INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 25: 65–80 (2005)
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1118
HOMOGENEOUS TEMPERATURE AND PRECIPITATION SERIES OF
SWITZERLAND FROM 1864 TO 2000
MICHAEL BEGERT,* THOMAS SCHLEGEL and WALTER KIRCHHOFER
Federal Office of Meteorology and Climatology (MeteoSwiss), Krähbühlstrasse 58, PO Box 514, CH-8044 Zurich, Switzerland
Received 31 March 2004
Revised 25 August 2004
Accepted 1 September 2004
ABSTRACT
A set of 12 homogenized monthly mean temperature and precipitation series of Switzerland for the period 1864–2000
are introduced. The standardized homogenization procedure, which has been developed and implemented at MeteoSwiss
during recent years, is briefly reviewed and the inhomogeneity types, causes, magnitudes, and timings are discussed.
Finally, a trend analysis is performed on each temperature and precipitation series and on a mean temperature series
of Switzerland. The results are compared with findings of other studies that have examined long-term temperature and
precipitation trends in Switzerland and neighbouring countries. The inhomogeneities of the Swiss temperature series are up
to ±1.6 ° C and the precipitation adjustment factors vary between 0.5 and 1.6. Each of the 12 temperature series analysed
contains several inhomogeneities that cause systematic biases in the adjustment curves. The slope of a mean temperature
curve derived from the original data is underestimated by 0.4 ° C/100 years. All precipitation series except one contain
inhomogeneities, but no systematic bias is observed. The trend analysis reveals an increase in the yearly temperature
series ranging from 0.9 ° C/100 years to 1.1 ° C/100 years at stations north of the alpine main crest and ∼0.6 ° C/100 years
at southern stations. Precipitation trends are observed at most sites north of the alpine main crest in winter and in some
of the yearly series. The annual slopes vary between 7 and 10% and the winter slopes between 16 and 37%. Copyright
 2005 Royal Meteorological Society.
KEY WORDS:
homogenization; adjustments; Switzerland; temperature; precipitation; trend analysis; alpine climate
1. INTRODUCTION
The reliable analysis of temperature and precipitation evolution is an important part in the current discussion
on climate change. Since the introduction of measurement stations, climatic variations and trends have
been studied by analysing these long-term instrumental time series. The Federal Office of Meteorology
and Climatology (MeteoSwiss) has maintained a climate measurement and observation network since 1864.
Temperature and precipitation series of this network are included in different global and European data sets
(e.g. Schönwiese et al., 1994; Böhm et al., 2001; Schmidli et al., 2002; Jones and Moberg, 2003; Luterbacher
et al., 2004) and single stations have been used in several studies of regional climate variability (e.g. Beniston
et al., 1994; Rebetez, 1999). It is well known that long climatological time series often contain variations
due to non-climatic factors, such as site relocations, changes in instrumentation or changes in observing
practices (e.g. Auer et al., 2001; Tuomenvirta, 2001). As these inhomogeneities can distort or even hide the
true climatic signal, the use of homogenized time series is important in studies of climate change. Up to
the present, homogenization at MeteoSwiss was done periodically within specific projects, and no extensive
set of homogenized long-term data series was available. Original non-homogenized time series were used,
or data were homogenized by the users themselves. However, a detailed knowledge of the network and the
station history information can be of fundamental importance in the process of homogenization, mainly for
* Correspondence to: Michael Begert, MeteoSwiss, Krähbühlstrasse 58, PO Box 514, CH-8044 Zurich, Switzerland;
e-mail: michael.begert@meteoschweiz.ch
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M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
the determination of the exact time of inhomogeneities and as support for the interpretation of the statistical
test results. Homogenization, therefore, is best performed as the last step in the treatment of climate data
series within the national weather services.
Here, we introduce a set of 12 homogenized monthly mean temperature and precipitation series of
Switzerland for the period 1864–2000. The standardized homogenization procedure, which has been developed
and implemented at MeteoSwiss during recent years (Begert et al., 1999), is briefly reviewed and the
inhomogeneity types, causes, magnitudes and timings are discussed. Finally, a trend analysis is performed on
each temperature and precipitation series and on a mean temperature series of Switzerland. The results are
compared with findings of other studies that have examined long-term temperature and precipitation trends
in Switzerland and neighbouring countries.
2. DATA
Several criteria were used for the selection of the 12 stations. First, the selection had to represent the different
climatic regions of Switzerland (defined by Schüepp and Gensler (1980)). Second, the stations had to be still
in use and their digitized data series had to be as long as possible. Third, both parameters had to be measured
at the same location to allow comparative studies in the future. The stations selected and the available data
periods for temperature and precipitation are listed in Table I. The station locations are illustrated in Figure 1.
Information about the data acquisition has been recorded for each station since the beginning of the measurements. The information, also called metadata, gives details about changes in the measuring conditions, such as
site relocations, new types of screens and instruments, problems with instruments, new observers, changes in
the observation times, changes in the nearby environment and others. At MeteoSwiss, a systematic, computerbased station history was introduced in 1980. Older metadata (i.e. before 1980) were collected from original
documents and entered into the database. As the knowledge of the station history is important for the homogenization process, a short description of the development of the Swiss meteorological network is given next.
The first official Swiss meteorological network had 80 stations and was put into operation on 1 December
1863. Meteorological instruments were read by an observer once (morning) or three times a day (morning, midday, evening) and meteorological observations, such as cloud cover or visibility, were carried out
simultaneously. Such manned stations made up the whole network until the late 1970s, and some of them
are still in use today. They are called conventional stations hereafter and are marked by a ‘c’ in Table I. The
Table I. Name, altitude, geographical coordinates, available data periods and station type (a: automatic; c: conventional)
of the homogenized stations
Station
Altitude (m)
Lat./Long.
