Trends in indices for extremes in daily temperature and precipitation

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Int. J. Climatol. 25: 1149–1171 (2005)
Published online 20 June 2005 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1163
TRENDS IN INDICES FOR EXTREMES IN DAILY TEMPERATURE AND
PRECIPITATION IN CENTRAL AND WESTERN EUROPE, 1901–99
ANDERS MOBERGa,b, * and PHILIP D. JONESa
Climatic Research Unit, University of East Anglia, Norwich, UK
Department of Meteorology, Stockholm University, Stockholm, Sweden
a
b
Received 29 March 2004
Revised 10 January 2005
Accepted 10 January 2005
ABSTRACT
We analyse 20th century trends in six indices for precipitation extremes and four indices for temperature extremes,
calculated from daily observational data for European stations. The indices chosen reflect rather moderate extremes. Most
of the ∼80 stations used are situated in central and western Europe; therefore, results mainly refer to this region. Trends
are calculated over 1901–99, 1921–99, 1901–50 and 1946–99. Two different trend estimators are used, and significance
is assessed with a bootstrap technique. We find that:
• Significant increasing precipitation trends over the 20th century dominate in winter for both average precipitation
intensity and moderately strong events. Simultaneously, the length of dry spells generally increased insignificantly.
• There are few significant trends of any sign for precipitation indices in summer, but there are insignificant drying
trends over Scandinavia and wetting trends over central and western Europe for 1921–99. The length of dry spells in
summer generally increased insignificantly.
• Both the warm and cold tails of the temperature distribution in winter warmed over the entire 20th century. Notably
low values in the cold tail for daily Tmax and Tmin occurred in the early 1940s, leading to strong but insignificant
negative trends for 1901–50, whereas little change occurred before 1940.
• Warming of winters during 1946–99 occurred in both the warm and cold tails for both Tmax and Tmin, with the
largest warming in the cold tail for Tmin.
• The warm tail of daily Tmin (and to a smaller extent Tmax) in summer warmed significantly during the past century.
There is more evidence for summer warming in the first half of the century compared with the second half.
• During 1946–99, the warm tail of daily Tmax in summer was generally warming while the cold tail was cooling (both
insignificantly).
• More digitized daily observational data from various European sub-regions are needed to permit a spatially more
extensive analysis of changes in climate extremes over the last century.
Copyright  2005 Royal Meteorological Society.
KEY WORDS:
daily meteorological observations; climate extremes; temperature; precipitation; Europe; trend estimation; significance
testing
1. INTRODUCTION
Extreme climatic events, such as unusual heat waves, floods and droughts, can have strong impact on society
and ecosystems and are thus important to study. In the second assessment report of the Intergovernmental Panel
on Climate Change (IPCC), Nicholls et al. (1996) stated that data on climate extremes and variability were
inadequate to say anything about global-scale changes. Only in some regions were enough data available to
show that either increases or decreases had occurred. This lack of data, together with the need for information
for policymakers and their scientific advisors, was the impetus for a workshop held in June 1997 in Asheville,
* Correspondence to: Anders Moberg, Department of Meteorology, Stockholm University, SE-106 91 Stockholm, Sweden;
e-mail: [email protected]
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USA, focusing on indices and indicators of climate extremes (Karl et al., 1999). A number of indices were
suggested, each aimed at depicting a certain aspect of climatic extremes.
One outcome of a second workshop in Asheville, in March 1999, was the development of a software package
for calculating various indices for extremes from daily meteorological observations (see Peterson et al. (2001)).
The current version of this software (ClimDex) is available at http://ccrp.tor.ec.gc.ca/etccd/software.html.
Another direction of recent development is the compilation of databases of daily climate data and extremes
time series derived from the daily data. An example is the European Climate Assessment dataset (Klein Tank
et al., 2002a,b) with long daily temperature and precipitation series from about 200 European stations, of which
most are available at http://eca.knmi.nl/. This Website also contains time series for extremes indices derived
from daily temperature or precipitation station records and a dictionary of such indices. Further development
on the definition of extremes indices has been undertaken in the STARDEX project (e.g. Haylock and Goodess,
2004) and their software is available at http://www.cru.uea.ac.uk/cru/projects/stardex/. An intercomparison of
the different extremes indices that are currently in use can be found at the ClimDex Website.
A number of studies of changes in climatic extremes, both using observations and the output from climate
models, have appeared recently, and some were mentioned in the IPCC’s Third Assessment Report (Folland
et al., 2001). In a study based on daily station data for the second half of the 20th century, for as much
of the world as possible, Frich et al. (2002) found coherent patterns of statistically significant changes in
some indices for temperature extremes, e.g. an increase in warm summer nights and a decrease in the annual
number of frost days. Precipitation indices showed more mixed patterns of change, but significant increases
were seen in the totals derived from wet spells in some areas. An overall finding by Frich et al. (2002) was
that a significant proportion of the global land area with data was increasingly affected by a significant change
in climatic extremes during the second half of the 20th century. In another global-scale study, Horton et al.
(2001) found an increase in warm extremes and a decrease in cold extremes in ocean surface temperatures
since the late 19th century, by analysing gridded annual and seasonal mean data.
In a European regional analysis, using daily data for the 1946–99 period, Klein Tank and Können (2003)
found that Europe-wide indices of wet extremes increased, although the spatial coherence of trends was low.
They also found that, for the 1976–99 sub-period, the annual number of warm extremes had increased more
than twice the number expected from the corresponding decrease in the number of cold extremes, hence
implying a change in the shape of the temperature distribution and not a simple shift of the mean. Other
European studies have linked variability in the occurrence of various indices for precipitation or temperature
extremes to atmospheric circulation variability (Kyselý, 2002; Domonkos et al., 2003; Haylock and Goodess,
2004).
Regional studies of climatic extremes have also been untertaken for other parts of the world. Using
homogenized daily data for southern Canada for 1900–98, Bonsal et al. (2001) found evidence for significantly
fewer days with extremely low temperatures in winter, spring and summer and more days with extremely
high temperatures in winter and spring, although little evidence was found for changes in the number of
extremely hot summer days. For the USA, DeGaetano (1996) found a significant reduction in the number
of cold minimum temperature threshold exceedances and significant increases in the exceedance of warm
minimum temperature thresholds over the 1951–93 period, and Easterling (2002) found a decrease in the
number of frost days and a lengthening of the frost-free season during 1948–99. Changes in temperature and
precipitation extremes in the 20th century have also been observed in southeastern Asia and the southern
Pacific region (Plummer et al., 1999; Manton et al., 2001), in China (Zhai et al., 1999; Zhai and Pan, 2003),
in the Caribbean region (Peterson et al., 2002) and in several countries in Africa (Easterling et al., 2003).
Analyses of changes in climate extremes in transient experiments with coupled atmosphere–ocean general
circulation models have also been undertaken, both with historical forcings for the 20th century and with
scenario forcings for the 21st century (e.g. Kharin and Zwiers, 2000; Semenov and Bengtsson, 2002; Hegerl
et al., 2004). On a global scale, these experiments indicate larger changes in extreme precipitation compared
with changes in mean precipitation for a warming world. They also show that changes in the warm and cold
tails of the temperature distribution can be notably different from changes in the mean. Both these general
findings imply that results from analyses of changes in extremes are important in the context of anthropogenic
climate change.
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A step towards improved possibilities to compare observed extremes with output from global climate model
runs was taken by Kiktev et al. (2003), who produced gridded trends over the 1950–95 period for a number
of selected extremes indices derived from observations. They found significant decreases in the number of
frost days and increases in the number of very warm nights over much of the Northern Hemisphere, whereas
regions of significant increases in rainfall extremes and decreases in the number of consecutive dry days
were smaller in extent. Comparison with trend estimates from climate models indicated that the inclusion of
anthropogenic effects in the model integrations significantly improved the simulation of trends in temperature
extremes, suggesting that human-induced forcing has already played a role in the change of temperature
extremes.
Although all of the studies cited above have studied changes in climatic extremes, it should be noted that
few of them dealt with events that are proper extremes in the sense of being highly unusual. Rather, the indices
studied are often based on the 10th/90th or 5th/95th percentiles of daily data, which represent moderately
unusual events. A reason not to study too ‘extreme’ extremes is that trends become difficult to detect, as they
will be based on few data compared with more moderate extremes (e.g. Frei and Schär, 2001; Klein Tank
and Können, 2003). Therefore, to avoid the difficulties of detecting trends in highly unusual events, our study
deals specifically with rather moderate extremes.
