This presentation addresses the challenge of generating high-resolution spatial wind climatologies for Switzerland, a region characterized by complex mountainous terrain and, for the purpose of mapping wind, a sparse measurement network. Accurately mapping wind patterns in such areas is thus inherently difficult but of great importance for supporting applications such as risk assessment or wind energy planning. In this study we explore three different and complementary approaches to tackling this challenge: Model-only: a new test data set, the Swiss ICON Reanalysis-Light1-CH1 (REA-L), has recently been produced by MeteoSwiss for the period 2005-2024 at 1km mesh-size over the ICON-CH1-EPS domain. Long-term climatologies for mean wind and wind gusts have been produced from this data set. Machine-Learning: in an attempt to further downscale the ICON REA-L surface fields to sub-kilometer scale, a ML approach based on Gaussian Processes has been developped within obsweatherscale, an open-source Python package that integrates station measurements, high-resolution topographic descriptors, and auxiliary atmospheric predictors to produce a continuous spatial distribution of the target variable. The method captures nonlinear wind-terrain interactions, provides uncertainty estimates, and remains physically interpretable. Station Transfer: this approach follows classical statistics to estimate the full statistical distribution for mean wind and wind gusts at each grid point based on station measurements from 1981 – 2025 and topographic information. The resulting outcomes are intercompared, verified, and explored regarding their consistency and accuracy, and to gather the pros and cons for each method. A special focus is on their suitability to accurately describe extremes. We present the resulting climatological fields as well as some case-study verification of selected wind events over Switzerland. References: Lloréns Jover, I., & Zanetta, F (2024). obsweatherscale: observation-conditioned ML downscaling of surface weather fields. GitHub repository: https://github.com/MeteoSwiss/obsweatherscale Zanetta, F., Nerini, D., Buzzi, M., & Moss, H. (2025). Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks. Artificial Intelligence for the Earth Systems.

Switzerland's current Energy Strategy 2050 pursues two key objectives: Abandoning nuclear power plants in the long term and supporting climate policy that implements measures to achieve the goals of the Paris Climate Change Agreement, which essentially means achieving zero net emissions of greenhouse gases by 2050. The strategy therefore includes the promotion of renewable energies in Switzerland as well as savings and market measures to increase the efficiency of the energy supply. The strong dependence of both the production and consumption of energy on weather means that there is a great need for customized weather and climate information. This need suddenly became urgent in the fall 2022, when the conflict in Ukraine jeopardized the supply of natural gas and, in addition, water reserves were significantly below average after a dry summer, which also jeopardized domestic energy production strongly depending on hydropower. In this situation, MeteoSwiss quickly developed a special weekly bulletin for the energy sector providing a detailed picture on past and future heating energy demand. It is based on tools from established climate and temperature forecasting services and tailored to the energy sector by providing monitoring and forecasts in the form of heating degree days (HDD). Expressing both monitoring and sub-seasonal to seasonal forecasts in terms of HDD and relative to climatology proved to be useful identifying energy shortages and taking early action. As there is inevitably considerable uncertainty associated with long-term forecasts, one of the challenges was to convey this uncertainty while still providing actionable information. We present the different product elements of the energy bulletin and then discuss our experience in this example of rapid service development in response to an urgent need, covering both technical and organizational aspects.

Describing the climate evolution using trend lines and estimating the current climate mean (CCM) on the local scale is an important climate service. For an increasing number of variables, accelerating climate change disqualifies the use of traditional climatological normals and long-term linear trends as CCM estimators. Although several alternatives are available and already in use, there are few comprehensive assessments of the different approaches let alone a consensus for recommending a particular method. Here we evaluate frequently used approaches that use past climate data to estimate the CCM applying several transparent criteria. The performance is assessed in a perfect model framework for the strongly changing Swiss mean temperature 1864–2099 with the centered 30-year mean as CCM benchmark. Short-term linear trends, cubic splines and local linear regression with optimized parameters all provide unbiased CCM estimates for a broad range of climate evolutions and independent of trend magnitudes. To enable broad usability, additional criteria are considered such as a wide applicability to a large number of climate variables and simplicity in terms of use, settings and communication. In the overall assessment, local linear regression emerges as a particularly promising method to describe nonlinear climate trends and to determine the CCM. The criteria-based assessment approach has proven very useful in choosing a method as objectively as possible. We present ideas for modern climate services to complement the toolbox of climate monitoring and encourage the community to develop recommendations at the international level to increase the coherence, objectivity and robustness of climate monitoring products.