316
565
985
47° 33 /07° 35
46° 56 /07° 25
46° 29 /07° 09
Chaumont
Davos
1073
1590
47° 03 /06° 59
46° 49 /09° 51
Engelberg
Geneva
Lugano
Säntis
Segl-Maria
Sion
Zurich
1035
420
273
2490
1798
482
556
46° 49 /08° 25
46° 15 /06° 08
46° 00 /08° 58
47° 15 /09° 21
46° 26 /09° 46
46° 13 /07° 20
47° 23 /08° 34
Basle
Berne
Château-d’Oex
Copyright  2005 Royal Meteorological Society
Temperature
Precipitation
Type
01/1864–12/2000
01/1864–12/2000
01/1879–12/2000
Gap: 10/1887–12/1900
01/1864–12/2000
01/1866–12/2000
Gap: 12/1871–06/1873
01/1875–12/1875
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
01/1879–12/2000
Gap: 10/1887–06/1886
01/1864–12/2000
01/1866–12/2000
Gap: 12/1871–06/1873
a
a
c
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
09/1882–12/2000
01/1864–12/2000
01/1864–12/2000
01/1864–12/2000
a
a
a
a
c
a
a
c
a
Int. J. Climatol. 25: 65–80 (2005)
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SWISS TEMPERATURE AND PRECIPITATION SERIES
Figure 1. Geographical distribution of the 12 stations used in this study. Shaded areas indicate altitudes above 1000 m a.s.l
density of the Swiss meteorological network has undergone permanent changes since its introduction. Stations were abandoned for various reasons (e.g. lack of observers, time-limited measurements) and new stations
were added to the network to fulfil new requirements, such as higher spatial or temporal resolution of the
measurements. The most important change in the network was the introduction of automatic measuring equipment in the late 1970s. About 50% of the conventional climate stations were converted into automated stations
(ANETZ). Since then, measurements of these automated stations (marked by a ‘a’ in Table I) have been
carried out and transmitted every 10 min to a MeteoSwiss database. The meteorological network of
MeteoSwiss currently comprises 72 automatic and 26 conventional stations. There are an additional 365
precipitation stations because this meteorological variable exhibits greater spatial variability. All available
stations of the entire network were used in the present study to serve as potential reference series for the
homogenization process.
Looking at the evolution of the instruments since 1864, advances in technology and the need for more
precise measurements led to the introduction of new types of instrument. In particular, the introduction of the
ANETZ stations required a new generation of instruments. For temperature, mercury thermometers have been
used in the conventional network from the beginning. At first the thermometers were fixed vertically in a
small metal wall screen, called ‘Zinkblechhuette’, in front of a north-facing window of the observer’s house.
These wall screens were gradually replaced by free-standing screens in the 20th century. Two models of freestanding screen are mentioned in the station history: the larger, metallic Wild screen and the smaller, wooden
Stevenson screen. Wild screens were rare, whereas Stevenson screens were widely used and are still in use in
the conventional network today. In the automatic network, the ventilated thermometer (VHT) was running till
1990. Since then, temperature has been measured with a ventilated thermo-hygrometer (THYGAN). Both the
THYGAN and the VHT are no longer placed in screens. For the measurement of precipitation, ombrometers
(Swiss model) with an orifice of 500 cm2 were used in the conventional network at first. At the beginning
of the 20th century these ombrometers were gradually replaced by Hellmann pluviometers with an orifice
of 200 cm2 , which are still in use in the conventional network today. In the automatic network (ANETZ),
precipitation is measured by a modified Hellmann pluviometer. A precipitation seesaw is used to measure the
rain or snow amount. Solid precipitation is melted by built-in heating.
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 65–80 (2005)
68
M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
3. HOMOGENIZATION
In the past, homogenization at MeteoSwiss was done periodically in the course of projects or single studies.
The requirement to be able to homogenize data series more frequently on a regular basis as a last step in the
treatment of climate data led to the development of an application called THOMAS (tool for homogenization
of monthly data series). The application provides a flexible working method for the homogenization of
monthly climate data series for different parameters. A detailed description of the tool and its use to produce
a homogeneous data set of nine different parameters in the climate normal period 1961–90 is given in Begert
et al. (2003).
3.1. Method
The homogenization procedure can be divided into two main steps: the detection of inhomogeneities and
the calculation of the adjustments. A review of different statistical methods is presented in Peterson et al.
(1998) and WMO guidelines on homogenization are provided in Aguilar et al. (2003). The homogenization
procedure implemented in THOMAS follows the two steps mentioned above. The procedure is a synthesis
of conclusions drawn from earlier studies at MeteoSwiss (Aschwanden et al., 1996; Bosshard, 1996;
Baudenbacher, 1997) and other published methods. The procedure allows searching and adjusting of the
two most frequent types of inhomogeneity found in data series, i.e. shifts in mean and linear trends. In
accordance with Schönwiese et al. (1994), we agree that a combined use of different statistical methods is
important, because the results must be considered as estimations or hypothesis respectively. Therefore, at least
two statistical methods are used in the detection and the adjustment procedure of THOMAS. The simultaneous
use of parametric and non-parametric methods is preferred.
The detection of inhomogeneities with THOMAS is a combination of metadata analysis and the use of
12 different homogeneity tests. Table II provides an overview of the test methods currently implemented.
