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The BernClim plant phenological data set from the canton of Bern (Switzerland) 1970–2018

Abstract. In 1970, the Institute of Geography of the University of
Bern initiated the phenological observation network BernClim. Seasonality
information from plants, fog and snow was originally available for applications in
urban and regional planning and agricultural and touristic suitability and is
now a valuable data set for climate change impact studies. Covering the
growing season, volunteer observers record the dates of key development
stages of hazel (Corylus avellana), dandelion (Taraxacum officinale), apple
tree (Pyrus malus) and beech (Fagus sylvatica). All observations consist of
detailed site information, including location, altitude, exposition (aspect)
and inclination, that makes BernClim unique in its richness in detail on decadal
timescales. Quality control (QC) by experts and statistical analyses of the
data have been performed to flag impossible dates, dates outside the
biologically plausible range, repeated dates in the same year, stretches of
consecutive identical dates and statistically inconsistent dates (outliers
in time or in space). Here, we report BernClim data of 7414 plant
phenological observations from 1970 to 2018 from 1304 sites at 110 stations, the QC procedure and selected applications (Rutishauser et al., 2019: https://doi.org/10.1594/PANGAEA.900102). The QC points to very good
internal consistency (only 0.2 % were flagged as internally inconsistent) and likely high quality of the data. BernClim data indicate a trend towards an extended growing season. They also track the regime shift in the late 1980s well to pronounced earlier dates like numerous other phenological records across the Northern Hemisphere.


indicate growth cycle stages of annual and perennial plant life. The stages are closely linked to environmental drivers such as light, temperature and precipitation and are, thus, used as climate change impact indicators (Menzel et al 2006, IPCC 2013 or serve as proxy data in climate reconstructions (Rutishauser et al. 2008;Anderson et al. 2013, Ge et al., 2014. For instance, the inventory of climate monitoring series of the Swiss GCOS Office (MeteoSwiss 2018) lists phenological records. Peñuelas 35 et al (2008) stressed the importance of phenological observations for climate feedback processes that can only be studied when ground observational data are available in a reasonable quality (Rutishauser et al. 2009).
In Switzerland, the longest continuous phenologial series reach back to 1808 (Horse chestnut bud burst, Geneva) and 1894 (Cherry flowering, Liestal;Defila andClot 2001, Defila et al. 2016). In Europe, single observations are documented from the High Middle Ages onwards (Pfister and White 2018). Systematic collections started in the 18th century, e.g., several decades 40