In this paper, we use daily temperature and precipitation data from European stations for the period 1901–99
to study the spatial pattern of changes in various indices for precipitation and temperature extremes in Europe.
This work places the European analysis for 1946–99 by Klein Tank and Können (2003) in a longer time
perspective, although we use a different selection of indices. Furthermore, we study winter and summer data
separately, whereas Klein Tank and Können (2003) studied indices derived using data for the entire year. We
analyse maps with plots of station trends in extremes indices and present selected time series for such indices
for a central European sub-region to exemplify details in the temporal evolution. We compare results from
two different methods for trend estimation, where significance is assessed using a bootstrap technique.
2. DATA
The data were obtained from the European Climate Assessment (ECA) dataset (Klein Tank et al., 2002a,b;
http://eca.knmi.nl/), augmented with one additional station in Italy (Milan; Maugeri et al., 2002). Homogeneity
tests of the ECA series (Wijngaard et al., 2003) led to the assessment that a large majority (94%) of
the temperature series and 25% of the precipitation series were considered as ‘doubtful’ or ‘suspect’ (as
opposed to ‘useful’) when the entire 1901–99 period was considered. In the sub-period 1946–99, 61% of the
temperature series and 13% of the precipitation series were assigned to the ‘doubtful’ or ‘suspect’ classes.
Wijngaard et al. (2003) found that most of the inhomogeneities in the temperature data could be attributed
to documented observational changes. The seemingly favourable results for precipitation data can, at least
partly, be attributed to the greater local variability compared with temperature, and hence a lower probability
of detecting inhomogeneities.
The classification of a station record as being ‘doubtful’ or ‘suspect’ does not necessarily mean that it is
useless for studies of climate variability. The tests applied by Wijngaard et al. (2003) may be very sensitive
to changes in the internal time series properties, and the climatic information in the records may be relatively
robust even if a negative test result has been obtained. It is very clear, though, that the findings of Wijngaard
et al. (2003) strongly suggest that care has to be taken in interpreting results from analyses of the ECA
series before these have been properly homogenized. In our study, we focus on large-scale patterns and are
suspicious about ‘strange’ values or outliers of any kind, e.g. strong positive trends for a station in a region
where most other stations show negative trends. Given the large potential problem of inhomogeneous data,
the numeric values of trends are not given much attention here. We place more emphasis on the sign and the
relative size of trends, e.g. in different regions, different time periods and for different indices. This approach
should justify the use of a dataset that is known to contain several inhomogeneous series. For more accurate
quantitative assessment of changes, an extensive homogeneity testing exercise would first be required. This
is beyond the scope of the present investigation, and there is actually a lack of techniques for properly testing
and even fewer for adjusting daily observational records.
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2.1. Station selection
When dealing with analyses of daily data where the focus can, for example, be on finding the maximum
accumulated precipitation in a sequence of consecutive wet days, or estimating a percentile from daily data in
a selected season, it is essential that the daily data are nearly (sometimes even entirely) complete in the season
analysed. Furthermore, when analysing century-long trends it is essential that years with many missing data,
if they occur, are relatively few and also not clustered together during a certain period of the record, as this
period might have had an anomalous climate. For these reasons, we applied rather strict rules for selecting
stations that were considered as having complete enough data for this study:
1. A month is considered as having complete data if there are 2 missing days.
2. A year is considered as complete if all months are complete according to (1).
3. A station series is considered as complete if all three 20 year blocks 1921–40, 1941–60, 1961–80 and
the 19 year block 1981–99 have 3 missing years according to (2).
When applying these rules to the whole data set, we found 81 stations that were considered complete
enough for analyses for the 1921–99 period. Of these 81 stations, there are 61 with complete enough daily
precipitation (53 Tmin, 51 Tmax) data. A smaller number of stations were also considered complete enough
(42 precipitation, 33 Tmin, 31 Tmax) in the 20 year block 1901–20 (3 missing years, according to (2)) to be
used for analyses over the entire period 1901–99. The maps in Figure 1 show their geographical distributions.
The stations that are complete over 1901–99 are mainly located in central and western Europe, whereas the
spatial coverage in other parts of Europe is only slightly better when the 1921–99 period is considered.
2.2. Quality control checks
Once the 81 stations had been selected, simple quality control checks were made. For temperature data,
we checked whether there were any cases where Tmax < Tmin, and for precipitation we looked for negative
values. No negative precipitation values were found and only very few inconsistent temperature values (∼50
cases out of the ∼1.5 million station-days with temperature data). These few cases were checked and about half
of them were left uncorrected, as these were found to occur in periods with very small diurnal temperature
variability, and hence are not seriously incorrect for our purposes (at least some of them may in fact be
entirely correct, as the time interval for reading Tmax and Tmin sometimes differ). In the remaining cases
with obviously wrong values, we either inserted a missing value or a corrected value whenever a plausible
value could be quite easily determined (e.g. cases with missing minus signs, or errors by 10 ° C when daily
mean temperatures were also available and thus provided additional information). During the later work, when
we studied index time series (of a particular extreme), a few cases were detected when zeros were present
instead of a flag for missing numbers. In these cases we inserted proper missing value flags and the index
b. Tmin
a. Precipitation
c. Tmax
60°N
60°N
60°N
50°N
50°N
50°N
40°N
40°N
40°N
0°
10°E
20°E
30°E
0°
10°E
20°E
30°E
0°
10°E
20°E
30°E
Figure 1. Locations of the 81 European stations used in this study. Small black (large grey) dots indicate stations with complete data
1921–99 (1901–99): (a) daily precipitation; (b) daily temperature maxima; (c) daily temperature minima
Copyright  2005 Royal Meteorological Society
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time series were recalculated for the stations affected. This kind of error was also reported to the manager of
the ECA dataset, who could update the affected series on their Website.
3. THE SET OF EXTREMES INDICES
We study a set of 10 indices, where eight are taken from the set of 10 core indices selected in the
STARDEX project (see http://www.cru.uea.ac.uk/cru/projects/stardex/). STARDEX chose these core indices
for assessing different aspects of both precipitation (intensity, frequency and proportion of total precipitation)
and temperature (both magnitude and frequency) extremes. We use the same six precipitation indices as
in STARDEX, while we replaced their two frequency-based temperature indices (a heat-wave index and a
frost-day index) with two percentile-based indices, giving us a set with four percentile-based temperature
indices. Our choice of temperature indices allows assessments of changes in both the cold and warm tails for
both daily temperature maxima and minima. Furthermore, the indices chosen reflect changes in moderately
strong events (rather than highly unusual events) to ensure robustness as regards the detectability of trends
(Frei and Schär, 2001; Klein Tank and Können, 2003). A list with index acronyms and short explanations
is given in Table I. Each of the indices chosen can be defined for any selected season. Here, we study the
December–February (DJF) and June–August (JJA) seasons. For each index, one value is derived per season,
i.e. each index time series has a time resolution of one value per year.
3.1. Precipitation
Five of the indices (PREC90P, R90N, R90T, R5D and SDII) provide information on the ‘wetness’, whereas
the sixth index (CDD) is a measure of ‘dryness’. Sometimes we will refer to the first five collectively as ‘the
five wetness indices’. Together, the indices chosen represent a mixture of measures, including the magnitude
(PREC90P, R5D) and frequency (R90N) of strong precipitation events and the proportion of total precipitation
falling during intense events (R90T), whereas SDII is an index of the average precipitation on wet days rather
than an index of unusual events. Hence, SDII provides a possibility to compare changes in the average
precipitation with changes in the upper part of the distribution. The CDD measures the length of dry periods,
which may be longer than the season analysed. In these cases, the CDD value is set equal to the length of
the season (i.e. 92 for JJA and 90 or 91 for DJF).
Four of the indices (R90N, R90T, SDII, CDD) require a distinction between wet and dry days, where a
wet day (dry day) is defined as a day with 1.0 mm (<1.0 mm) precipitation. With this definition, a dry day
is allowed to have a small amount of precipitation, but generally small enough for the ground not to recover
after a long period of dryness.
