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ORIGINAL ARTICLE
Andreas Pauling ÆJu
¨rg Luterbacher ÆCarlo Casty
Heinz Wanner
Five hundred years of gridded high-resolution precipitation
reconstructions over Europe and the connection to large-scale
circulation
Received: 6 July 2005 / Accepted: 17 October 2005 / Published online: 20 December 2005
Springer-Verlag 2005
Abstract We present seasonal precipitation reconstruc-
tions for European land areas (30Wto40E/30–71N;
given on a 0.5·0.5resolved grid) covering the period
1500–1900 together with gridded reanalysis from 1901 to
2000 (Mitchell and Jones 2005). Principal component
regression techniques were applied to develop this dataset.
A large variety of long instrumental precipitation series,
precipitation indices based on documentary evidence and
natural proxies (tree-ring chronologies, ice cores, corals
and a speleothem) that are sensitive to precipitation sig-
nals were used as predictors. Transfer functions were de-
rived over the 1901–1983 calibration period and applied
to 1500–1900 in order to reconstruct the large-scale pre-
cipitation fields over Europe. The performance (quality
estimation based on unresolved variance within the cali-
bration period) of the reconstructions varies over centu-
ries, seasons and space. Highest reconstructive skill was
found for winter over central Europe and the Iberian
Peninsula. Precipitation variability over the last half
millennium reveals both large interannual and decadal
fluctuations. Applying running correlations, we found
major non-stationarities in the relation between large-
scale circulation and regional precipitation. For several
periods during the last 500 years, we identified key
atmospheric modes for southern Spain/northern Mor-
occo and central Europe as representations of two pre-
cipitation regimes. Using scaled composite analysis, we
show that precipitation extremes over central Europe and
southern Spain are linked to distinct pressure patterns.
Due to its high spatial and temporal resolution, this
dataset allows detailed studies of regional precipitation
variability for all seasons, impact studies on different time
and space scales, comparisons with high-resolution cli-
mate models as well as analysis of connections with re-
gional temperature reconstructions.
1 Introduction
To detect anthropogenic warming, knowledge concern-
ing the range of variability in hydroclimatic variables
such as seasonal precipitation and drought in past cen-
turies at regional and continental scale is important from
a societal as well as scientific point of view (e.g. Jones and
Mann 2004; Xoplaki et al. 2004; Touchan et al. 2003,
2005). Alongside temperature, precipitation is the key
climatic factor affecting human economies and terrestrial
ecosystems. Further, extreme precipitation events such as
floods and droughts have an essential influence on hu-
man life (e.g. Cook et al 2004; Wanner et al. 2004; Bar-
riendos 2005). Therefore, efforts should be made to
increase our understanding of long-term changes in
precipitation and drought patterns and their links to
controlling circulation influences (e.g. Casty et al. 2005a;
Hirschboeck 1988; Jacobeit et al. 2003; Jones and Mann
2004; Touchan et al. 2005; Wanner et al. 2004; Xoplaki
et al. 2000,2004). Also, the positive feedback mechanism
between increased temperatures during the last few dec-
ades and an enhanced water cycle could be regionally
studied on much longer timescales (IPCC 2001). Climatic
reconstructions covering the last few centuries, however,
require a dense network of suitable proxies. Europe is
one of the few regions with substantial coverage of long
instrumental records, documentary evidence and spatio-
temporally highly resolved natural proxies.
Electronic Supplementary Material Supplementary material is
available for this article at http://dx.doi.org/10.1007/s00382-005-
0090-8 and is accessible for authorized users.
A. Pauling (&)ÆJ. Luterbacher ÆH. Wanner
Institute of Geography, University of Bern,
Hallerstrasse 12, CH-3012 Bern, Switzerland
E-mail: pauling@giub.unibe.ch
Tel.: +41-31-6318868
Fax: +41-31-6318511
J. Luterbacher ÆH. Wanner
National Center of Competence in Research (NCCR) in Climate,
Erlachstrasse 9a, CH-3012 Bern, Switzerland
C. Casty
Climate and Environmental Physics, Physics Institute,
University of Bern, Sidlerstrasse 5, CH-3012 Bern, Switzerland
Climate Dynamics (2006) 26: 387–405
DOI 10.1007/s00382-005-0090-8
Up to the present, continuous European precipitation
reconstructions covering the last few centuries are only
available for various regions such as south-east Ger-
many (Wilson et al. 2005), southern Moravia, Czech
Republic (Bra
´zdil et al. 2002), Central Scandinavia
(Linderholm and Chen 2005), Iberian Peninsula (Barri-
endos and Rodrigo 2005), southern Spain (Rodrigo
et al. 1999,2001), northwestern Spain (Saz 2004),
southeastern Mediterranean (Touchan et al. 2003,2005),
the Alpine region (Casty et al. 2005a), parts of Tyrol,
Austria (Oberhuber and Kofler 2002), western France
(Masson-Delmotte et al. 2005; Raffalli-Delerce et al.
