1800 zurückreicht. Erste Analysen deuten auf eine systematische Langfrist-Oszillation zwischen gegenläufigen Trends NW-lich und SE-lich der Alpen hin.