According to Easterling and Peterson (1995), the detection of inhomogeneities using THOMAS is an iterative
procedure to search multiple shifts and trends in a time series. The results of the statistical tests and of the
station history analysis serve to identify the date of an inhomogeneity and to divide the data series into
segments. Each segment is then investigated separately and the iteration is repeated until each segment is
determined as homogeneous at the 95% confidence level and it can be judged as homogeneous according to
the station history. As the decision on whether an inhomogeneity is statistically significant or not is taken
during the adjustment procedure, there are no rules for weighting the results of the different tests during the
detection phase. Any indication of a discontinuity is marked and investigated further in the adjustment phase.
With the exception of Lanzante’s absolute homogeneity test, all available test methods in THOMAS are
based on the concept of relative homogeneity (Conrad and Pollak, 1950). Reference series are used to isolate
the effects of station discontinuities from regional climate change. Therefore, the creation of the reference
series is the most important step in the homogenization procedure presented, and the selection of suitable
nearby stations as input for a reference series has to be undertaken carefully. First, the potential reference
stations must show the same climatological characteristics as the station being examined (hereafter called
the candidate series). In THOMAS, reference stations are chosen according to the technique of Peterson and
Easterling (1994), which is based on a correlation analysis. To prevent distorted correlations due to shifts,
the change in data per unit time (instead of the raw data) is used in the correlation analysis. Second, the time
series of potential reference stations must not contain large inhomogeneities themselves. This condition is
checked by comparative cumulative sums of differences between each series (Rhoades and Salinger, 1993)
and with Lanzante’s absolute homogeneity test. In our study, the first method turned out to be more efficient,
as the absolute homogeneity test showed a clear limitation in its capacity to separate discontinuities from
true climate signals. Metadata are used to find inhomogeneities that occurred at several stations in the same
climatological region at about the same date. Simultaneous shifts or trends in the candidate and the reference
series would lead to distorted test results.
From the stations selected, the reference series is calculated as a weighted mean, using the squared
correlation coefficients between the candidate series and each reference station as weights. The testing and the
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 65–80 (2005)
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SWISS TEMPERATURE AND PRECIPITATION SERIES
Table II. Homogeneity tests included in THOMAS, with kinds of inhomogeneity being detected (mean: change in mean;
var: change in variance; trend: trend), corresponding method type (o: objective; s: subjective) and reference to the original
publication
Test/author
Test for
Mean
Craddock
Potter
Group of standard normal
homogeneity test (SNHT)
SNHT1
SNHT2
SNHT3
Trend-SNHT
Cumulative residuals
Cumulative differences
Easterling and Peterson
Jarušková
Lanzante
Normalized q-series
Var
×
×
×
×
×
×
×
×
×
×
Method
Trend
×
×
Reference
×
×
×
×
×
s
o
Craddock (1979)
Potter (1981)
o
o
o
o
o
s
o
o
o
s
Alexandersson (1986)
Alexandersson and Moberg (1997)
Alexandersson and Moberg (1997)
Alexandersson and Moberg (1997)
Lamarque (1993)
Rhoades and Salinger (1993)
Easterling and Peterson (1995)
Jarušková (1996)
Lanzante (1996)
Aschwanden et al. (1996)
calculation of the reference series are carried out on deseasonalized monthly datasets. Using the concept of
relative homogeneity, it is possible to detect and adjust undocumented discontinuities. However, there remains
a certain risk of misinterpreting a test result due to an unsuitable reference series. Therefore, an undocumented
shift or trend should always be confirmed by calculating the tests with two independent reference series. The
individual stations of each reference series should be selected in such a way that meteorological factors can
be excluded as a cause of any inhomogeneity detected.
During the adjustment procedure of THOMAS, each potential inhomogeneity detected is judged again, using
different parametric and non-parametric statistical methods. Shift adjustments are calculated by comparing
the homogeneous segments before and after the shift with the corresponding segments of a reference series.
The length of a segment is delimited by the previous and the following inhomogeneity. The significance of
the adjustments is tested using the Student’s t-test for temperature and the robust Wilcoxon rank sum test
for precipitation. Adjustments are generally calculated for each month separately, as there is often a seasonal
variation in their magnitudes. If the monthly values do not differ significantly from their mean, only the mean
adjustment is stored. As the number of values available in each segment has an influence on the significance
of an adjustment, the lengths of the homogeneous segments also determine whether it is possible to calculate
monthly adjustments or not.
Adjustments for trend inhomogeneities are calculated by applying a least-squares fit to the corresponding
part of the difference or ratio series (hereafter called q-series) derived from the candidate series and the
reference series. The homogenization amounts or factors are estimated directly from the slope of the trend,
as detrending the q-series is equal to adapting the trend of the candidate series to that of the reference series.
Following an idea by Hennessy et al. (1999), the Kendall tau test is used as a non-parametric method to test
the significance of trends. In addition to the statistical calculation of adjustments, THOMAS also offers the
possibility to include metadata information, such as correction factors derived from comparison measurements
or calibration. This possibility is important for time periods where no satisfactory reference series can be built
due to an insufficient number of suitable nearby stations or large discontinuities in their time series.
3.2. Statistical analysis of the adjustments
Parameter-specific characteristics of the inhomogeneities detected were evaluated, namely their magnitude,
their frequency distribution, their temporal occurrence and a possible systematic bias. Generally, it has to be
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 65–80 (2005)
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M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
emphasized that inhomogeneities were often a combination of several causes. For example, the new automatic
network (ANETZ) introduced new types of instrument, but many stations were also relocated for the new
network. It was not possible to separate and quantify the effects of the different causes with THOMAS.