2004), Hungary (Ra
´cz 1999), Switzerland (Pfister 1999;
Gimmi et al. 2005), Germany (Glaser 2001), Sicily
(Diodato 2006), Morocco (Till and Guiot 1990), south-
ern Jordan (Touchan 1999), central Turkey (D’Arrigo
and Cullen 2001; Akkemik and Aras 2005), western
Black Sea region of Turkey (Akkemik et al. 2005) and
the Canary Islands (Garcı
´a-Herrera et al. 2003).
This study aims at providing temporally and spatially
highly resolved precipitation reconstructions hitherto
not available. The new independently reconstructed
precipitation fields will complement the existing recon-
structions of other climatic variables such as surface air
temperature (Luterbacher et al. 2004; Xoplaki et al.
2005), sea level pressure (SLP) and 500 hPa geopotential
height fields (Luterbacher et al. 2002a; Casty et al.
2005c) for the Eastern North Atlantic/European area
and will allow extensive analyses of European climate
over the last 500 years. For the first time it is possible to
perform such analyses on continental and regional
scales.
Using this new precipitation dataset we examine the
stationarity of the relationship of winter precipitation
and atmospheric modes. Further, we investigate large-
scale circulation that has led to extremely dry and ex-
tremely wet winters over Europe as well as over selected
subregions where skilful reconstructions are achieved
(see below).
This work is structured as follows: Sect. 2 describes
the predictor and predictand data. It also outlines the
reconstruction and verification methods. Additionally,
an overview of running correlations and the scaled
composite technique is given. Section 3 presents the
reconstructions and their quality. They are compared
with independent precipitation reconstructions and their
connection to large-scale circulation is investigated. In
Sect. 4 the results are discussed and in Sect. 5 conclu-
sions are provided.
2 Data and methods
2.1 Predictor data
Three data types are used as predictors: long quality-
checked instrumental precipitation series, precipitation
indices based on documentary evidence and natural
proxies (tree-ring chronologies, ice cores, corals and a
speleothem) resolving precipitation signals. There are a
few long instrumental precipitation series (e.g. Slonosky
2002; Wales-Smith 1971; Gimmi et al. 2005; Tabony
1981; Wigley et al. 1984; Camuffo 1984; Tarand 1993)
while a few documentary indices date back as far as
1500 (see Fig. 1, lower panel). Documentary evidence
comprises all non-instrumental man-made data on past
weather and climate as well as instrumental observa-
tions prior to the set-up of continuous meteorological
networks (e.g. Bra
´zdil et al. 2005 and references therein;
Pfister 2005; Przybylak et al. 2005). Non-instrumental
evidence is subdivided into descriptive documentary
data (including weather observations, e.g. reports from
chronicles, daily weather reports, travel diaries, ship
logbooks, etc.) and documentary proxy data (more
indirect evidence that reflects weather events or climatic
conditions such as the beginning of agricultural activi-
ties, religious ceremonies in favour of ending meteoro-
Fig. 1 Upper panel: spatial distribution and types of the proxies
used for the Europe-wide precipitation reconstruction. The
rectangles indicate the extent of the three regional reconstructions,
which are discussed in more detail in the text. Lower panel: the
temporal evolution of the number of proxies available for the
winter reconstructions
388 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
logical stress such as drought or wet conditions, etc.)
(e.g. Bra
´zdil et al. 2005 and references therein; Pfister
2005; Barriendos 2005). The first step in the procedure
to obtain indexed values is the evaluation of available
documentary data taking into consideration the critical
analysis of sources, author and/or institutional frame-
work, calibration of documentary proxy data, etc.
Useful values from documentary evidence are obtained
by transforming the basic data into simple and weighted
precipitation indices on an ordinal scale. Simple
monthly indices use a three-term classification (precipi-
tation: 1 wet, 0 normal, -1 dry with respect to a defined
reference period within the 20th century). Seasonal
indices are obtained by summation of monthly values.
