A paper published in Global Change Biology in 2006 revealed that phenological responses in 1971–2000 matched the warming pattern in Europe, but a lack of chilling and adaptation in farming may have reversed these findings. Therefore, for 1951–2018 in a corresponding data set, we determined changes as linear trends and analysed their variation by plant traits/groups, across season and time as well as their attribution to warming following IPCC methodology. Although spring and summer phases in wild plants advanced less (maximum advances in 1978–2007), more (~90%) and more significant (~60%) negative trends were present, being stronger in early spring, at higher elevations, but smaller for nonwoody insect‐pollinated species. These trends were strongly attributable to winter and spring warming. Findings for crop spring phases were similar, but were less pronounced. There were clearer and attributable signs for a delayed senescence in response to winter and spring warming. These changes resulted in a longer growing season, but a constant generative period in wild plants and a shortened one in agricultural crops. Phenology determined by farmers’ decisions differed noticeably from the purely climatic driven phases with smaller percentages of advancing (~75%) trends, but farmers’ spring activities were the only group with reinforced advancement, suggesting adaptation. Trends in farmers’ spring and summer activities were very likely/likely associated with the warming pattern. In contrast, the advance in autumn farming phases was significantly associated with below average summer warming. Thus, under ongoing climate change with decreased chilling the advancing phenology in spring and summer is still attributable to warming; even the farmers’ activities in these seasons mirror, to a lesser extent, the warming. Our findings point to adaptation to climate change in agriculture and reveal diverse implications for terrestrial ecosystems; the strong attribution supports the necessary mediation of warming impacts to the general public.

Abstract. A continuous, 36-year measurement composite of atmospheric carbon dioxide (CO2) at three measurement locations on Mount Zugspitze, Germany, was studied. For a comprehensive site characterization of Mount Zugspitze, analyses of CO2 weekly periodicity and diurnal cycle were performed to provide evidence for local sources and sinks, showing clear weekday to weekend differences, with dominantly higher CO2 levels during the daytime on weekdays. A case study of atmospheric trace gases (CO and NO) and the passenger numbers to the summit indicate that CO2 sources close by did not result from tourist activities but instead obviously from anthropogenic pollution in the near vicinity. Such analysis of local effects is an indispensable requirement for selecting representative data at orographic complex measurement sites. The CO2 trend and seasonality were then analyzed by background data selection and decomposition of the long-term time series into trend and seasonal components. The mean CO2 annual growth rate over the 36-year period at Zugspitze is 1.8±0.4 ppm yr−1, which is in good agreement with Mauna Loa station and global means. The peak-to-trough amplitude of the mean CO2 seasonal cycle is 12.4±0.6 ppm at Mount Zugspitze (after data selection: 10.5±0.5 ppm), which is much lower than at nearby measurement sites at Mount Wank (15.9±1.5 ppm) and Schauinsland (15.9±1.0 ppm), but following a similar seasonal pattern.

Abstract. Critical data selection is essential for determining representative baseline levels of atmospheric trace gases even at remote measurement sites. Different data selection techniques have been used around the world, which could potentially lead to reduced compatibility when comparing data from different stations. This paper presents a novel statistical data selection method named adaptive diurnal minimum variation selection (ADVS) based on CO2 diurnal patterns typically occurring at elevated mountain stations. Its capability and applicability were studied on records of atmospheric CO2 observations at six Global Atmosphere Watch stations in Europe, namely, Zugspitze-Schneefernerhaus (Germany), Sonnblick (Austria), Jungfraujoch (Switzerland), Izaña (Spain), Schauinsland (Germany), and Hohenpeissenberg (Germany). Three other frequently applied statistical data selection methods were included for comparison. Among the studied methods, our ADVS method resulted in a lower fraction of data selected as a baseline with lower maxima during winter and higher minima during summer in the selected data. The measured time series were analyzed for long-term trends and seasonality by a seasonal-trend decomposition technique. In contrast to unselected data, mean annual growth rates of all selected datasets were not significantly different among the sites, except for the data recorded at Schauinsland. However, clear differences were found in the annual amplitudes as well as the seasonal time structure. Based on a pairwise analysis of correlations between stations on the seasonal-trend decomposed components by statistical data selection, we conclude that the baseline identified by the ADVS method is a better representation of lower free tropospheric (LFT) conditions than baselines identified by the other methods.

Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing-based drought indices can detect agricultural drought in these areas. To answer these questions, we conducted a correlation analysis between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) within the growing season from 2001 to 2020 in Bavaria (Germany) and investigated the relationship with land cover and altitude. In the second step, we applied the drought indices Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) to primarily water-limited areas and evaluated them with soil moisture and agricultural yield anomalies. We found that, especially in the summer months (July and August), on agricultural land and grassland and below 800 m, NDVI and LST are negatively correlated and thus, water is the primary limiting factor for vegetation growth here. Within these areas and periods, the TCI and VHI correlate strongly with soil moisture and agricultural yield anomalies, suggesting that both indices have the potential to detect agricultural drought in Bavaria.

Low-cost phenological experiments with cut twigs are increasingly used to study bud development in response to spring warming and photoperiod. However, a broader variety of species needs to be tackled and in particular the influence of insufficient winter chilling deserves more attention. Therefore, we investigated if and how chilling requirements can be efficiently investigated by cut twigs and how this low-tech approach could be successfully implemented as a citizen science or school project. We conducted an experiment on bud burst and leaf development of Corylus avellana L. twigs, with natural chilling outdoors on a shrub (S) and another chilling treatment as cut twigs in containers (C), and subsequent forcing indoors. Subsampling of the number of cutting dates and number of twigs was used to infer minimum required sample sizes. Apart from insufficiently chilled twigs,~80% of the twigs (both S and C) reached leaf out. For multiple definitions of chilling and forcing, a negative exponential relationship was revealed between chilling and amount of forcing needed to reach certain developmental stages. At least 5 out of 15 cutting dates or alternatively half of the 10 twig repetitions, but especially those mirroring low chilling conditions, were needed to describe the chillingforcing relationship with some degree of robustness. In addition, for cutting dates with long chilling, i.e., from January onwards, freshly cut twigs (S) required significantly more forcing to reach bud burst than twigs from containers (C), although the effect was small. In general, chilling conditions of mature shrubs were well captured by cut twigs, therefore opening the possibility of chilling through refrigeration. We conclude that experimental protocols as outlined here are feasible for citizen scientists, school projects, and science education, and would have the potential to advance the research field if carried out on a large scale. We provide an easy-to-use Shiny simulation app to enable citizen scientists to build up a bud development model based on their own experimental data and then simulate future phenological development with winter and/or spring warming. This may encourage them to further study other aspects of climate change and the impacts of climate change.