Therefore, the adjustments calculated describe the differences between the measuring conditions before and
after an inhomogeneity, including the contributions of all different causes.
For temperature, each of the 12 data series contained several inhomogeneities. In total, 1612 years were
tested. Thereby, one trend and 75 shifts were detected. The mean length of the homogeneous subintervals
turned out to be 19.9 years (Table III). The most frequent reason for a shift inhomogeneity was site relocation,
with about 50% of all shifts. Other reasons for shifts were the introduction of the ANETZ, new types of
instrument, changes in screens, changes in instruments, new observers, and changes in the time of observation
and calibrations. Six inhomogeneities could not be explained by station histories. The analysis of the
adjustments revealed magnitudes that are comparable to the climatic signal calculated from the homogeneous
temperature series. As shown in the left graph of Figure 2, the monthly adjustment amounts vary between
−1.6 and +1.6 ° C. The largest adjustments were caused by site relocations in alpine regions, particularly
if site relocations were accompanied by changes in the micro-climatic conditions. As the evaluation of the
adjustments was based on the statistical significance, the size of a detectable inhomogeneity was dependent
on the correlation between the candidate series and the available reference stations, or rather on the density
of the measuring network, the spatial variability of the parameter and the topography. In general, the size of
a detectable inhomogeneity in this study was therefore smaller at stations of the Swiss plateau than at alpine
stations and smaller in recent decades than in the 19th century. High correlations enabled the detection and
correction of relatively small breaks in temperature series of around 0.3 ° C.
The temporal occurrence of the shifts per station and decade is illustrated in the right graph of Figure 2. An
increased number of shifts was found before 1900 and in the last decades of the period examined. The main
cause for the relatively high number of shifts in the 19th century was site relocation, often in combination
with a change of the screen type. Additionally, the contraction of the mercury receptacle, which was often
noticed in the station history, was responsible for inhomogeneities during this period. Between 1900 and
1970 the number of inhomogeneities became remarkably smaller, although the Zinkblechhuetten and Wild
screens were then gradually replaced by Stevenson screens. New types of instrument led to more stable
Table III. Quantitative analysis of the shift inhomogeneities detected
Parameter
Temperature
Precipitation
Period analysed
(years)
Number of detected shifts
Explained by metadata
Not explained
75
32
6
2
1612
1598
Homogenous
sub-period (years)
19.9
47.0
1.0
20
0.8
[number]
[%]
15
10
5
0.6
0.4
0.2
0.0
0
−1.5 −1.0 −0.5 −0.0 −0.5 −1.0 −1.5
61-70 71-80 81-90 91-00 01-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-00
Figure 2. Left: frequency distribution of the monthly adjustment amounts for temperature in classes of 0.2 ° C. Right: number of shifts
per decade between 1864 and 2000. The value of the first decade 1861–70 was extrapolated from the period 1864–70
Copyright  2005 Royal Meteorological Society
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SWISS TEMPERATURE AND PRECIPITATION SERIES
measurements in the early decades of the 20th century, and site relocations occurred less frequently. The
high number of inhomogeneities in the 1970s and 1980s was caused by changes of the observation times
on 1 January 1971, the introduction of the ANETZ and the frequent instrument changes during the first
years of the ANETZ. Obviously, the requirement of automatic measurements with a high temporal resolution,
which had to be accompanied by changes in the measuring equipment, was in contradiction to the demand
of homogeneous data series. Within the meteorological network of MeteoSwiss almost every temperature
series was affected by the introduction of the ANETZ measurement equipment. The simultaneous systematic
change in the whole network of Switzerland on 1 January 1971 (shifted evening observation times) could
not be treated with THOMAS. However, corrections for the calculation of the daily mean temperature were
established at MeteoSwiss earlier and found to be sufficient to guarantee the homogeneity of the monthly
temperature series (de Montmollin, 1993).
On analysing the mean adjustment series of the 12 stations, it was found that the adjustments are
systematically biased. Figure 3 shows the mean adjustment curves (homogenized minus original, averaged
over all single series) for the entire year as well as for the summer and winter half-years. Systematically,
higher temperatures (i.e. negative adjustments) were measured until the introduction of ANETZ in the late
1970s. The mean adjustments were higher in the summer half-year than in the winter half-year. Except for
the substitution of the THYGAN for the VHT at the beginning of 1991, most changes in the measurement
conditions obviously led to lower temperatures within the period 1864–2000 examined, because the new
measurement methods were less sensitive to the influence of radiation. Müller (1984) analysed comparison
measurements from Wild screens and Stevenson screens, as well as from different types of screen and the
VHT. It was found that the Stevenson screen is influenced less by radiation effects than the Wild screen
and that the VHT is less sensitive to radiation than screens in general. During the homogenization process,
we also recognized that the replacements of the Zinkblechhuetten by Stevenson screens led to lower mean
temperatures. These findings are in accordance with Böhm et al. (2001), who report systematic shifts due to
the transition from wall screens to the Stevenson screens in Austrian time series. In addition, comparisons of
free-standing and wall screens in Nordic time series revealed that, at least during the summer, the bulk of the
measurements show higher temperatures in the wall screens than in the free-standing screens (Nordli et al.,
1997).