Therefore, in simple indices the 3-month seasonal values
(e.g. DJF, MAM, JJA, and SON) can fluctuate from –3
(very dry) to 3 (very wet) (see Bra
´zdil et al. 2005 for a
detailed descripition). Series of indices obtained from
documentary evidence should overlap the period of
instrumental measurements. This is only possible for a
few cases and very distinct periods (e.g. Pauling et al.
2003). In order to extend the historical indices into the
20th century, measurements have been indexed using the
same numerical scheme (typically from -3 to +3) as the
historical documentary indices. However, these mea-
surement-derived indices from the 20th century proba-
bly overestimate the accuracy of the documentary series.
In order to deal with this issue, white noise has been
added to the measurement-derived indices. We have
chosen white noise since the inaccuracies of two inter-
annual seasonal documentary indices are believed to be
uncorrelated. This degrading procedure has also been
applied by Mann and Rutherford (2002), Pauling et al.
(2003), Xoplaki et al. (2005) and Luterbacher et al.
(2005) and is thought to model the inaccuracies present
in the documentary indices. To estimate the magnitude
of the added noise, an overlap period is needed. How-
ever, there are few examples where instrumental mea-
surements and documentary indices are available for the
same period (e.g. Bra
´zdil and Friedmannova
´1994 and
Bra
´zdil et al. 2003 both for the Czech Republic; Ra
´cz
1999 for Hungary; Rodrigo et al. 1999 for Spain).
Correlation coefficients between documentary indices
and measurements in these studies vary and are of the
order 0.5. The standard deviation of the added noise has
been chosen to ensure to same correlation as in these
studies.
Apart from documentary indices, natural proxies
have been used. The tree-ring chronologies consist of
networks that have been published for their seasonal
precipitation signal (for the number and location see
Fig. 1). However, some tree-rings are sensitive to winter
precipitation, others to spring or summer precipitation.
A list of all chronologies can be found in the electronic
supplementary material accompanying this work. From
this list it can also be seen for which season each chro-
nology has been used as predictor. The applied stan-
dardization procedure included the following steps: By
detrending and indexing (standardizing) the tree-ring
measurement series, chronologies have been produced.
Subsequently, a robust estimation of the mean value
function is applied to remove effects of endogenous stand
disturbances (ITRDB 2005). In many cases this has been
done using the program ARSTAN (Cook and Holmes
1986). Standardization has a large effect on the frequency
spectrum of the reconstructions. Here a procedure has
been used that stresses interannual to interdecadal vari-
ability. Thus, lower frequencies of the reconstructions
stem from other predictors than tree-rings.
Only those chronologies that start before 1750 and
end after 1982 have been used to ensure full coverage of
the calibration and verification period (1901–1983) as
well as parts of the reconstruction period (1500–1900).
The end date of the calibration (1983) has been chosen
as a result of many natural proxies that end in the early
1980s. Additionally, to select the chronologies contain-
ing the strongest regional precipitation signal, backward
elimination techniques (e.g. Luterbacher et al. 2005;
Pauling et al. 2003; Ryan 1997) have been applied to
these data. This method ranks the predictors according
to the proportion of the predictand’s variance they can
explain. Subsequently, we removed iteratively the least
important chronologies until the remaining chronologies
still explained 90% of the seasonal variance that the full
set of chronologies explained.
Four reconstructions have been performed (see rect-
angles in Fig. 1): one for European land areas (30Wto
40E/30–71N; hereafter ‘‘European reconstruction’’) as
well as three regional reconstructions as there are dif-
ferent precipitation regimes over Europe (e.g. Qian et al.
2000; Zveryaev 2004): central-eastern Europe (3–25E/
46–56N; hereafter CEE), France (5Wto3E/44–50N;
FRA) and the Iberian Peninsula including Morocco
(10Wto3E/30–44N; IPM). The advantage of the
regional reconstructions is the slightly higher recon-
structive skill. However, regional reconstructions are
only possible where sufficient meaningful local predic-
tors are available, as it is often impossible to rely on
precipitation signals from remote regions for precipita-
tion reconstructions. Therefore, we expanded the pre-
dictor sets for the regional reconstructions with further
chronologies from the International Tree-ring Data
Bank (ITRDB). From these chronologies only those
that fulfil the same length requirements as above and
correlate significantly with precipitation from the near-
est grid point over the 1901–1983 period have been
considered.