Same as other parts of Europe, the inner-Alpine Swiss Rhône valley has been increasingly affected by Scots pine (Pinus sylvestris L.) dieback events since the 1990s. Such events were not confined to years of extreme heat and drought across Switzerland and Europe such as 2003 or 2018, and the severity and frequency of sudden tree mortality varied on relatively small spatial scales. Which are the relevant parameters that changed in time and which factors triggered these dieback events?We found that sudden mortality events occurred exclusively after periods of below average precipitation between July and September. During this time of the year, soil moisture regularly drops to a minimum while the atmospheric water demand is high. Further factors such as insect infestation or spring frost may increase the magnitude of tree mortality, but they were neither a required contributor nor were they found to trigger dieback events. Consequently, the region with lowest summer precipitation within the Swiss Rhône valley outlines the area most affected by Scots pine dieback.However, the amount and frequency of the highly variable summer precipitation did not decrease since the 1980s, but the atmospheric water demand in spring and summer increased continuously. As a result of the higher water loss to the atmosphere, the period of low soil moisture has been prolongated and intensified. Therefore, Scots pines have become more dependent on (temporary) water stress relief by precipitation events during mid and late summer.Many Scots pine died (most likely due to hydraulic failure) within months following severe summer water stress. The effects of such periods appeared faster on tree crown defoliation (i.e., the proportion of needles that should be present on a tree, but which have been lost) than on mortality, as some trees died only after a year or two. We found that these Scots pines exceeded a defoliation threshold of about 75 % and were unable to recover. In such strongly defoliated trees, stress-related metabolites increase in needles, but get depleted in roots, indicating that mortality is linked to belowground carbon starvation negatively affecting functions central for tree survival.

Austrian observations of snow depth date back to 1895 and are thus among the longest available quantitative snow information from hydrometeorological networks worldwide. It is well known that such long‐term observations are prone to inhomogeneities, which may not only affect climatologies and trends, but derived products used in research or practice. While the reliability of available methods for detecting breaks in snow time series has been shown before and could also be confirmed by our work, we focused on improving the adjustment method. Conventional methods often refer to the median of difference or quotient series (INTERP), whereas our proposed method also uses a quantile‐wise adjustment (InterpQM), which is useful to minimize a bias on the tails of the frequency distribution. We demonstrated the success of the new method by using Swiss parallel snow depth observations. Errors of the analysed indicators could be reduced in 68% of the cases when compared with INTERP. The results were best for large snow depths, being up to 19% better. Overall, InterpQM was better in 75% of validation cases for the daily large, 72% of all observations and 56% of mean seasonal snow depth cases. We describe the performed homogenization procedure in detail, including quality control, gap filling, homogeneity testing, break detection, calculation of and improvements to the adjustment method. Our results show that snow depth time series generally have a lower number of breaks compared with station data of other climate variables. This underlines their high quality, even if measuring snow presents challenges. Using Austrian snow depth series as an example, the effects of the new adjustment method on trends were analysed using the Mann–Kendall and Sen's Slope. Homogenization may have a significant effect on derived trends: Two of the six adjusted series were changed from nonsignificant to significant and one vice versa.