Table I. Acronyms and definitions of the 10 indices for moderate climatic extremes
Acronym
Explanation
Unit
Precipitation indices
PREC90P
90th percentile of precipitation amount in wet days
R5D
Greatest 5-day total precipitation
R90N
No. of events with precipitation greater than long-term 90th percentile of wet days (P90)
R90T
Percentage of total precipitation from events > P90
SDII
Simple daily intensity (average precipitation per wet day)
CDD
Max no. of consecutive dry days
Temperature indices
TMAX90P
Tmax 90th percentile
TMAX10P
Tmax 10th percentile
TMIN90P
Tmin 90th percentile
TMIN10P
Tmin 10th percentile
Copyright  2005 Royal Meteorological Society
mm
mm
days
%
mm
days
°C
°C
°C
°C
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R90N and R90T use percentile-based thresholds (90th percentile) and, as the units are the number of days
and percentage of the total precipitation respectively, comparisons of trends at different stations can be made
directly. The units for PREC90P, R5D and SDII are millimetres; thus, it is necessary to normalize these
indices before comparing trends at stations in different climate regimes. The long-term percentile (P90) used
as threshold for R90N and R90T is a fixed quantity for each station and season, calculated from all available
precipitation data (in all years) for wet days in the appropriate season. Percentiles are determined from the
empirical distribution with interpolation.
The PREC90P shows how the 90th percentile of wet days in a given season varies from year to year. For
a given summer, it is defined simply as the 90th percentile empirically determined from the wet days in the
92 day period between 1 June and 31 August (and analogously for a given winter). Because the percentile
is estimated from wet days only, it can sometimes (particularly in summer) be based on very few values,
which causes an uncertainty in its estimation. Nevertheless, we found later on (in Section 5) that the spatial
pattern of trends in PREC90P, and the significance of trends, is very similar to those for the other wetness
indices. If there are no wet days at all in a season, then PREC90P (and also R90T) cannot be calculated and
a missing value is assigned. A possible way to overcome this problem would be to analyse seasons that are
longer than 3 months, but this was not attempted here to ensure that all analyses refer strictly to the DJF and
JJA seasons.
As the three indices PREC90P, R90N and R90T all involve the 90th percentile, they reflect the same degree
of ‘unusualness’ of precipitation events. It is not obvious, though, how they relate to R5D and how strong
the events they reflect are compared with SDII. Figure 2 visualizes an impression of these relations. The
1921–99 average SDII value for each station is plotted in descending order. Using the same station order,
the long-term 90th percentile (P90; which is a constant quantity for each station and season) is also plotted,
as well as the 1921–99 averages for the PREC90P and R5D values. For comparison of R5D with the other
indices in Figure 2, we first standardized the R5D index values so that they show the average amount per
mm/day
102
P90
PREC90P
R5D
SDII
a. DJF
101
100
0
10
20
10
20
30
40
50
60
30
40
50
60
102
mm/day
b. JJA
101
100
0
Station no.
Figure 2. Comparison of the size of precipitation intensity reflected by the five wetness indices. Plotted for each station are the 1921–99
average values for SDII and PREC90P, the long-term 90th percentile (P90), and the 1921–99 average values for R5D normalized by
dividing by the average number of precipitation days in the 5-day sequences that determined the index values. P90 is used as threshold
for the indices R90N and R90T: (a) winter (DJF); (b) summer (JJA). The stations are plotted in order of decreasing SDII values,
separately for DJF and JJA. See text and Table I for definition of acronyms
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day with precipitation. This was achieved by dividing by the average number of days with precipitation in
the particular 5-day sequences that determined the R5D values. This average number of days was found to
be between four and five for most stations. The graphs for the winter data reveal a very clear relation, where
P90 and the average PREC90P value are nearly identical and both are stably about 2.2 times larger than
SDII. The standardized R5D values are always smaller, being on average about 1.7 times larger than SDII.
The R5D/SDII ratio is not constant however, and decreases with decreasing SDII. It is evident from Figure 2
that the PREC90P, R90N, R90T and R5D indices reflect rather moderate extremes, as they refer to events
that are on average only about an order 2 larger (both for winter and summer) than the average precipitation
per wet day.
The relationships for the JJA data (where the station order in Figure 2 is different than DJF) are mostly
similar to those for DJF, but there are some clear anomalies, particularly for stations with very small average
SDII values (<5 mm; stations 57–61 in Figure 2(b)). These stations are located on Cyprus and in Israel,
where there is often no precipitation at all in the JJA season. The stations nos. 17 and 30 in Figure 2(b),
also with irregularities in the relationships, are located on Cyprus too. The precipitation events at the stations
on Cyprus and in Israel are so rare in the JJA season that the various precipitation indices become quite
meaningless and, therefore, the relationships between them are very unstable. Similarly, for the same stations,
the CDD index in the JJA season is more or less a random variable, which is often more dependent on the
date(s) when the rare precipitation events (if any) take place than on the weather in the season analysed.
Therefore, we decided to omit the stations in Cyprus and Israel from precipitation analyses in the JJA season,
and to use these only for analyses in the DJF season.
3.2. Temperature
For temperature, we study four indices that are all of the same kind; they are based on either the 10th or
the 90th percentile of the distribution of daily Tmax or Tmin data. The TMAX90P value for a single JJA
season, for example, is simply the 90th percentile empirically determined from the 92 daily Tmax values
between 1 June and 31 August in that year. The resulting index time series shows how the 90th percentile
for daily values varies from year to year. The other indices are calculated analogously.
3.3. Additional missing value check
Before an index value was assigned for a particular season and year, we made a final check of the number
of days with missing values in that particular season. This check was necessary, as the missing-value check
described in Section 2.1 was only used to reject or accept a whole station record. Even an accepted record
may contain too many missing days in one particular season. In the final check, no missing days were allowed
for CDD in the 3-month seasons analysed. For all other indices, up to six missing days per 3 month season
were allowed. A missing value was assigned in the cases when the test was not passed.
4. TREND ESTIMATION METHODS
Linear trends in indices for climate extremes have previously been investigated for various regions or globally
(e.g. DeGaetano, 1996; Plummer et al., 1999; Manton et al., 2001; Frich et al., 2002; Kiktev et al., 2003; Klein
Tank and Können, 2003; Haylock and Goodess, 2004), mainly for the second half of the 20th century. Most
of these studies have recognized that index series may have properties that make them unsuitable for ordinary
least-squares trend fitting simply followed by significance assessments based on the standard Student’s t-test.
The reasons for this, for example, may be non-normal distribution, autocorrelation and the presence of outliers.
Different methods have been used to account for this, e.g. by using non-parametric methods for trend fitting
and/or Monte Carlo procedures for significance assessments. None of the studies mentioned, however, has
presented direct comparisons between different methods for trend fitting and significance testing, and there is
no universally preferred method.
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We estimate linear trends in the extremes indices at each station for the four periods 1901–99, 1921–99,
1901–50 and 1946–99 using two different methods and compare the results. The 1921–99 period is the
longest possible when all stations with complete enough series can be used. The periods 1901–50 and
1946–99 show trends for approximately the first and second halves of the 20th century. The latter period
was chosen (despite its short overlap with 1901–50) so as to make direct comparisons possible with earlier
analyses of climate indices based on the ECA data by Klein Tank et al. (2002a; Klein Tank and Können,
2003).
With 10 indices, two seasons, two methods and four periods, the total number of analyses is 160. A map
showing the trends was produced for each analysis, and all the 160 maps were inspected visually to obtain
an overall impression of the results and to identify individual stations with obviously anomalous properties
compared with nearby stations. Complementary time series plots for single stations versus neighbouring
stations suggested homogeneity problems for a number of stations. This led to the rejection of a few stations
from further analyses. In particular, precipitation indices for the high-elevation alpine stations of Zugspitze,
Säntis and Sonnblick (all above 2400 m a.s.l.) behave markedly differently, both compared with each other
and with nearby low-elevation stations. It is beyond the scope here to investigate whether these differences
are due to inhomogeneous data or due to their extreme locations. To simplify the analyses we excluded these
stations from further analyses, for both their temperature and precipitation data.
4.1. Two trend estimators
Linear trends were estimated using both the standard ordinary least squares (OLS) method (e.g. von Storch
and Zwiers, 1999) and a so-called resistant (RES) method (Emerson and Hoaglin, 1983), which is also
sometimes referred to as a robust method. The latter involves a division of the data into three groups (the
first, middle and last thirds) and achieves resistance to ‘wild points’ by using medians within the groups.