The variation of the mean adjustments before 1900 in Figure 3 is put down to the correction of the oftennoticed ageing problem of the thermometers used in the 19th century (contraction of the mercury receptacle)
and to large inhomogeneities due to site relocations in the series of Säntis and Davos. The continuous increase
of the curve between 1900 and 1970 was caused by the reasons mentioned above and was not a result of
gradual urbanization, which is often discussed in present studies. No significant trends in urban candidate
series were seen in the tests, using reference series consisting of rural reference stations only.
−0.0
[˚C]
−0.2
−0.4
−0.6
−0.8
1860
1880
1900
1920
1940
1960
1980
2000
Figure 3. Mean adjustment amounts of the 12 temperature series (homogenized minus original data, averaged over all single series),
year (thick), summer-half year (April–September; dashed) and winter half-year (October–March; thin)
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 65–80 (2005)
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M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
As an illustration of the effect of homogenization, the original and homogeneous temperature data series
are plotted in Figure 4. It is clearly visible that the variability of the annual anomalies is reduced by
homogenization. The evolution of the 12 homogeneous series is remarkably similar over the whole period.
Only Segl-Maria and Lugano, which are both situated south of the alpine main crest, show a slightly different
climatic signal in the 19th century and at the beginning of the 20th century. The slope of any trend found in
the period 1864–2000 would be underestimated if derived from a mean Swiss temperature series of original
values. The mean adjustment curve, corresponding to the difference between the homogeneous and the original
data series, shows a significant trend of 0.43 ° C/100 years at a confidence level of 95% (Figure 3).
Concerning precipitation, all except one series (Château-d’Oex) contained significant inhomogeneities.
In total, 1598 years were tested and 34 shifts were detected (Table III). Only two of the shifts could not
be explained by station histories. The mean homogeneous subinterval turned out to be 47 years, which is
longer than the homogeneous subinterval of the temperature series. As precipitation measurements were
generally more stable and often less affected by changes in the measuring conditions, the longer subintervals
of precipitation are not surprising. About 50% of all shifts in the precipitation series were caused by site
relocations. During homogenization, we realized that even site relocations over small distances can lead
to considerable shifts. Some of the site relocations were connected to the introduction of the automatic
measurement equipment. This combination was one of the main reasons for shift inhomogeneities in
precipitation series. Shifts were also a result of changes in instruments of the same type, new observers,
calibrations and inspections. The monthly adjustment factors vary between 0.5 and 1.6 (Figure 5, left). Such
large inhomogeneities, which occurred rarely, were caused by site relocations at wind-exposed locations in
the alpine area, e.g. Säntis. Most of the adjustment factors for shifts are in the interval 0.8–1.2 and are
distributed nearly symmetrically around 1.0. In regions and periods with highly correlated neighbouring
stations, relatively small shifts of around 5% could be detected and adjusted. Although precipitation shows
larger variability in space than temperature, correlation coefficients turned out to be similar due to the higher
density of the precipitation measurement network.
original values
1.0
0.5
0.0
[˚C]
−0.5
−1.0
−1.5
1860
1880
1900
1920
1940
1960
1980
2000
1920
1940
1960
1980
2000
homogenized values
1.0
0.5
0.0
[˚C]
−0.5
−1.0
−1.5
1860
1880
1900
Figure 4. Annual temperature anomalies from the 1961–90 mean, smoothed with a 10 year Gaussian low-pass filter, of the 12 original
and homogeneous series (thin lines) and their corresponding mean (thick line)
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 65–80 (2005)
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SWISS TEMPERATURE AND PRECIPITATION SERIES
1.0
20
0.8
[number]
[%]
15
10
5
0.6
0.4
0.2
0.0
0
0.6
0.8
1.0
1.2
1.4
61−70 71−80 81−90 91−00 01−10 11−20 21−30 31−40 41−50 51−60 61−70 71−80 81−90 91−00
1.6
Figure 5. Left: frequency distribution of the monthly adjustment factors for precipitation in classes of 0.1. Right: number of shifts per
decade between 1864 and 2000. The value of the first decade 1861–70 was extrapolated from the period 1864–1870
Similar to temperature, an increasing number of inhomogeneities per station and decade can be observed
in precipitation series in the 19th century and towards the end of the period examined (Figure 5, right). The
accumulation of shifts in the 1970s and 1980s is clearly the result of the introduction of ANETZ, but there is no
evident reason for the large number of shifts in the 19th century. Figure 6 shows the mean adjustment factors
(homogenized values divided by original values, averaged over all single series) for the summer and winter
half-years and for the entire year. In contrast to the analysis of the temperature adjustments, no systematic
bias can be observed. However, the curves are influenced strongly by some outstanding adjustments of single
stations. A quantitative study including all 72 ANETZ stations of Switzerland showed that the introduction
of the automatic equipment led to measuring losses of about 5% in the mean (Begert et al., 2003). Owing to
heating effects, the winter half-year is affected more than the summer half-year. The mean adjustment curve in
Figure 6 does not show this systematic bias: first, not all of the stations examined have automatic equipment
today; second, a large shift (adjustment factor of 1.37) in the series of Säntis in 1988 hides the systematic bias.
The variability of annual precipitation anomalies is reduced by the homogenization (Figure 7). Nevertheless,
the variability remains relatively high compared with the homogeneous temperature series.
Comparing the adjustments for long-term temperature and precipitation series and their statistics with
findings of other homogenization studies (e.g. Hanssen-Bauer and Førland, 1994; Moberg and Alexandersson,
1997; Nordli et al., 1997; Auer et al., 2001; Böhm et al., 2001; Tuomenvirta, 2001), similar characteristics of
the results are found. Similar lengths of the homogeneous subintervals and systematic biases in temperature
series are reported. However, the homogenization amounts and the number of inhomogeneities depend strongly
on the specifics of the meteorological network and the topography of the different countries.