As the ice core records are not numerous, no rigorous
selection could be performed. The Crete and the GISP
ice core were selected because they have a high temporal
resolution and are publicly available. Both ice cores are
located on the ice divide of the Greenland ice cap. The
parameters include annual accumulation rates, seasonal
dHandd
18
O. Due to the high resolution of these ice
cores it was possible to distinguish between winter and
summer signals. The rationale for using these predictors
is the known teleconnections between European and
Greenland climate, especially during wintertime (e.g.
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 389
van Loon and Rogers 1978; Hurrell and van Loon
1997). These teleconnections are also confirmed by
correlations of 0.3 over the 1902–1990 period (signifi-
cant at the 95% level) between ice core parameters and
precipitation over various European regions (Spain,
Scotland and north-eastern Europe).
Additionally, d
18
O from the bimonthly-resolved Ras
Umm Sidd coral from the Red Sea (Felis et al. 2000) was
used. The high resolution of this coral allowed to extract
distinct seasonal climate signals. According to Felis et al.
(2000) variations of coral d
18
O reflects both sea surface
temperature and d
18
O of the seawater. They argue that
colder conditions over the Red Sea is connected to in-
creased rainfall in the southeastern Mediterranean basin
and obtained a correlation coefficient of 0.36 (significant
at the 99.5% level) between the mean annual coral d
18
O
record and precipitation from Cairo. Further, they also
report on oscillations in the coral d
18
O series that are
probably due to the North Atlantic Oscillation (NAO)/
Arctic Oscillation (AO) (Rimbu et al. 2001).
Finally, an annually dated speleothem from Scotland
(Proctor et al. 2000) was used. This speleothem has also
been shown to record precipitation throughout the year.
Proctor et al. (2000) obtained a clear negative correla-
tion of –0.53 over the 1879–1990 period (significant at
the 99.9% level) between decadal smoothed data of
bandwidth and annual local precipitation. According to
Proctor et al. (2000) the cave of that stalagmite is located
under a peat layer. They argue that CO
2
production in
the overlying peat is quicker in warmer and drier con-
ditions. The partial pressure of CO
2
in the drip water
also determines the Ca
2+
concentration, which in turn
determines the growth rate of the speleothem making it a
precipitation (and temperature) proxy. The annual dat-
ing was achieved through luminescent organic matter
that form annual bands (Proctor et al. 2000). A list of all
predictors can be found in the electronic supplementary
material.
2.2 Predictand data
The gridded precipitation field by Mitchell and Jones
(2005) was used as the dependent variable (predictand).
This dataset has been developed by interpolating many
instrumental precipitation station series onto a 0.5·0.5
grid. It covers all global land areas 1901–2002. The
values are expressed as anomalies from the 1961–1990
reference period. From this dataset we extracted the
predictand for both the European and the four regional
reconstructions.
2.3 Methods
2.3.1 Calibration
We aim at reconstructing gridded precipitation over
Europe through multivariate statistical climate fields
reconstruction (CFR) approaches (Jones and Mann
2004; Luterbacher et al. 2004; Bro
¨nnimann and Lut-
erbacher 2004; Casty et al. 2005a,c; Rutherford et al.
2005; Xoplaki et al. 2005; Mann et al. 2005). CFR seeks
to reconstruct large-scale climate patterns, such as pre-
cipitation, by assimilating a spatial network of proxy
indicators. This so-called ‘upscaling’ involves fitting
statistical models, which are mostly regression-based,
between the local proxy/instrumental data and the large-
scale climate fields/patterns. We perform a multivariate
calibration of the large-scale information in the proxy
data network against the available instrumental data
under the assumption of stationarity. Principal compo-
nent regression (PCR) has been used to perform the
reconstructions. We retained 70% of the predictor’s and
90% of the predictand’s variance. A description of this
technique can be found, e.g. in Jones and Mann (2004)
and Luterbacher et al. (2002a,2004).