Many planning applications require statistical information about the climate at the scale of a point. In civil engineering, for example, the design of heating and cooling systems relies on estimates of the occurrence of cold/warm extremes at the location of the building. An emerging practice of climate service providers is to derive such information from interpolation-based or reanalysis-based climate datasets on a fine (km-scale) grid. Yet, such datasets are poorly suited for this purpose. The effective resolution is limited and, as a consequence, this data represents regional area-mean conditions, not the point scale. As a result, the frequency of extremes is underestimated, and cross-parameter relationships are compromised. Here, we propose and illustrate a statistical procedure to derive point-scale climate data, at any point in space, without the artefacts of traditional optimal-prediction-based interpolation. The proposed method, denoted “station transfer”, derives a climate series for the target location by transferring the observations of a suitable station and applying an adjustment, so that the result is representative for the target location. The choice of station and the adjustment are fully data driven. Unlike conventional interpolation, where concomitant observations in the neighborhood are combined, the “station transfer” insists on a single station as the origin of an adjustment, which is less invasive and better preserves point-scale tail statistics and cross-parameter relationships. We introduce a stochastic model for the transfer where the adjustments and the areas of representativity are jointly estimated from the entire station network. The method is illustrated with an example application that predicts point-scale surface air temperatures over the territory of Switzerland, using data from 65 stations. The development and experimental application of this presentation are preliminaries of an upcoming project, where MeteoSwiss will derive new building-design climate basics for the Swiss Society of Engineers and Architects. But “station transfer” may become a much more widely used complement to conventional gridding, because of the wide-spread request for point-scale and high-frequency climate information in civil planning.

Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Measurements of snow depth can vary dramatically over small distances, and as with any other meteorological variable, snow depth time series are affected by inhomogeneities or break points. Such inhomogeneities can arise due to e.g.; changes of instrumentation, changes to station location and observer practices, or changes in the local environment such as urbanisation or plant growth. In order to analyse and monitor variation in snow depth time series accurately, homogenised snow data series are required. In deriving such homogenised series, it is essential to understand the characteristics and impacts of inhomogeneities. Having applied some pre-selection criteria to identify candidate series, time series homogenization for 184 Swiss snow depth series was performed using ACMANT, Climatol, and HOMER, three state-of-the-art break detection algorithms. For the 91 year base period of 1931-2021, we investigated which method and set-up worked best for detecting breaks in this network of Swiss snow data series. The approach identified valid break points in 25% of the series, with HOMER identifying more valid breaks than either ACMANT or Climatol. By evaluating the network using multiple methods, there is more confidence that the results can be applied to snow time series with insufficient metadata or no immediately nearby reference stations in order to include them in future homogenisation efforts.

Abstract. Our current knowledge on snow depth trends is based almost exclusively on these non-homogenized data.Long-term observations of deposited snow are well suited as indicator of climate change. However, like all other long-term observations, they are prone to inhomogeneities that can influence and change trends if not taken into account. We investigated the effects of removing inhomogeneities in the large network of Swiss snow depth observations on trends and extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depth in the period 1961–2021. For this task, three homogenization methods were applied: Climatol and HOMER, which use a median based adjustment method, and interpQM, which applies quantile based adjustments. All three were run using the same break points and input data. This allowed us to investigate and quantify the effects of these methods on the homogenization results. We found that all three methods agree well on trends in seasonal average snow depth, while differences are visible for seasonal maximum snow depth and the corresponding extreme values. Here, the quantile-based method performed slightly better than the two median-based methods, as it had the smallest number of stations outside the 95 % confidence interval for 50-year return periods of maximum snow depth. These differences are mainly caused by the way the reference series are selected. The combination of a high minimum correlation (>0.7) and restrictions in vertical (<300 m) and horizontal (<100 km) distances proved to be better suited than only using correlations or distances respectively as criteria. The adjustments removed all positive trends for snow days in the original data and strengthened the negative mean trend, especially for stations >1500 m. In addition, the number of significant negative stations was increased between 7–21 %, with the strongest changes at higher snow depths.

Determining the current climate mean state (CCMS) on the regional and local scale is an important task of climate monitoring. The CCMS and the long-term climate change signal derived from it are relevant for a wide range of users. For climate variables with strong, possibly non-linear trends, accelerating climate change more and more disqualifies the use of traditional normals and long-term linear trends. Although several alternatives are in use, there are few comprehensive assessments of different approaches to estimate the CCMS, let alone a consensus on a new widely applicable standard. Here, we identify approaches based on historical data that allow accurate estimates, mainly using the example of the strongly changing Swiss mean temperature. The performance is assessed for the past and future combining long-term observations and climate projections with the centred 30-year mean (15 years observations, 15 years predictions) as CCMS benchmark. Several approaches, e.g. short-term linear trends, cubic splines and weighted local linear regression (LOESS) provide unbiased CCMS estimates for a broad range of climate scenarios and independent of trend magnitudes. Additional requirements such as the applicability to a wide range of variables, simplicity and the straightforward availability of uncertainty information are used to identify the most-suitable approaches. LOESS emerged as the most promising method in the overall assessment. KNMI already uses LOESS and MeteoSwiss plans to implement and use LOESS operationally in the near future. It will become MeteoSwiss’ new standard for determining long-term climate change signals and will also replace the Gaussian smoother currently used to visualise the evolution of climate variables.