1.05
1.00
0.95
1860
1880
1900
1920
1940
1960
1980
2000
Figure 6. Mean adjustment factors of the 12 precipitation series (homogenized divided by original data, averaged over all single series),
year (thick), summer-half year (April–September; dashed) and winter half–year (October–March; thin)
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 65–80 (2005)
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M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
1.6
original values
1.4
1.2
1.0
0.8
1860
1880
1900
1920
1940
1960
1980
2000
1920
1940
1960
1980
2000
1.6
homogenized values
1.4
1.2
1.0
0.8
1860
1880
1900
Figure 7. Annual precipitation anomalies from the 1961–90 mean, smoothed with a 10 year Gaussian low-pass filter, of the 12 original
and homogeneous series (thin lines) and their corresponding mean (thick line)
4. TREND ANALYSIS
The homogenized data set was used to study annual and seasonal characteristics of the temperature and
precipitation series in the period 1864–2000. Annual values correspond to the period from December to
November and are dated by the year in which January is included. Winter values refer to December–February
(DJF), spring to March–May (MAM), summer to June–August (JJA) and autumn to September–November
(SON). All values used for trend analysis are anomalies from the 1961–90 mean value. The non-parametric
Mann–Kendall test, as described in Sneyers (1990), was applied for trend analysis. The proposed graphical
representation of the progressive direct test value u and the backward series u was used to determine the
beginning of a trend. The intersection of the two curves enables the start of the phenomenon to be located
approximately. A trend is considered statistically significant at a confidence level of 95% and the slopes of
the trends are calculated by least squares linear fitting. Figures 8 and 9 give an overview of the trends found
in the data series covering the whole period 1864–2000. The analysis was not performed for Château d’Oex
(T , P ), Davos (T , P ) and Säntis (P ) because of incomplete data series in the 19th century.
Most of the yearly and seasonal temperature series show a significant positive trend. The only exception
occurs in summer, when no significant trend is found in the series of Lugano, Segl-Maria and Zurich. The
slopes of the yearly series range from 0.9 ° C/100 years to 1.1 ° C/100 years at stations on the northern side
of the Alps, whereas the two stations of Lugano and Segl-Maria on the southern side of the alpine main
crest both show smaller slopes of 0.6 ° C/100 years. The largest increase in yearly temperature series (i.e.
1.2 ° C/100 years) is observed in Sion, which is situated in the Valais (large alpine valley). Looking at trends
in different seasons, those stations with a higher elevation (Chaumont, Säntis, Segl-Maria) reveal their largest
slopes in autumn, ranging from 0.8 ° C/100 years to 1.3 ° C/100 years. The largest increase for stations at lower
elevations is found in winter, ranging from 0.9 ° C/100 years to 1.6 ° C/100 years. Again, the smallest slopes
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SWISS TEMPERATURE AND PRECIPITATION SERIES
75
Figure 8. Annual and seasonal temperature trends for complete data series covering the period 1864–2000. The linear trends are given
as ° C/100 years. Significance levels are 99% (bold) or 95% (normal). Empty entries denote values not significant at the 95% level.
Station altitudes in metres a.s.l. are given in parentheses
of all stations are observed in Lugano and Segl-Maria, independent of the season. Comparing the results with
findings of previous studies (e.g. Schönwiese et al., 1994; Brunetti et al., 2000; Auer et al., 2001), there is
generally good agreement. However, a quantitative comparison is complicated by the different time periods
and analysis procedures, e.g. station-based versus grid-point-based trends. On the European scale, our results
are comparable to those of Schönwiese et al. (1994). Larger trends in winter and autumn than in spring and
summer, as well as different magnitudes of temperature increase between the regions north and south of the
Alps, are observed in central Europe for the period 1891–1990. On an alpine scale, Auer et al. (2001) provide
a temperature trend analysis for different climatological regions in central Europe. The trend detected in the
yearly series of their region West, which includes the stations Zurich and Davos, is 1.15 ° C/100 years for the
period 1890–2000. This result is slightly higher than our findings. However, the starting point of the trend
analysis in 1890, which occurs in the coldest period between 1864 and 2000, has to be taken into account.
Finally, the findings of Brunetti et al. (2000) for northern Italy for the period 1867–1995 also correspond
quite well to our results for Lugano and Segl-Maria. In northern Italy, the largest increase is observed in
winter, followed by autumn and spring. Comparable to our results, no significant trend is noticed in summer.
Presumably because the period examined was earlier, the magnitudes of the slopes are slightly smaller in the
study of Brunetti et al. (2000) than in our study.