First, all selected precipitation proxies in the Euro-
pean/North Atlantic region have been calibrated against a
European precipitation field to produce the European
reconstruction. Second, the three regional reconstruc-
tions were performed. For these regional reconstructions
only predictors within the predictand field were used since
remote predictors did not prove to be meaningful for
precipitation in these regions. Reconstructive skill of the
regional reconstructions is slightly higher compared with
the European reconstruction (Figs. 3,4). The 1901–1956
period has been used for calibration and the derived
models have been verified using the data of the 1957–1983
period. Data after 1983 could not be used as many proxy
series end at that time. Additionally, the calibration per-
iod could not be extended back into the 19th century as the
Mitchell and Jones (2005) dataset starts in 1901. There-
fore, the 1901–1983 period was used for calibration to
reconstruct precipitation back to 1500.
2.3.2 Verification
The skill of the reconstructions has been estimated using
the reduction of error (RE) measure (see review in Cook
et al. 1994). It is defined as:
RE ¼1P
n
i¼iðxi^
xiÞ2
P
n
i¼iðxi
xÞ2;
where x
i
are the observed values over the 1957–1983
verification period, ^
xiare the reconstructed values over
the 1957–1983 verification period and
xis the mean of
the observed values over the 1901–1956 calibration per-
iod.
RE values of 1 indicate a perfect reconstruction (no
difference between reconstruction and the predictand
during the verification period), a value of 0 means that
the reconstruction is as good as climatology (mean over
the verification period) and –1 is equivalent to a random
390 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
guess. Thus, RE values larger than 0 indicate recon-
structive skill. Due to the change of the predictors
through time, we had to calibrate and verify numerous
statistical models. For RE calculation of a certain
model, we performed calibration (1901–1956) and veri-
fication (1957–1983) during the 20th century using the
same predictors that are available for the period to be
reconstructed (e.g. Luterbacher et al. 1999,2002a,2004;
Xoplaki et al. 2005; Casty et al. 2005a). The assumption
is, that the calculated RE within the verification period is
also valid for the considered reconstruction period.
Additionally to this measure, the reconstructions were
compared with existing precipitation reconstructions
that are independent from ours for further verification.
It is important to note that statistical reconstructions
are always associated with uncertainties. In most cases,
predictors can only explain parts of the predictand’s
variance. The unexplained part is the main source of the
uncertainties in the reconstructions. Hence, we have
calculated error ranges based on the predictor’s variance
not captured by the model. Similar to Briffa et al. (2002)
we used ±2 standard errors to provide an estimate of the
uncertainties that are associated with the reconstruc-
tions. Thus, the uncertainty range of any reconstructed
value is given by the equation:
Uncertainty range ¼v2ffiffiffiffiffiffiffiffiffiffiffiffiffiffi
varunex
p:
Where mis any reconstructed value and var
unex
is the
unexplained variance of a specific statistical model.
According to Briffa et al. (2002) the uncertainty ranges
are in fact timescale-dependent. The uncertainties pre-
sented here are only valid for individual values (i.e.
seasons) rather than decadal or lower frequencies. Those
uncertainties, however, do not take into account
uncertainties in the instrumental precipitation data
against which the multiproxy data are calibrated, nor
the possibility of additional uncertainties in the proxy
data prior to the calibration/verification periods (e.g.
Jones and Mann 2004). The former are generally negli-
gible compared to other contributions to uncertainty
(e.g. Folland et al. 2001). The latter contribution may in
some cases be large, suggesting circumspect use. In the
case of tree-ring series that represent a composite of a
decreasing number of contributing trees in the chro-
nologies back in time or of documentary proxy data
when earlier information is less reliable than the
instrumental data (Jones and Mann 2004).
2.3.3 Running correlations
To investigate the stability of relations of climatic
variables, running correlations are often used in climate
research (e.g. Casty et al. 2005a; Gershunov et al. 2001;
Jacobeit et al. 2001,2003; Jones et al. 2003; Luterbacher
et al. 1999; Schmutz et al. 2000; Slonosky and Yiou
2002; Slonosky et al. 2001b; Timm et al. 2004; Touchan
et al. 2005). This technique includes calculation of cor-
relation coefficients using a time window that is moved
by one time unit over the whole time series. A 30-year
running correlation analysis (ranging from 1500 to
2000) is performed to examine the relationship between
winter precipitation averaged over the regions southern
Spain/Morocco and Central Europe and large-scale
atmospheric modes. These modes are defined as the
three leading empirical orthogonal functions (EOFs)
derived from a pressure dataset that has been recon-
structed as described by Luterbacher et al. (2002a). The
used version of the pressure reconstructions, however,
rely solely on pressure and temperature information as
predictors. Thus, they are independent from the pre-
cipitation reconstructions presented in this study (i.e.
they share no common predictors).