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 Rhône 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 Rhône 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.

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.

Determining the current state of the climate is a core task of climate monitoring and climate information in every climate service department. Traditionally, averages over a recent 30-year period, so called climate normals, are used for this purpose. However, the classic concept of climate normals is based on the assumption of a stationary climate. Due to climate change, this stationarity assumption is violated for some variables such as temperature and climate normals can deviate considerably from the true current state of the climate. Since 2012, the World Meteorological Organization recommends updating climate normals more frequently, every 10 years instead of every 30 years. The scientific literature however shows that further alternative approaches are desirable and can potentially help users make better informed decisions. MeteoSwiss is currently examining the possibilities of introducing supplementary estimates that better describe the current state of the climate. In this presentation we discuss statistical properties of a series of alternative estimates such as shorter averaging periods, different linear trend fits and applying smoothed curve fitting (e.g. cubic splines, kernel regression). The analysis is applied for the testbed of Switzerland using a perfect model framework for combined observational/climate scenario temperature series. The results allow to determine if supplementary estimates are superior to the classical normal or not and are a central component for deciding whether alternative to the classical normals should be introduced. Another important goal of this presentation is to initiate a discussion among climate service providers about their thoughts, experiences and approaches in defining the current climate state.

Measurements of snow depth and snowfall can vary dramatically over small distances. However, it is not clear if this applies to all derived variables and is the same for all seasons. Almost all meteorological time series incorporate some sort of inhomogeneities. Complete metadata and existing “parallel” stations in close proximity are not always available. First, we analyse the impacts of local-scale variations based on a unique set of parallel manual snow measurements for the Swiss Alps consisting of 30 station pairs with up to 70 years of parallel data. Station pairs are mostly located in the same villages (or within 3km horizontal and 150m vertical distances). Seasonal analysis of derived snow climate indicators such as maximum seasonal snow depth, sum of new snow, or days with snow on the ground shows that largest differences occur in spring and the smallest ones are found in DJF and NDJFMA. Relative inter-pair differences (uncertainties) for days with snow on the ground (average snow depth) are below 15% for 90% (30%) . Second, in view of any homogenization efforts of snow data series, it is paramount to understand the impacts of inhomogeneities. Using state-of-the-art break detection algorithms, we strive to investigate which method works best for detecting breaks in snow data series. The results can then be used on time series with insufficient metadata or no neighbouring stations in order to include them in future homogenization processes. Furthermore, the knowledge about inhomogeneities and breakpoints paves the way for new applications such as the reliable combination of two parallel series into one single series.

Daily measurements of snow depth and snowfall can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are usually located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall, and snow onset and disappearance dates, which are calculated for various seasons (December to February (DJF), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential variability. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA, which show median variations of less than 5 % for all indicators. Local-scale variability ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. The highest percentage (90 %) of station pairs with variability of less than 15 % is observed for days with snow on the ground. The lowest percentage (30 %) of station pairs with variability of less than 15 % is observed for average snow depth. Median differences of snow disappearance dates are rather small (3 d) and similar to the ones found for snow onset dates (2 d). An analysis of potential sunshine duration could not explain the higher variabilities in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3 d new snow sum, which are often used for avalanche prevention measures. The found return levels are within each other's 95 % confidence intervals for all (but three) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of variabilities of commonly used snow indicators and extremal indices. Knowledge about such variabilities in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