For precipitation, there is no indication of a significant increase or decrease in spring, summer or autumn at
the 95% confidence level. However, significant precipitation trends are observed at most sites in winter and in
some of the yearly series. The trends in Figure 9 are given as percentage changes over a 100 year period. A
significant increase in yearly values can be seen at the stations of the Swiss Plateau (Berne, Zurich, Geneva)
and in the series of Chaumont, representing the Jura mountains. The slopes of the trends range from 7 to
10% per 100 years. However, looking at a progressive analysis of the yearly precipitation series, it becomes
clear that the trends are influenced strongly by the last few years with relatively high annual precipitation
amounts. In winter, positive trends of 16 to 37% per 100 years are found for the stations north of the alpine
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M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
Figure 9. Annual and seasonal precipitation trends for complete data series covering the period 1864–2000. The linear trends are given
as percentage changes per 100 years. Significance levels are 99% (bold) or 95% (normal). Empty entries denote values not significant
at the 95% level. Station altitudes in metres a.s.l. are given in parentheses
main crest. The strongest increase is observed in the western part of Switzerland (Geneva, Chaumont). The
results of the progressive application of the Mann–Kendall test identify the beginnings of the trends in most
of the series around 1940 to 1950. Similar to temperature, the two sites situated south of the alpine main crest
clearly differ from the other stations. There is no significant increase or decrease in the yearly or seasonal
precipitation series of Lugano or Segl-Maria. The comparison of our trend results with findings of previous
studies (e.g. Auer and Böhm, 1994; Widmann and Schär, 1997; Buffoni et al., 1999; Schmidli et al., 2002)
shows a qualitatively good agreement. However, the different time periods and analysis procedures must
be considered again. For the region of the European Alps, Schmidli et al. (2002) provide grid-point-based
trend maps for the period 1901–90. In agreement with our results, a significant (at the 90% level) increase
in winter precipitation of 20–40% was observed in northern and western Switzerland, whereas no other
comparable significant trends were found over the territory of Switzerland in other seasons. For the country
of Switzerland, Widmann and Schär (1997) provide high-resolution trend maps for the period 1901–90. A
statistically significant increase was also noticed in winter. Generally, the results of the station-based winter
trend analysis agree well in magnitude and in their distribution. However, the region with maximum increase
(Valais) differs from our results (western Switzerland, including the stations of Geneva and Chaumont).
Comparing our results for Lugano and Segl-Maria with findings for the region of northern Italy, substantial
differences in the development of long-term precipitation series are recognized. The positive trend in winter
and the negative trends in spring, summer and autumn in northern Italy (Brunetti et al., 2000) for the period
1867–1995 cannot be observed in southern Switzerland.
So far, we have looked at overall trends in each single series. In order to give an overview of the main
characteristics of climate variability in Switzerland between 1864 and 2000, the mean Swiss series were
also analysed. We used the first principal component (PC) of a PC analysis (PCA) to judge whether a mean
of the 12 available series adequately represents the whole Swiss territory. The PCA was performed on the
standardized anomalies providing data for the whole period. Therefore, Château d’Oex (T , P ), Davos (T ,
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SWISS TEMPERATURE AND PRECIPITATION SERIES
P ) and Säntis (P ) were not used because of incomplete data in the 19th century. As the PCs are linear
functions of the input data that maximize the amount of their explained variance, the first PC represents a
mean series using closely correlated input data. The explained variance can be used as an assessment of
the quality of the mean value expressed by the first PC. A correlation analysis between the first PC and a
simple average of the 12 series proves the usability of the mean for further investigations. Table IV lists the
explained variance of the first PCs of the annual and seasonal temperature and precipitation series, as well
as the corresponding correlation coefficients between the first PCs and the simple means. Owing to the high
percentage of explained variance (89–92%) and the high correlation, a simple averaged temperature series
is a good representation for all areas of Switzerland. The mean value of the available 12 series can be used
to describe the main characteristics of the temperature development in Switzerland between 1864 and 2000.
As expected, the precipitation field shows more local patterns. The explained variance of the first PCs is
less than for temperature, ranging from 61 to 69% depending on the season. Therefore, we have not used a
mean series to analyse temporal precipitation changes in Switzerland. The different climatological regions,
each including several homogeneous long-term series, have to be examined separately in order to analyse the
different regional variations. However, additional precipitation series have to be homogenized for this purpose.
Figure 10 shows the standardized mean annual and seasonal temperature series, together with the
progressive direct test value u and the backward series u of the Mann–Kendall test for trend. The main characteristics of the Swiss mean annual temperature curve are the cold period at the end of the 19th century, with a
minimum around 1891, and the following more-or-less continuous increase. The increase is interrupted by
a relatively warm period in the second part of the 1940s and has intensified in the last 15 years. Whether
the evolution must be regarded as a sequence of trends, abrupt changes (especially around 1985) or even
both cannot be determined. Considering the different possible reasons for the temperature evolution, such as
continuous increase of greenhouse gases or periodic changes in the frequency of weather situations, the evolution might even be a combination of trends and abrupt changes. The mean temperature increase in Switzerland
amounts to 1.0 ° C/100 years. According to the results of the Mann–Kendall test, the overall trend becomes
significant around 1950. The interpretation of the intersection of u and u , i.e. the approximate start of the
trend, is disturbed by the abrupt increase of the annual temperature around 1985 and shows the limit of the
linear trend model. Looking at the seasonal evolutions, a different behaviour of SON and DJF compared
with MAM and JJA can be observed. The autumn and winter curves show a more continuous increase, with
larger negative fluctuations around 1890 (SON, DJF) and around 1915 (SON). Both curves show significant
trends beginning around 1940 in autumn and around 1970 in winter. In spite of the earlier beginning of the
increase, the slope obtained from a linear least-squares fitting is smaller in autumn (1.1 ° C/100 years) than
in winter (1.3 ° C/100 years). Temperature evolutions in spring and summer are subject to larger and more
abrupt fluctuations compared with autumn and winter, in particular due to the warmer period 1945–55 and
the remarkable sudden increase after 1980. The shift-like increase in the curves of MAM and JJA in the 1980s
is responsible for the significant trends found by the Mann–Kendall test in the whole period 1864–2000. The
slopes of the linear fitting are 0.8 ° C/100 years for spring and 0.7 ° C/100 years for summer. Figure 10 clearly
illustrates that there is no continuous evolution towards warmer temperature in spring and summer.