There are small lag-one autocorrelations in the pre-
cipitation data. Gershunov et al. (2001) state that
autocorrelations have to be accounted for when calcu-
lating significance thresholds. Therefore, to obtain a
conservative estimate of significant correlations, we
estimated 95% confidence levels using Monte-Carlo
simulations. Thousand random time series having the
same standard deviation, mean and lag-one autocorre-
lation coefficients as the original data are computed and
then correlated (Wilks 1995).
2.3.4 Scaled composites
We have also used scaled composite analysis (Brown and
Hall 1999; Touchan et al. 2005) to explore the rela-
tionship between precipitation extremes and the associ-
ated atmospheric patterns. Conventional composite
analysis includes averaging a subset of the data, com-
monly extremes. According to Brown and Hall (1999)
these analyses have at least two shortcomings: first, the
mean is strongly influenced by outliers especially in
small samples and second, it does not account for the
associated variance. Therefore, we used scaled compos-
ites, which do not have these disadvantages as they are
scaled to the square root of the sample size divided by
the standard deviation. This method can also be applied
to data that are not normally distributed (Brown and
Hall 1999). The significance of the anomalies is assessed
by modified t-values that account for the non-Gaussian
distribution of the data (Brown and Hall 1999).
The scaled composites are based on the 5% wettest
(25 cases) and the 5% driest (25 cases) winters (DJF) of
the period 1500–2000. For these extreme winters we
calculated the associated SLP anomalies and compared
them with the scaled composites.
3 Results
3.1 Precipitation reconstructions
Figure 2shows the spatially averaged time series of the
European reconstruction. This provides an overview of
the seasonal precipitation evolution over the last
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 391
a
b
c
d
Fig. 2 Spatially averaged time series and uncertainty estimates of the
European reconstruction. They are the average of the field 30Wto
40E/30–71N (5,791 land grid points). To the original reconstruction
(blue curve) and its uncertainties (red curves) a 30-year-Gauss-filter is
applied to stress the interdecadal fluctuations. The smoothedversion is
no new reconstruction and therefore the smoothed uncertainties can
merely be interpreted as the general course of the interannual
uncertainties and do not provide uncertainty estimates on decadal
timescales (Sect. 2.3.2). a=winter (DJF), b=spring (MAM), c=sum-
mer (JJA), and d=autumn (SON)
392 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
500 years. However, we have also performed detailed
spatial analysis of the precipitation history (Sect. 3.4.2).
Beside trend analyses, additional time series of the
regional reconstructions covering all seasons are pro-
vided in the electronic supplementary material.
The winter (DJF) reconstruction (Fig. 2a) is rather
stable from 1500 to 1700. Thereafter, interdecadal vari-
ability increases with a sharp rise during approximately
1705–1720. This is followed by a pronounced decline
during the following two decades to the lowest overall
values. A second positive peak is reached around 1770.
During the 19th century a slow decrease can be observed
and the 20th century is characterized by a positive trend.
The wettest winter on record is 1720 when precipitation
reached around 200 mm. The dry counterpart occurred
in 1744 with only 124 mm of precipitation. During the
period 1500–1700, the unfiltered uncertainty amounts to
approximately 35 mm. Afterwards, a gradual decrease
can be observed to values of 25–30 mm in the 20th
century.
Spring (MAM) precipitation (Fig. 2b) shows a steady
increase from 1540 to around 1620. Thereafter, multi-
decadal variability increases until 1700. The first half of
the 18th century is characterized by rather stable but
above average precipitation levels. This is followed by
a decline until 1800. The following 180 years show a
gradual increase with a short period of rather low pre-
cipitation around 1950. Spring precipitation extremes
occur during the second half of the 17th century: The
year 1686 is the driest (118 mm) and 1693 the wettest
(163 mm) spring on record. During 1500–1750 the
uncertainty amounts to approximately 35 mm. After
1750 the uncertainty considerably decreases to values of
around 25 mm. In the course of the 19th century the
uncertainty reaches the same level as during the 20th
century (20 mm) with similar variability.