An iterative process is then used to polish the line fit. The RES method is selected to account better for
outliers and non-normality than the OLS method. The OLS method is widely used in many applications
and is computationally very efficient. In comparison, the RES method is much more time consuming, as it
involves iteration until convergence is achieved. When used in combination with Monte Carlo techniques for
significance testing (as we do), the RES method is very time consuming.
4.2. Significance tests
Nicholls (2001) discussed various reasons why classical dichotomous significance testing is not valid in the
kind of exploratory analyses often undertaken in climate research. One reason is that rejection of all values
that are not significant at a certain probability level leads to the loss of a substantial amount of information
in the data analysed. More information will be retained if the focus is more on the strength of a relationship
(e.g. the magnitude of a linear trend) than on statistical significance only. In this paper we apply significance
testing, but we show both the results from significance tests and the size of all trends (both significant and
insignificant) when we plot the results on maps.
To assess significance we use a so-called moving-block bootstrap technique that is identical to the method
used by Kiktev et al. (2003) in their global study of gridded extremes indices. This method involves a random
resampling of the time series for each station, to produce a distribution of 1000 series that could plausibly
have occurred in the real world. Rather than resampling the individual values separately, blocks of consecutive
values in the original series are used to ensure that the autocorrelation structure is maintained. An extensive
discussion of the moving-block bootstrap, although applied to differences of means rather than trends, is given
by Wilks (1997). For each resampling, the trend is estimated using both the OLS and RES methods. This
gives 1000 estimates of ‘plausible’ trends for each method. The 2.5 and 97.5 percentiles of the trend estimates
then provide a 95% confidence interval. A trend is considered as significant if this confidence interval does
not contain a zero trend. See Kiktev et al. (2003) for full details of the method.
With the moving-block bootstrap method it is necessary to determine the length L of the blocks to be
shuffled around in the resampling. Wilks (1997) presented three alternative methods for determining L. We
determined L for each index series at each station for each season using two of the methods. These assume
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
EXTREME DAILY TEMPERATURE AND PRECIPITATION TRENDS
1157
Table II. Block lengths L used in
moving-block bootstrapping for significance testing of linear trends
DJF
JJA
PREC90P
R90N
R90T
R5D
SDII
CDD
2
3
2
2
3
2
2
3
2
2
2
2
TMAX90P
TMAX10P
TMIN90P
TMIN10P
2
3
2
3
3
2
4
4
that the time series can be modelled as either a first- or a second-order autoregressive process. We found
the first-order model to be most valid. As the autocorrelation differs among stations, there is a choice as to
whether to use individual L-values for each station or to adopt one universal block length for all stations and
a given variable. Choosing too small an L-value means that the test becomes permissive (too many trends
may be regarded as significant), whereas choosing too large an L-value means that the test becomes too
restrictive. We chose a universal L-value by taking the smallest integer such that at least 90% of the stations
have their individually estimated L-values less than or equal to the universal L-value, thus ensuring that the
test is not permissive as regards the large majority of stations. This choice is the same as in Kiktev et al.
(2003).
Table II lists the block lengths for each index and season as determined for the 1921–99 data. All L-values
are between 2 and 4 (the majority are 2, with 3 being the second most common), implying that autocorrelation
must be taken into account (otherwise the L-values would have been equal to 1). This result is in agreement
with Kiktev et al. (2003), who found that block lengths of either two or three were adequate for the indices
they used.
5. RESULTS
Here, we present a selection of the main results in the form of maps, tables and time series. Tables III and
IV list the percentage of stations with statistically significant (two-sided tests at the 0.05 level) positive and
negative trends for each of the four time periods. Separate columns (labelled O and R) show the percentages
of significant trends for the OLS and RES methods respectively. The columns labelled B show the percentage
of stations where both methods give significant trends. Trend maps for selected precipitation (Figure 3) and
all temperature indices (Figures 4 and 5) are also shown. These maps show the average sizes of trends
obtained with the OLS and RES methods and they also indicate stations where significance is reached with
both methods. Additionally, time series for selected indices for one particular region (in central Europe, see
Figure 6) are shown in Figures 7 and 8.
With the significance level chosen, it is expected that about 5% of the stations have significant trends of
any sign just by chance (i.e. about one to three stations given the about 30–60 stations analysed, depending
on time period and index used). Allowing for spatial correlation, it is only when the percentage is larger than
5%, by some amount, that a climatic change can be said to be statistically significant. Our approach to using
the information contained in the significance tests is to identify those time periods and those indices for which
there is evidence for trends that are strong relative to the temporal variability. Therefore, we focus on cases
where a substantial fraction of the stations have significant trends of the same sign, and in particular when
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
1158
A. MOBERG AND P. D. JONES
Table III. Percentage of stations with linear trends in precipitation indices that are significant at the 5% level, with
positive (italics) and negative signs, as obtained with the OLS method (O), RES method (R) and with both methods (B)
1901–99
Positive
1921–99
Negative
Positive
1901–50
Negative
Positive
1946–99
Negative
Positive
Negative
O
R
B
O
R
B
O
R
B
O
R
B
O
R
B
O
R
B
O
R
B
O
R
B
Winter
PREC90P
R90N
R90T
R5D
SDII
CDD
36
36
28
18
41
0
33
23
26
21
26
0
28
21
15
13
21
0
0
0
3
3
0
3
3
3
0
0
0
3
0
0
0
0
0
3
34
29
29
28
36
0
28
38
31
31
41
3
28
28
24
22
31
0
2
7
3
3
9
12
3
9
2
7
7
12
2 8
5 0
2 0
2 5
5 13
9 3
5
3
0
3
8
3
0
0
0
0
3
0
8
5
3
5
5
3
5
8
3
3
8
3
5
5
0
3
3
3
12
10
12
7
16
3
10
7
9
9
16
5
9
5
9
3
9
2
3
5
0
7
2
2
5
3
3
5
0
5
2
2
0
3
0
0
Summer
PREC90P
R90N
R90T
R5D
SDII
CDD
15
10
10
10
8
5
13
8
10
5
3
8
8
3
5
5
3
3
0
3
3
3
8
3
3
3
3
3
3
3
0 6
0 4
0 4
3 4
3 4
3 10
6
4
4
6
0
6
2
2
2
4
0
6
2
4
0
2
4
6
2
6
4
2
6
4
3
5
0
0
13
8
3
3
0
0
8
3
0
0
0
0
0
0
0
0
3
3
3
3
0 6
0 6
0 0
0 2
0 2
0 12
0
4
0
2
2
16
0
4
0
0
2
8
2
6
0
0
4
6
2
4
2
4
2
4
0
4
0
0
0
4
0
0
0
0
2
4
5
5
5
5
8
8
Table IV. Percentage of stations with linear trends in temperature indices that are significant at the 5% level, with positive
(italics) and negative signs, as obtained with the OLS method (O), RES method (R) and with both methods (B)
1901–99
Positive
O
Negative
B
O
R
Winter
TMAX90P 54 18 18
TMAX10P 4 7 4
TMIN90P 27 17 13
TMIN10P
7 7 7
0
0
0
0
0
0
0
0
Summer
TMAX90P
TMAX10P
TMIN90P
TMIN10P
4
7
0
7
4
7
0
10
36
18
70
43
R
1921–99
32
14
60
27
18
11
60
27
B
Positive
R
Negative
Positive
B
O
R
B
0 54 35 33
0 6 17 4
0 34 40 26
0 16 16 10
0
6
0
4
0
2
0
2
0
2
0
2
4
7
0
7
O
1901–50
15 8 8
13 2 2
34 14 14
22 24 18
6 13 4
4 13 4
4 6 4
10 12 10
O
1946–99
Negative
R
B
O
R
7 14
0 0
0 3
0 0
4
0
0
0
0
18
7
17
0
4
3
3
64
46
67
47
43
32
57
40
39
25
53
33
0 0
0 0
3 3
13 17
B
Positive
O
Negative
R
B
O
R B
0 31 10
4 6 2
3 26 8
3 18 6
8
2
8
6
0
2
0
0
0
0
0
0
0
0
0
0
0 15 19 8
0 2 2 0
3 40 30 28
7 38 22 20
6
10
2
2
6
8
2
4
4
8
2
0
this occurs with both methods. We do not define rigorously how large a ‘substantial fraction’ must be, but
suggest 20% as a rule-of-thumb. This is considered, somewhat ad hoc, as a conservative choice, but essential
given the likely presence of a number of inhomogeneous station records.