Comparing the long-term trends in the annual and seasonal mean Swiss temperature series with the updated
version of the most frequently used global dataset of the University of East Anglia (CRU/UEA), described in
Jones and Moberg (2003), small but systematic differences can be found. The temperature increase derived
Table IV. Explained variance of the first PC (var) and correlation coefficient (cor) between the first PC and the mean of
the 12 series
Year
Temperature
Precipitation
Winter
Spring
Summer
Autumn
Var (%)
Cor
Var (%)
Cor
Var (%)
Cor
Var (%)
Cor
Var (%)
Cor
91
64
0.999
0.991
89
69
0.999
0.988
92
63
0.999
0.988
92
61
0.999
0.992
89
64
0.999
0.988
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M. BEGERT, T. SCHLEGEL AND W. KIRCHHOFER
3
year
2
1
0
−1
−2
−3
6
4
2
0
2
1860
3
1880
1900
1920
1940
3
DJF
2
2
1
1
0
0
−1
−1
−2
−2
1960
1980
2000
MAM
−3
−3
6
4
2
0
−2
6
4
2
0
−2
1860
3
1880
1900
1920
1940
1960
1980
1860
2000
3
JJA
2
2
1
1
0
0
−1
−1
−2
−2
1880
1900
1920
1940
1960
1980
2000
SON
−3
−3
6
4
2
0
−2
6
4
2
0
−2
1860
1880
1900
1920
1940
1960
1980
2000
1860
1880
1900
1920
1940
1960
1980
2000
Figure 10. Standardized temperature anomalies of Switzerland from 1864 to 2000 with a 20 year Gaussian low-pass filter (thick line)
and a progressive representation of the Mann–Kendall test values u (thin line) and u (dashed line) for yearly and seasonal series. The
horizontal dotted lines in the lower part of the graphs indicate a confidence level of 95%
from the mean Swiss series in the period 1864–2000 is 0.2 ° C/100 years above the trend value calculated
from the corresponding grid box of the CRU/UEA dataset (45–50 ° N, 5–10 ° E). The difference does not
depend on the season. The CRU/UEA long-term temperature trend over the central alpine region is, therefore,
slightly underestimated.
5. CONCLUSIONS
The homogenization procedure used at MeteoSwiss, in combination with the analysis of the station history,
has proved to be a powerful tool for detecting and adjusting inhomogeneities in long-term temperature and
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SWISS TEMPERATURE AND PRECIPITATION SERIES
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precipitation series of Switzerland. As high-quality homogenization and in-depth analysis is a labour-intensive
process, the primary goal was to retrieve a small but well-homogenized set of Swiss data series, which has
to be enlarged in future. The dataset contains the best available monthly temperature and precipitation data
of Switzerland and is available to the research community over the Internet (www.meteoschweiz.ch) or by
contacting MeteoSwiss.
All except one of the 24 series analysed turned out to be inhomogeneous, containing one or several shifts
or trends in the period 1864–2000. Most of the inhomogeneities detected in the temperature and precipitation
series were caused by site relocation. In temperature series, the mean homogeneous subinterval is 19.9 years
and the calculated adjustments range from −1.6 to +1.6 ° C. The original series are systematically biased
by the inhomogeneities detected. Looking at a mean temperature curve derived from the original data, the
long-term amplitude of the evolution is underestimated by 0.4 ° C/100 years, which is almost half as much
as the finally observed increase in a mean homogeneous temperature series of Switzerland. The systematic
bias underlines the necessity of homogenization. The use of original temperature series leads to incorrect
conclusions even if an average series of several single stations is used. Precipitation series contain longer
homogeneous subintervals of 47.0 years on average. The adjustment factors range from 0.5 to 1.6. Although
the introduction of automatic measuring equipment led to systematically lower measurements (i.e. 5% in
mean) in Switzerland, no systematic bias can be observed in the mean adjustment curve of the 12 series. This
is due to a large inhomogeneity in the series of Säntis and because not all stations examined have automatic
equipment today. However, the individual adjustment series reveal negative trends, e.g. Chaumont (−15% per
100 years), and positive trends, e.g. Zurich (+8% per 100 years). Precipitation studies based on few stations
or on ANETZ stations only must be performed with homogenized data.
Most of the yearly and seasonal temperature series show a significant positive trend in the period
1864–2000. Stations at higher elevations show their largest increase in autumn, whereas the largest increase at
stations at lower elevations can be observed in winter. The increase is generally smaller on the southern than
on the northern side of the Alps. Looking at a progressive analysis of the mean temperature of Switzerland,
the autumn and winter curves show a more continuous increase than the curves of the other two seasons.
Temperature evolutions in spring and summer are subject to larger and more abrupt fluctuations, with a sudden
increase after 1980. Significant positive trends in precipitation series can only be observed north of the alpine
main crest in winter and in some of the yearly series. Stations situated in the western part of Switzerland
show the strongest increase.
ACKNOWLEDGEMENTS
The development of the homogenization tool THOMAS, as well as the presentation of the homogenization of
the temperature and precipitation series itself, was part of the project NORM90 at MeteoSwiss. We wish to
thank the following individuals for the substantial contribution to the successful undertaking of the project:
Geneviève Baudraz, Rudolf Dössegger, Marianne Giroud, Rainer Kegel, Vera K öhli, Marc Musa, and Gabriela
Seiz. Christof Appenzeller, Stephan Bader and Simon Scherrer from MeteoSwiss are gratefully acknowledged
for valuable suggestions for improvements of this paper.
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