Summer (JJA) precipitation (Fig. 2c) from 1500 to
1660 is characterized by a gradual increase. During the
following 100 years, high decadal variability can be
observed. From 1800 to 1983 variability decreases with
Fig. 3 Spatial distribution of the RE values for the European winter reconstruction for (a) 16th century, (b) 17th century, (c) 18th century
and (d) 19th century
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 393
no overall trends. Extremely dry summers occur in 1666
(142 mm) and 1669 (137 mm) while 1663 (204 mm) is the
wettest summer on record. The uncertainties remain the
same from 16th to 18th century (40 mm) but decrease
slightly after 1800 (35 mm) and reach values around
25 mm in 1900.
Autumn (SON) precipitation reveals no decadal vari-
ability and no trend from 1500 to around 1650. Thereaf-
ter, strong decadal fluctuations can be observed. Around
1800, however, variability is dominated by high frequen-
cies and again reveals no obvious trend. The wettest au-
tumn on record is reached in 1664 (217 mm), followed by
the driest one in 1669 (137 mm). The uncertainty of this
reconstruction remains rather stable until 1750 (40 mm)
when it gradually decreases until 1860 when it reaches the
level of the 20th century (20 mm).
The CEE reconstruction shows similar features as the
European reconstruction. Together with the FRA and
IPM reconstructions it is available in the electronic
supplementary material.
3.2 Performance of the reconstructions
Figure 3presents the RE values from the European
reconstruction averaged over each century for winter.
Similar figures for the other seasons can be found in the
electronic supplementary material including analyses on
the reconstructive skill of instrumental measurements
relative to proxy data. In general, an increase from the
16th to the 19th century can be observed. This coincides
with a steady increase of the number of available pre-
dictor information (Fig. 1). However, already in the
16th century there are regions with REs over 0.6 while
neighbouring regions show no skill. During the 16th and
17th century regions with positive RE values for winter
precipitation include central and eastern Europe, parts
of the Iberian Peninsula and adjacent Morocco. During
the 18th century RE values are above zero over western
Europe including northern Morocco, parts of Scandi-
navia and eastern Europe. During the 19th century this
pattern stays the same with RE values generally above
the level of the 18th century.
Figure 4shows the spatial distribution of the RE
values of the CEE reconstruction for winter. During
the 16th and 17th century reconstructive skill varies
substantially within the study area. RE values range
from below -0.2 to over 0.6 on relatively small spatial
scales. In the 18th century most of western Europe and
large parts of eastern Europe exhibit high reconstruc-
tive skill and during the 19th century RE values are
over 0.6 in many parts of Europe. Compared with
Fig. 3those values are slightly higher. That feature is
even more pronounced for the FRA reconstruction (see
electronic supplementary material). RE maps for
spring, summer and autumn reconstructions are also
available from the electronic supplementary material.
Generally, these reconstructions show somewhat lower
Fig. 4 As Fig. 3, but for the CEE reconstruction
394 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
RE values but the pattern is very similar to the one of
winter.
3.3 Comparison with other reconstructions
For further validation of our reconstructions we com-
pared them with a few selected regional precipitation
reconstructions independent from ours. Wilson et al.
(2005) reconstructed spring/summer precipitation based
on tree-rings of the Bavarian Forest region. In Fig. 5a
this series and the average of the corresponding nine grid
points from the CEE reconstruction are displayed (also
spring/summer mean). Correlations are highly signifi-
cant for all centuries (Spearman’s rho
critical, a=5%
=0.19,
based on a two sided t-test). Although, there are no
common predictors, the high correlation can at least
partly be explained by the significant correlation of the
chronology by Wilson et al. (2005) and the western
Austrian network (Oberhuber and Kofler 2002; Strumia
et al. 1997), which has been employed in our recon-
struction. There is also less low-frequency variability in
the Wilson et al. (2005) record than in our recon-
struction. This may be due to the standardization of the
tree-ring data in the Wilson et al. (2005) reconstruction.
These results confirm that both reconstructions capture
the same precipitation signal over the Bavarian Forest
region.
Figure 5b shows the spring/summer precipitation
reconstruction from the Czech Lands by Bra
´zdil et al.
(2002) together with the corresponding time series of our
CEE reconstruction. The reconstructions are indepen-
dent, i.e. they share no common predictors. The corre-
lations are positive for all centuries although the two
reconstructions do not agree in the low-frequency do-
main. This can be attributed at least partly to the stan-
dardization procedure of the tree-ring data and to the
fact that tree-ring data often carry an annual temperature
signal in their low-frequency domain (Frank and Esper
2005). The slightly lower correlation in the 19th century
may be related to the low number of samples that are
used in the development of this tree-ring chronology.
a
b
c
Fig. 5 Comparison of subregions of the CEE reconstruction with
other independent reconstructions. Panel a: time series (spring/
summer mean) of the reconstruction by Wilson et al. (2005;red)
and the CEE reconstruction (blue) of the same region and season.