Inspection of Tables III and IV shows that there are sometimes more station trends that reach significance
with the OLS method, and sometimes there are more significant trends with the RES method. A count of the
number of significant trends reveals that these are more frequent with the OLS method. In those cases where
there are substantially more significant cases with one of the methods, it is always OLS that has the larger
number. Thus, significance is more easily reached with the OLS than with the RES method. This is due to a
smaller variance of the OLS estimator itself compared with RES.
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
EXTREME DAILY TEMPERATURE AND PRECIPITATION TRENDS
a. PREC90P, DJF 1921_99
1159
d. PREC90P, JJA 1921_99
%/100yr
−40
−20
60°N
−8
60°N
+8
+20
50°N
50°N
+40
+60
40°N
40°N
0°
10°E
20°E
0°
30°E
10°E
20°E
30°E
e. SDII, JJA 1921_99
b. SDII, DJF 1921_99
%/100yr
−40
−20
60°N
−8
60°N
+8
+20
50°N
50°N
+40
+60
40°N
40°N
0°
10°E
20°E
0°
30°E
10°E
20°E
30°E
f. CDD, JJA 1921_99
c. CDD, DJF 1921_99
# days/100yr
+5
+2
−2
60°N
60°N
−5
−10
50°N
−15
50°N
−20
−25
40°N
40°N
0°
10°E
20°E
30°E
0°
10°E
20°E
30°E
Figure 3. Trends for three indices for moderate precipitation extremes, 1921–99. Colours represent the average trend estimated with the
OLS and RES methods (see text for definitions). Small black dots indicate where significance is reached with both methods (two-sided
0.05-level significance): (a)–(c) winter (DJF); (d)–(f) summer (JJA)
5.1. Precipitation
Significant positive trends in all the five wetness indices (Table III) are common in the DJF season for the
two longer periods (1901–99 and 1921–99), but there is much less evidence of significant DJF trends in
the two century halves. For the JJA season, there are generally rather few stations with significant trends in
any period. Increasing trends in average and moderately strong precipitation intensity are thus most clearly
significant in winter and, furthermore, when trends are calculated over the longer periods. Trends in the CDD
index are mostly insignificant in both seasons.
Maps showing trends for three selected indices (PREC90P, SDII, CDD) for the 1921–99 period are
presented in Figure 3. Trends for PREC90P and SDII are normalized and express the percentage change
relative to the 1921–99 station average. Only two of the wetness indices are shown, but the corresponding
maps for the other three have qualitatively similar patterns.
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
1160
A. MOBERG AND P. D. JONES
a. TMAX90P, DJF 1901_50
e. TMAX90P, DJF 1946_99
60°N
60°N
50°N
50°N
40°N
40°N
0°
10°E
20°E
30°E
b. TMAX10P, DJF 1901_50
0°
60°N
50°N
50°N
40°N
40°N
10°E
20°E
30°E
c. TMIN90P, DJF 1901_50
50°N
50°N
40°N
40°N
20°E
60°N
50°N
50°N
40°N
40°N
10°E
20°E
30°E
20°E
30°E
10°E
20°E
30°E
h. TMIN10P, DJF 1946_99
60°N
0°
10°E
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
0°
30°E
d. TMIN10P, DJF 1901_50
30°E
g. TMIN90P, DJF 1946_99
60°N
10°E
20°E
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
0°
60°N
0°
10°E
f. TMAX10P, DJF 1946_99
60°N
0°
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
0°
10°E
20°E
30°E
Figure 4. Trends for the four indices for moderate temperature extremes in the winter (DJF) season. Colours represent the average trend
estimated with the OLS and RES methods (see text for definitions). Small black dots indicate where significance is reached with both
methods (two-sided 0.05-level significance): (a)–(d) 1901–50; (e)–(h) 1946–99
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
EXTREME DAILY TEMPERATURE AND PRECIPITATION TRENDS
1161
In the DJF season, all five wetness indices display similar patterns, with positive 1921–99 trends dominating
much of central and western Europe and the few scattered stations in eastern Europe (in Ukraine and Russia).
Over Scandinavia the picture is less clear, with both positive and negative trends, mostly insignificant ones,
in about equal proportions. The stations on Cyprus, in Israel and a few in the northern Mediterranean area
mostly show insignificant decreasing trends or almost no change.
The SDII is the index with the largest number of significant increasing 1921–99 DJF trends. In central and
western Europe, the increases in SDII (Figure 3(b)) are generally between 8 and 40% per century relative to
the 1921–99 average (a few values between 40 and 60% also occur). Although the total number of significant
trends is slightly less for PREC90P (Figure 3(a)), there are more stations with trends of 40–60% per century
compared with SDII. There is thus a weak tendency for greater increases in the upper part of the distribution
than in the average precipitation intensity for the DJF season.
The map for trends in CDD for the DJF season during 1921–99 (Figure 3(c)) shows a dominance of
insignificant positive trends (i.e. longer dry spells) over much of central and western Europe. This observation
suggests a tendency for winter climate in this region to have become slightly drier in terms of the length of
dry spells, but when precipitation falls it has become significantly wetter both with respect to average and
moderately strong precipitation events.
As already mentioned, there are few significant trends in the JJA season. The PREC90P and SDII maps
for 1921–99 trends (Figure 3(d) and (e)) suggest slightly increasing precipitation trends over parts of central
and western Europe, but slightly decreasing trends or no change over Scandinavia. The maps further suggest
that the drying trends over Scandinavia were more widespread for PREC90P than SDII, thus suggesting a
larger change in the upper part of the distribution compared with the average intensity. The summer map for
CDD (Figure 3(f)) is dominated by insignificant increasing trends (longer dry spells) over most of the study
region. When comparing the two century halves (Table III) there is somewhat more evidence of significant
positive CDD trends for summers in the second half. The corresponding maps for this period (not shown)
reveal that these significant trends occur over north-central Europe and southern Scandinavia.
5.2. Temperature
Overall, there are more significant warming trends compared with the number of significant cooling trends
for both winter and summer for the two longer periods 1901–99 and 1921–99 (Table IV). In contrast to the
precipitation indices, there are also a substantial number of cases with significant trends in the two century
halves (particularly for JJA). The pattern of trends is notably different in these two periods. The trend pattern
for DJF is also different compared with that for JJA. We show trend maps for the two periods 1901–50 and
1946–99 in Figures 4 and 5 to illustrate the differences between the two century halves.
In the 1901–50 period, cooling in the DJF season dominates the maps in Figure 4 (apart from TMAX90P,
where there is no clear pattern). The size of the winter cooling trend is particularly strong for the two coldtail indices (TMAX10P, TMIN10P; often between −2 and −5 ° C/century). Few of these trends, however,
reach significance with both methods (although nearly 20% are significant with OLS), indicating that the
background variability upon which the trends occur is large. In contrast to the first half of the century, all
four DJF indices for the 1946–99 period are clearly dominated by warming, where TMIN10P shows the
largest warming trends (many station trends above +3 ° C/century). Few 1946–99 DJF trends are significant
with both methods, but several significant warming trends are found with the OLS method for TMAX90P,
TMIN90P and TMIN10P. Because the sign of trends for TMAX10P and TMIN10P are essentially of opposite
sign in the two century halves, a part of the strong warming over 1946–99 for the two cold-tail indices can
be regarded as a recovery after the preceding cooling.
In summer, there is an abundance of significant warming trends for TMIN90P (i.e. generally warm nighttime temperatures) over the entire 1901–99 period (Table IV, map not shown), with as many as 60% of the
stations reaching significance with both methods and even 70% with OLS. The 1901–99 map for TMAX90P
(not shown) is also dominated by warming trends, but with considerably fewer trends reaching significance
with both methods. Also, the TMIN10P map (not shown) depicts overall warming over the 20th century, but
there is no clear trend pattern for TMAX10P.