Observation Network and Data
The BernClim observation network focuses on the territory of the Canton of Bern (Switzerland, Figure 1). The Canton of Bern stretches across three major Swiss landscapes from the Jura mountains across Swiss Plateau to the Alps and spans an altitudinal range from 400 to 4000 m a.s.l.. Climate in the study region is determined by westerly, northwesterly and southwesterly winds (i.e., from the Atlantic Ocean) and the passage of weather systems. In summer, the Azores high is the dominant pressure 70 system, alternating with westerly and northerly flow situations. Regional wind systems such as Föhn and the Bise may play an important role. Most areas receive an adequate amount of precipitation throughout the year. the success of the first phase, the project was continued with funding from diverse sources. Many observers continued and the network has been operated ever since by the Institute of Geography of the University of Bern, during the last 3 decades as a non-funded activity. Apart from serving spatial planning, the BernClim data have been used in education. While the number of observers has steadily decreased, five have remained to the present day. These long term series are today a valuable source of information also for science, particularly as there were only few observer changes throughout the network. 80 To cover all four seasons, observation periods were divided into growing and resting periods. Plant phenology from early spring to late autumn documents summers. During winter, fog presence and duration and snow cover were observed daily from late autumn to early spring ( Table 1) (40% of all values tested). For this test the standardised dates were restandardised across all stations for each 130 year.
The quality control found no impossible dates and no consecutive identical dates. Five dates (0.07%) were outside 3 sd per series and ten dates (0.13%) were outside 3 sd of all series in a given year. These are very low rates, which points to a good internal consistency and likely a good quality of the data.
There are many "non-firsts" (2.47%), for which the original documents sometimes provide explanations. Since these are 135 deviations from the observation instructions, we flagged them nevertheless. Interestingly, we found a relatively high rate of dates outside the range given by MeteoSwiss, namely 3.56%. Of these, most (60%) concerned leaf colouring of the beech and 22.7% concerned the flowering of the Apple tree. The range given by MeteoSwiss refers to a 3 sd range per phase and altitude region. This means that for a normal distribution, 0.3% outliers are expected, however, we find ten times this amount. At the same time, only six are picked up by the other tests, which indicates that most of these outliers are consistent with the other 140 observations both in space and time.
The QC methods test for outliers and exceptionally wide distributions, but not for the opposite, too narrow distributions. Here, it is noteworthy that the data set has only one hazel flowering event before the start of the year, whereas we might expect this to occur more frequently.
Long series (>= 20 years) were checked for temporal inhomogeneities (caused, e.g., by a change of observed plant) following 145 the method described in Auchmann et al. (2018). In short, 3 different statistical tests are applied to each phenological series and the agreement between the tests determines the significance of an inhomogeneity (significant when at least 2 tests agree on an inhomogeneity). At least 3 correlated reference series are required to run the tests: this requirement limits the number of tested series to 51 (out of 56 long series). Only one series was found significantly inhomogeneous (Fig. 6).
In summary, BernClim data are expert data and subject to uncertainties. These depend on the observability of the phenomena 150 and the speed of the development. Spring phases are typically relatively clearly defined (±1 days), whereas the autumn phases have larger uncertainties (typically ±3.5 days, see Brügger 1998). Several series cover more than 40 years. As an example for a long time series, Figure 8 shows the start of blossom of the Apple 160 tree from 9 sites at Wyssachen. The series clearly show a trend towards earlier flowering dates over the observation period. The series also shows a shift in the late 1980s. This shift is well documented in many other series (see also Reid et al. 2013).

Analyses
It is also found in European or even Northern Hemispheric spring snow cover (Brönnimann 2015). A change in late winter temperature around the late 1980s, albeit smaller than in observations, is also found in forced atmospheric model simulations, implying that part of this change was due to an overlap of forcing factors such as greenhouse gases, sea-surface temperatures 165 (El Niño 1986/7, La Niña 1988/9), volcanic eruptions, and other effects (Brönnimann et al. 2006. The BernClim Data can thus help to better analyse this step-wise climatic and ecological change.

Data availability
The data presented and described in this paper are available in the data repository PANGAEA:

Conclusions
A plant phenological data set spanning almost 50 years is published as a data set on PANGEAE. Subsequently the data will be added to the PEP725 data and also available from the Geoportal of the Canton of Berne. The series were quality controlled.
It should be noted that BernClim-Data are expert data and subject to uncertainties. Quality control procedures were performed to flag uncertain observations. 175 Although the number of stations decreased rapidly after the initial phase of the network, five long-term series remained which allow a 50-year view with almost no observer changes. Despite the sparseness, they still cover spatial variability of climatically relevant plant development stages of four species in three typical climate zones of Switzerland. Inhomogeneity tests suggest that stepwise changes are rarely driven by observational artifacts such as changes in observers, definitions or station changes, revealing a strong consistency within long time series that underline the quality of the data. 180 In the future, the data series could be continued with citizen science data such as PhaenoNet and OpenNature (Lehmann et al. 2018). This paper only describes the phenological data. The rich (daily) winter data remain to be explored further. BernClim data may help to constrain further relevant indices such as leaf area or NDVI on a small scale. The data have the potential to locally extend satellite data back to 1970, and they have the potential to study biological processes on the local level with continuous evidence over five decades.