Forest decline has been attributed to climatic changes in many parts of the world. Although climate conditions are an undisputed crucial factor affecting tree vitality, open questions remain regarding the relative roles of evaporative demand versus precipitation and the relative importance of individual climate variables. In recent decades, there was a pronounced decline of Scots pines (Pinus sylvestris) at lower elevations in the inner-alpine Rhône valley in Switzerland. Similar observations were made in other inner-alpine valleys. Tree vitality was not continuously decreasing: single events of strong decrease in tree vitality and high mortality rates were observed in between phases of largely constant vitality levels. However, trees were hardly able to recover from such events in recent decades, resulting in a pronounced decrease in living Scots pines. Climate-trend signals in the Rhône valley from 1981 to 2018 vary between the seasons. The clearest changes occurred in spring, when a strong climatic shift towards drier conditions was detected with significantly increasing evapotranspiration, decreasing precipitation sums and frequency of precipitation events, increasing duration of dry spells at lower elevations, and increasing diurnal temperature ranges. Relative trends of evapotranspiration are elevation dependent with the highest increase at low elevations. Temperature trends are the main driver towards higher evapotranspiration rates, but humidity and sunshine duration are important drivers too. For seasonal evapotranspiration anomalies, anomalies of temperature, humidity, and sunshine duration are of similar importance. In previous works on Scots pine mortality in the Rhône valley, mortality events were attributed to prolonged periods of water deficits. However, the occurrence and magnitude of mortality events cannot be explained by droughts only. In case of Scots pines at low elevations in the Rhône valley, factors such as insect infestation and spring frost may strongly impact tree vitality and increase mortality rates. In summary, climatic conditions changed markedly in the Swiss Rhône valley within the last approximately 40 years, especially in spring and at lower elevations. Hence, the less favorable climatic background conditions result in decreased resilience and recuperative power of the Scots pines to various disturbances, leading to the observed forest decline.

Snow on the ground is an important climate variable which is normally measured either as snow depth or height of new snow. Like any other meteorological variable, manually measured snow is prone to local influences, changes in the environment or procedure of the measurements. In order to investigate the robustness of snow measurement series towards such non-climatic changes, a unique set of parallel manual snow measurements over 25 years from 23 station pairs between 490 and 1800 m a.s.l. was compiled. A sensitivity analysis based on typical snow climate indicators (e.g., mean snow depth, sum of new snow) from these parallel time series was carried out to find the most robust snow climate indicators for climatological analyses. Results show that there are only small differences in the sensitivity of the various snow climate indicators with regards to local changes. However, the indicators number of days with snow on the ground as well as the maximum snow depth are least affected by local influences and changes at station level. Median values of all station pairs reveal relative differences of about 7% for the number of days with snow cover and 11-16% for all other indicators. However, in extreme cases, the deviations within a single station pair can reach 25-40%.

&lt;p&gt;Switzerland has a unique dataset of long-term manual daily snow depth time series ranging back more than 100 years for some stations. This makes the dataset predestined to be analyzed in a climatological sense. However, there are sometimes shorter (weeks, months) or longer (years) gaps in these manual snow depth series, which hinder a sound climatological analysis and reasonable conclusions. Therefore, we examine different methods for filling data gaps in daily snow depth series. We focus on longer gaps and use different methods of spatial interpolation, temperature index models and machine learning approaches to fill the data gaps. We assess the performance of the different methods by creating synthetic data gaps and set the applicability of the methods in relation to the density of the available neighboring stations, elevation and climatic setting of the target station.&lt;/p&gt;

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.

Phenological data have become increasingly important as indicators of long-term climate change. Consequently, long-term homogeneity of the records is an important aspect. In this paper, we apply a breakpoint detection algorithm to the phenological series from the Swiss Phenology Network (SPN). A combination of three statistical tests is applied and different constraints are tested with respect to the choice of reference series. Breakpoint detection is only possible for a fraction of the series due to the shortness of some series and the lack of suitable reference series. Spring phases are more likely to be suitable than fall phases because of their higher spatial correlation. Out of nearly 3000 phenological series with at least 20 data points, only about 5% were found to be significantly inhomogeneous, although a visual validation indicates that many mid-sized breakpoints remained undetected. The detected breakpoints were compared with metadata and more than half of them could be attributed to a change of observer.