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
1162
A. MOBERG AND P. D. JONES
a. TMAX90P, JJA 1901_50
e. TMAX90P, JJA 1946_99
60°N
60°N
50°N
50°N
40°N
40°N
0°
10°E
20°E
30°E
0°
b. TMAX10P, JJA 1901_50
60°N
50°N
50°N
40°N
40°N
10°E
20°E
60°N
50°N
50°N
40°N
40°N
20°E
60°N
50°N
50°N
40°N
40°N
20°E
30°E
20°E
30°E
10°E
20°E
30°E
h. TMIN10P, JJA 1946_99
60°N
10°E
10°E
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
0°
30°E
d. TMIN10P, JJA 1901_50
0°
30°E
g. TMIN90P, JJA 1946_99
60°N
10°E
20°E
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
0°
30°E
c. TMIN90P, JJA 1901_50
0°
10°E
f. TMAX10P, JJA 1946_99
60°N
0°
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
°C/100yr
+5
+4
+3
+2
+1
+0.5
−0.5
−1
−2
−3
−4
−5
0°
10°E
20°E
30°E
Figure 5. As Figure 4, but for the summer (JJA) season
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
EXTREME DAILY TEMPERATURE AND PRECIPITATION TRENDS
1163
60°N
50°N
40°N
0°
10°E
20°E
30°E
Figure 6. Locations of stations used to create the central European regional average index series shown in Figures 7 and 8. Crosses
indicate stations with precipitation data (Prague, Karlsruhe, Stuttgart, Potsdam, Bamberg, Jena, Basel, Zürich). Filled circles indicate
stations with temperature data (Frankfurt, Stuttgart, Potsdam, Bamberg, Jena, Zürich)
When splitting the summer trend analysis into the two half centuries (Figure 5), there is more evidence
for warming in the first half than the second half. In the 1901–50 period, all four indices have more than
20% positive trends reaching significance with both methods, with TMIN90P having the largest number of
significant trends (53%). In terms of magnitude, the largest 1901–50 trends are found for TMAX90P (the
majority larger than +4 ° C/century). In the 1946–99 period, only the two Tmin indices have at least 20%
positive trends reaching significance with both methods, and there are also many insignificant cooling trends
on the TMIN10P map. A notable feature in the JJA maps for 1946–99 is that TMAX90P is generally warming
in central Europe while TMAX10P is cooling. Although few of these trends reach significance, they suggest
a widening of the distribution of daily temperature maxima in the second half of the 20th century.
5.3. Regional average index time series for central Europe
To obtain a view of details in the temporal evolution of the indices, we show time series (Figures 7 and 8)
for a selection of individual station records (grey curves) in central Europe and an average of these series
(black). The station locations are shown in Figure 6. The selection includes eight stations with precipitation
data and six with temperature data. Five stations have both data types. The region chosen has the largest
density of stations with complete 1901–99 records in the study area, and all stations selected have data for
the entire period. Linear trends for the regional average series, calculated with both methods for the same
periods as above, are given in Tables V and VI, with bold numbers indicating significant trends. Additionally,
correlations between the various index time series are listed in Tables VII and VIII.
5.3.1. Precipitation
For the five wetness indices in the DJF season, all central European regional trends indicate significant
increases during 1901–99 and 1921–99 (bold values in Table V). The increase in winter precipitation during
the 20th century for PREC90P and SDII is visualized in Figure 7(a) and (b) as long-term increasing trends
with some decadal-scale variability superimposed. The inflection points on the smoothed curves (highlighting
decadal and longer scales) in PREC90P and SDII also have counterparts in the three wetness indices that
are not shown, indicating that all five share much common information. The strong correlations between
the indices (0.78–0.95; see above the main diagonal in Table VII) further illustrate a considerable similarity
among the wetness indices in the winter season. A further noteworthy observation (Table V) is that, although
the size of the trends estimated with the two methods are rather similar in most cases when both yield
significant trends, the RES method gives 50–75% larger trends compared with OLS for the R5D index (DJF,
1901–99 and 1921–99). This indicates that the size of estimated trends can depend quite substantially on the
method of trend estimation chosen.
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
1164
A. MOBERG AND P. D. JONES
d. PREC90P, JJA
280
280
240
240
100*index/mean
100*index/mean
a. PREC90P, DJF
200
160
120
160
120
80
80
40
1900
200
1920
1940
1960
1980
40
1900
2000
1920
280
280
240
240
200
160
120
1920
1960
1980
2000
1980
2000
1980
2000
200
160
120
40
1900
2000
1920
40
40
30
30
20
10
1940
1960
f. CDD, JJA
50
# days
# days
1940
c. CDD, DJF
50
0
1900
1980
80
80
40
1900
1960
e. SDII, JJA
100*index/mean
100*index/mean
b. SDII, DJF
1940
20
10
1920
1940
1960
1980
2000
0
1900
1920
1940
1960
Figure 7. Time series for three precipitation indices for the central European stations in Figure 6. Grey curves show data for individual
stations. Thin black curves show the arithmetic average for all stations. Thick black curves highlight variability on time scales longer
than a decade. Horizontal dotted lines show the 1901–99 average. Data for PREC90P and SDII are normalized by their 1901–99
average: (a)–(c) winter (DJF); (d)–(f) summer (JJA)
For the JJA season there is a total absence of significant trends (Table V) in the precipitation indices for
the central European regional series. Accordingly, the corresponding time series (Figure 7(d)–(f)) show a
character of climatic noise around a constant level. This further strengthens the impression from Section 5.1,
that there is little evidence of significant change in the precipitation indices for summer in this region.
The correlations between the various wetness indices are slightly weaker for summer data (0.69–0.91;
below the main diagonal in Table VII) compared with winter data, but strong enough to reveal a substantial
amount of similar information among the wetness indices also for summer. The CDD index, on the other
hand, is only weakly negatively correlated with all five wetness indices for both seasons (−0.19 to −0.45),
suggesting that CDD provides information that is essentially different from the wetness indices.
5.3.2. Temperature. All four smoothed index curves for the DJF season (Figure 8(a)–(d)) lie above the 20th
century average after around 1990. The graphs also show evidence for relative winter warmth in the early
decades of the century and, furthermore, that the background interannual variability in the DJF temperature
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
1165
EXTREME DAILY TEMPERATURE AND PRECIPITATION TRENDS
e. TMAX90P, JJA
1900
degC
degC
a. TMAX90P, DJF
6
4
2
0
−2
−4
−6
−8
−10
1920
1940
1960
1980
2000
6
4
2
0
−2
−4
−6
−8
−10
1900
1920
1900
1920
1940
1960
1980
2000
1900
1940
1960
1980
1920
1920
degC
degC
degC
1940
1960
1960
1980
2000
1940
1960
1980
2000
1980
2000
h. TMIN10P, JJA
d. TMIN10P, DJF
1920
1940
6
4
2
0
−2
−4
−6
−8
−10
1900
2000
6
4
2
0
−2
−4
−6
−8
−10
1900
2000
g. TMIN90P, JJA
degC
1920
1980
6
4
2
0
−2
−4
−6
−8
−10
c. TMIN90P, DJF
6
4
2
0
−2
−4
−6
−8
−10
1900
1960
f. TMAX10P, JJA
6
4
2
0
−2
−4
−6
−8
−10
degC
degC
b. TMAX10P, DJF
1940
1980
2000
6
4
2
0
−2
−4
−6
−8
−10
1900
1920
1940
1960
Figure 8. Time series for temperature indices for the central European stations in Figure 6. Grey curves show data for individual stations.
Thin black curves show the arithmetic average for all stations. Thick black curves highlight variability on time scales longer than a
decade. Horizontal dotted lines show the 1901–99 average. Data are plotted as anomalies from the 1901–99 average: (a)–(c) winter
(DJF); (d)–(f) summer (JJA)
Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)
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A. MOBERG AND P. D. JONES
Table V. Linear trends in central European regional average precipitation indices estimated with the OLS (O) and
RES (R) methods. Bold values indicate significance at the 5% level. Units are: normalized precipitationa expressed
as percent/century (for PREC90P, R5D and SDII); days/century (R90N and CDD); percentage of total seasonal
rainfall/century (R90T)
1901–99
1921–99
1901–50
1946–99
O
R
O
R
O
R
O
R
Winter
PREC90P
R90N
R90T
R5D
SDII
CDD
14.94
1.10
8.55
16.55
13.33
0.82
12.99
1.42
7.47
28.14
14.64
−2.08
18.91
1.62
12.99
30.16
20.04
1.60
20.85
1.94
13.97
46.52
18.07
−2.08
26.60
1.09
6.79
12.92
17.43
−1.38
12.73
0.10
2.78
7.42
12.29
−5.22
11.53
0.24
7.49
20.15
13.51
5.26
18.22
1.37
16.89
39.68
16.57
0.15
Summer
PREC90P
R90N
R90T
R5D
SDII
CDD
5.49
0.17
4.04
4.21
3.40
1.63
3.52
0.00
2.29
8.62
7.33
1.11
8.38
0.29
6.17
2.60
3.12
2.25
3.90
−0.12
2.00
−1.92
7.39
1.44
−2.55
−0.08
−0.13
10.14
7.70
4.34
−6.97
−9.15
−1.79
3.73
−1.23
1.19
−0.39
−0.11
1.74
−23.07
−9.87
0.91
3.90
0.47
0.82
−18.73
−10.08
2.30
a For
PREC90P, R5D and SDII, the trend is divided by the 1921–99 average for each variable. A trend value in the table of, for
example, 10 (or −10) means that there is an increase (or decrease) by 10% per century relative to the 1921–99 average.
Table VI. Linear trends in central European regional average temperature indices estimated with the OLS (O) and RES
(R) methods. Bold values indicate significance at the 5% level. Unit:° C/century
1901–99
1921–99
1901–50
1946–99
O
R
O
R
O
R
O
R
Winter
TMAX90P
TMAX10P
TMIN90P
TMIN10P
0.78
0.14
0.56
0.28
0.82
0.85
0.44
0.97
1.98
1.30
1.28
1.53
2.06
1.17
1.60
2.65
−0.89
−3.73
−1.23
−3.87
1.34
−0.66
−0.71
0.61
3.26
2.14
2.45
3.04
1.24
1.38
3.04
0.78
Summer
TMAX90P
TMAX10P
TMIN90P
TMIN10P
0.41
−0.46
1.24
0.69
0.60
−0.32
1.13
0.50
−0.16
−0.60
1.06
0.54
−1.10
−1.01
0.87
0.09
3.70
1.25
2.14
1.77
3.33
1.09
2.01
1.39
0.27
−1.58
1.50
0.72
1.80
−1.59
1.77
0.65
indices is large compared with long-term changes (in particular for TMAX10P and TMIN10P). All DJF trend
estimates (Table VI) for periods ending in 1999 are positive, but significance is reached only in a few cases
with the OLS method. An additional noteworthy observation for the DJF season in Figure 8 is the local
minimum of the smoothed curves for both TMAX10P and TMIN10P in the early 1940s. The low index
values at this time reveal that the large negative trends shown on the maps in Figure 4(b) and (d) do not
reflect a gradual increase during the first half of the past century, but are instead largely due to notably low
values in the early 1940s, whereas little change occurred in central Europe during the preceding 40 years.
There is more evidence for significant trends in the summer indices. There are also notable differences
between the time-series behaviour of the Tmax and Tmin indices for the summer data. Significant JJA
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EXTREME DAILY TEMPERATURE AND PRECIPITATION TRENDS
Table VII. Correlation coefficients (×100) between the precipitation indices for the central European
regional average in the 1901–99 period. Entries above (below) the main diagonal give correlations for
winter (summer) data
PREC90P
R90N
R90T
R5D
SDII
CDD
PREC90P
R90N
R90T
R5D
SDII
CDD
–
85
91
69
91
−17
86
–
87
77
87
−36
93
91
–
78
89
−15
78
85
80
–
81
−27
95
88
90
83
–
−19
−26
−45
−31
−38
−24
–
Table VIII. Correlation coefficients (×100) between the temperature indices for the central
European regional average in the 1901–99 period. Entries above (below) the main diagonal
give correlations for winter (summer) data
TMAX90P
TMAX10P
TMIN90P
TMIN10P
TMAX90P
TMAX10P
TMIN90P
TMIN10P
–
50
77
23
54
–
42
62
89
60
–
42
57
96
61
–
warming trends for the TMIN90P index occur for the three periods 1901–99, 1921–99 and 1901–50. The
time series for this index (Figure 8(g)) shows a relatively steady warming over the 20th century. This gives
further substance to the finding in Section 5.2, that warming of TMIN90P in summer is a significant feature
of European climate change in the 20th century. The graph for TMIN10P in summer also features relatively
steady warming over the past century, but significance is reached only with OLS (1901–99 and 1901–50).
In contrast to the relatively steady warming in summer for the two Tmin indices, the time series for the
two Tmax indices reveal more marked decadal-scale variability. The two Tmax series are also quite different
from each other. The TMAX90P has a decadal-scale maximum in the mid-1940s, whereas TMAX10P has
a somewhat later maximum around 1950. Furthermore, TMAX90P shows a pronounced decrease from its
maximum in the mid-1940s to low values in the early 1950s, after which an irregular warming has occurred to
the end of the record (although not significant for 1946–99). The TMAX10P shows instead a gently cooling
trend from its maximum near 1950 to around 1980, after which warming set in again (both trend methods
yield insignificant cooling trends for all three periods ending in 1999). The central European time series thus
add details to the previously observed widespread trends of different sign for TMAX90P and TMAX10P in
the second half of the 20th century (Figure 5(e)–(f)). Clearly, there has not been a steady widening of the
distribution of daily summer temperature maxima, but there is, rather, evidence of quite different behaviour
of the two indices. This is further revealed by their relatively weak correlation (+0.50; Table VIII). When
analysing the two century halves, it is seen that significant JJA trends occur only in the early half (significant
warming trends with both methods for TMIN90P but only with OLS for TMAX90P and TMIN10P). This
agrees with the finding in Section 5.2, that summer warming mainly occurred in the first half of the century.
The correlations among the various temperature indices (Table VIII) reveal stronger correlations in DJF
compared with JJA. For each season, stronger correlations are found for those indices that represent the
same percentile compared with those that represent the same variable (i.e. Tmax or Tmin). The strongest
correlation (0.96) is that between TMAX10P and TMIN10P for DJF, whereas the weakest (0.23) is that
between TMAX90P and TMIN10P for JJA. All eight entries in Table VIII where 10th and 90th percentiles
are correlated with each other are smaller than about 0.6, i.e. they have less than 40% variance in common.
This corroborates previous findings (Klein Tank and Können, 2003; Hegerl et al., 2004) that changes in the
Copyright  2005 Royal Meteorological Society
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warm and cold tails can behave notably differently, which implies that studies of mean temperatures fail to
reflect important changes that take place in the tails of the distribution.
6. DISCUSSION
With the data available to us, a high spatial density of daily temperature and precipitation records starting
around 1920 or earlier were only obtained for a central and western European region. Thus, one important
conclusion is that there is a clear need for more century-long daily observational records to allow a spatially
more extensive analysis covering the whole of the European continent. The main obstacle at present seems
to be that many national meteorological services (or other data holders) have not yet digitized much daily
data back before around 1950. Earlier non-digitized data should exist because monthly records back to
the 19th century are widespread. Recent progress on the digitizing side is fortunately being made, e.g. for
several Italian precipitation records (Brunetti et al., 2004), but these data were not available when our study
was made. Furthermore, the ongoing EU project EMULATE (within which the current study is a part;
http://www.cru.uea.ac.uk/cru/projects/emulate/) will digitize raw data and also collect already digitized data
from unpublished sources to create a database with European daily records starting before 1900. Hence, more
long European daily records will be available in the near future. However, with the main exception of North
America, it is still vital to digitize more long records of daily data, and make them available for research.
For other regions of the world they are even more vital, as, at present, global studies of changes in extremes
are limited to, at most, the last 50 years.
It is also essential to address the issue of data homogeneity before climatic interpretation of the observed
trends in extremes is made. As mentioned earlier, Wijngaard et al. (2003) found that the vast majority of
temperature series and a quarter of the precipitation records with data for the entire 20th century in the
ECA dataset (from which we obtained the data analysed here) have been classified as ‘doubtful’ or ‘suspect’
in homogeneity tests. Analyses of Finnish data (Tuomenvirta, 2001) show that extreme temperatures can
be more sensitive to relocations, screen changes and local environment changes than mean temperatures.
Homogeneity analyses of Italian, Austrian and Swiss data from the alpine region (Böhm et al., 2001) reveal
that inhomogeneities are very common in long time series and the time evolution of the inhomogeneities can
be very complex, with both station-specific and nationwide sources of non-climatic influences on the data.
When many stations from a region are aggregated, the averaged effects of inhomogeneities may lead both to
positive and negative artificial trends in the records. For example, growing cities can induce artificial warming
of temperature records from city locations, but sometimes this artificial warming is interrupted by a sudden
artificial cooling when city stations were relocated to airports at different times after the World War II (Böhm
et al., 2001). Precipitation data are also known to be commonly affected by inhomogeneities, often related to
changes in rain-gauge types (e.g. Tuomenvirta, 2001; Brunetti et al., 2004).
Even if many of the ECA series may have been subjected to homogenization by the institutions that
provided the records, there are certainly remaining non-climatic influences in the data, as revealed by the
findings of Wijngaard et al. (2003). It is not possible, with the information available at present, to judge with
any precision to what extent inhomogeneities have affected the trends in extremes indices observed here. Our
results, however, mostly show rather coherent patterns of trends (see Figures 3–5). For most of the maps,
there are only a few stations with anomalous trends compared with neighbouring stations. Furthermore, the
index time series for the selected subset of individual stations in central Europe shown in Figures 7 and 8
display changes that are synchronous at neighbouring stations. This suggests that the overall results in this
study are qualitatively robust to individual station inhomogeneities, and we judge that the observed trends
and changes are essentially reflecting real climatic changes, although the exact numerical values of trends
may be distorted.
Based on the available data, remembering its spatial-coverage- and homogeneity-related limitations, this
study has revealed some coherent patterns of climate changes in parts of Europe over the 20th century, using
various indices for moderate extremes in daily temperature and precipitation in both winter and summer.
The most outstanding feature for precipitation is that winter precipitation increased significantly at several
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stations, both regarding the mean precipitation intensity and moderately strong events; at the same time, the
length of dry spells in winters also generally increased (albeit insignificantly). The length of dry periods also
increased (insignificantly) in summer, but there are few significant changes in summer precipitation amounts.
Warming trends dominate in the study region over the 20th century as a whole, both in winter and summer
and both for the cold and warm tails of the temperature distribution. When analysing the two century halves
separately, there is evidence for markedly different behaviour in the warm and cold tails of the temperature
distribution and also strong differences between winter and summer. Winter temperatures warmed in the
second half of the century, with the largest changes in the cold tail for daily minimum temperatures. There
is much less evidence for widespread warming in summer in the same period. An outstanding feature, in
contrast, is cooling in the cold tail for daily temperature maxima and a simultaneous warming in the warm
tail, implying a widening of the distribution of daily Tmax in summer.
For an understanding of the underlying causes of the observed changes, it would be essential to analyse
how they relate to changes in atmospheric circulation and other climate variables, and also to analyse output
from transient climate model experiments with different forcings, from which similar extremes indices could
be derived. Such analyses will be undertaken elsewhere, e.g. in the EU project EMULATE. Here, however,
we restrict ourselves to documenting the observed changes and to discussing some of our findings in the
light of previous results. No formal attribution studies are attempted here. Furthermore, we pay little or no
attention to the vast literature on trends in mean temperature and precipitation in Europe, as our main purpose
has been to document changes in the tails of the distributions. This documentation of observed changes is
considered as essential background for future studies that aim to improve understanding of the causes of
changes in extremes and to relate these to changes in average values.
The temporal variability of temperature and precipitation in Europe is known to be largely influenced by
variability in the atmospheric circulation. The relationships are particularly strong in the winter season, where
numerous studies have linked changes in the North Atlantic oscillation (NAO) and other circulation indices
to changes in temperature and precipitation in Europe (e.g. Hurrell, 1995; Marshall et al., 2001; Hurrell et al.,
2003). In summer, the relationships are weaker and other factors than circulation also play important roles.
Cloud amount, for example, has been found to be strongly correlated with summer temperatures at Stockholm
and Uppsala (in Sweden) back to 1780 (Moberg et al., 2003).
As regards relationships between circulation and the occurrence of climate extremes in Europe, some
recent studies provide an insight into the statistical relationships. Haylock and Goodess (2004) made efforts
to attribute variability in winter precipitation extremes indices across Europe to various atmospheric surface
and height variables. They used the same R90N and CDD indices as we did, but based their study on nearly
500 European stations for the period 1958–2000. Their results identified the NAO as an influential factor,
and that changes in the NAO had caused much of the observed trends in the two indices. Focusing instead on
the frequency of warm temperature extremes in summer and also cold extremes in winter, for seven stations
in a south-central European region during the 1901–98 period, Domonkos et al. (2003) observed significant
correlations with large-scale circulation types for both seasons, but the relationships were stronger in winter.
A clear link between the atmospheric circulation pattern in summer and the fluctuations of heat waves at the
single station Prague–Klementinum (in the Czech Republic) has also been observed for the 1901–97 period
(Kyselý, 2002).
Hurrell and Folland (2002) discussed a possible link between sea-level pressures (SLP) averaged over the
southern action centre for the NAO in summer (JJA) and summer precipitation in Europe. They showed an
SLP time series (1899–2001) for the northeast Atlantic and European region that features notably low values
in the 1920s and 1930s compared with the 1970s to 1990s. A trend calculated over 1921–99 would certainly
be positive for this series, i.e. indicating increased anticyclonic conditions over the period. This change is
well in line with our observed, albeit statistically insignificant, increase in the length of dry spells in European
summers for the 1921–99 period.
Regarding the wet extremes, the analyses by Klein Tank and Können (2003) showed increases during
1946–99 in all-Europe averages of wet extremes indices, although with weak spatial trend coherence. Evidence
of disproportionally large changes in extremes compared with annual totals was also found. Our results show
that the trend coherence tends to be stronger when the longer time period 1921–99 is considered, indicating a
Copyright  2005 Royal Meteorological Society
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A. MOBERG AND P. D. JONES
gradual change over the century. Climate model runs with increasing greenhouse gases clearly suggest that, on
a global scale, a warming world will see enhanced increases of the intensity of precipitation extremes compared
with the mean precipitation changes (Kharin and Zwiers, 2000; Semenov and Bengtsson, 2002; Hegerl et al.,
2004). Regional results for Europe (Semenov and Bengtsson, 2002) show an increased contribution of heavy
precipitation to the total annual amount and decreases in the annual number of wet days in the 21st century as
a response to a greenhouse-gas-induced warming. Their results for the 20th century are qualitatively similar,
but less significant, as the change is relatively small compared with decadal variability. We note that there is
weak evidence in our study, but nothing more, pointing in the expected direction of enhanced increase for
strong precipitation events in winter for central Europe during the 1921–99 period. There is more evidence
for longer dry spells, which is in line with the model results by Semenov and Bengtsson (2002).
More emphasis on the shorter period 1976–99 was given by Klein Tank et al. (2002a; Klein Tank and
Können, 2003) in their studies of the ECA series (with much better coverage over northern and southern
Europe than in our study). Both those investigations revealed evidence for asymmetric warming trends in
the late 20th century, with relatively more warming in the warm tail than in the cold tail of the temperature
distribution. Our study provides some further details to the asymmetric changes, in particular the different
sign of trends in the upper and lower tails of daily temperature maxima in summers during 1946–99.
Finally, for future studies where changes in climate extremes are analysed, it should be useful to design
the analyses so that information is obtained for different parts of the distribution of the variable studied.
This could be achieved either by studying a set of indices based on different percentiles, or by fitting a
distribution to the data (e.g. such as in Kharin and Zwiers (2000)). If comparative analyses of observations
and results from model integrations are made, then assessment can be made with different forcings to study
how well the observed changes can be simulated, and thus enhance the understanding of the underlying
physical mechanisms.
ACKNOWLEDGEMENTS
This study was made with financial support by the Office of Science, US Department of Energy, under grant
no. DE-FG02-98ER62601. It was also made as a part of the EU project EMULATE, contract no. EVK2-CT2002-00161. We thank Malcolm Haylock, Jucundus Jacobeit and Albert Klein Tank for critical comments on
an early version of this article.
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Copyright  2005 Royal Meteorological Society
Int. J. Climatol. 25: 1149–1171 (2005)

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Trends in indices for extremes in daily temperature and precipitation

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