Both time series are averages of the region 12.25–13.25E/48.75–
49.75N. The black curve indicates the time series from the Mitchell
and Jones (2005) data averaged over the same area. The indicated
correlation coefficients are Spearman’s rho and are calculated for
one century. Panel b: as panel a, but for the Bra
´zdil et al. (2002)
reconstruction (Czech Lands). The area is given by 15.25–17.25E/
48.75–49.75N. Panel c: as panel a, but for the Linderholm and
Chen (2005) reconstruction (central Scandinavia). These curves are
filtered using a 5-year-Gaussian filter. The time series are an
average over the area 13–16E/62–64N
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 395
Bra
´zdil et al. (2002) state that after 1956 correlations
of their reconstruction with precipitation measurements
drop to non-significant levels. So far, no satisfactory
explanation can be given for this phenomenon (Bra
´zdil
et al. 2002) although it could be related to increased air
pollution. Bra
´zdil et al. (2002) further suggest that a
combination of extremely dry years and air pollution
(mainly NO
x
,SO
2
and ozone) may disturb the climatic
signal in tree-rings.
In a recent study, Linderholm and Chen (2005)
reconstructed winter/spring precipitation in central
Scandinavia using tree-ring data. Figure 5c depicts a
comparison of this reconstruction with the spring series
averaged over 13–16E/62–64N from our European
reconstruction. In this area the chronologies used in the
Linderholm and Chen (2005) study are located. Both
series are filtered with a 5-year Gaussian filter. There is
no significant overall correlation and the correlations
change sign. Various reasons are possible for these con-
flicting results. A climate signal derived from tree-rings in
that area is normally indicative of summer temperature
(e.g. Briffa et al. 2001) and not precipitation. Addition-
ally, according to Linderholm and Chen (2005) the
strongest precipitation signal of tree-rings is September–
April while we analyse March–May precipitation. We
use only spring precipitation because reconstructive skill
for winter in that region is very low. However, Linder-
holm and Chen (2005) find significant correlations with
precipitation using 5-year averages and argue that tree-
rings may contain information on winter precipitation
variability via complex interactions between winter snow
amount, ground frost and rain. Nonetheless, our recon-
struction correlates highly significant (r=0.73) with the
Mitchell and Jones (2005) data for the same region and
season while the Linderholm and Chen (2005) recon-
struction and the Mitchell and Jones (2005) data disagree
(Fig. 5c). It can be argued that this may be partly due to
the fact that the climatic response in the tree-rings is best
for September–April and not March–May. Still, during
some periods the long-term fluctuations of the two curves
is rather consistent.
3.4 Precipitation and large-scale circulation
3.4.1 Stability of the relation between precipitation
and large-scale circulation
We restricted the running correlation analysis to winter,
as during that season the dynamical relationship be-
tween precipitation and pressure is best pronounced in
the northern extratropics. Figure 6a depicts 30-year
running correlations of winter (DJF) precipitation
averaged over Southern Spain and Morocco (10–0W/
33–40N; see rectangles in Fig. 7) with the first three
principal components (PC) of North Atlantic/European
SLP reconstructions (see Fig. 7). We selected that region
because its precipitation is known to be sensitive to the
NAO (e.g. von Storch et al. 1993; Zorita et al. 1992;
Hurrell and Loon 1997; Rodriguez-Puebla et al. 2001;
Knippertz et al. 2003). These winter pressure recon-
structions are based on temperature and pressure
Fig. 6 Thirty-year running correlations of the first three principal
components (PC) of winter (DJF) SLP reconstructions by
Luterbacher et al. (2002a; calculated using no precipitation
predictors) with winter (DJF) precipitation (a) averaged over
Southern Spain and Morocco (see corresponding rectangle in
Fig. 7) and (b) averaged over parts of central Europe (see
corresponding rectangle in Fig. 7). The dashed lines denote the
5% significance threshold based on 1,000 Monte-Carlo simulations
396 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
predictors (no precipitation predictors are included to
ensure independence) and are described by Luterbacher
et al. (2002a). Luterbacher et al. (2002a) noted that
seasonal precipitation from Andalusia (Rodrigo et al.
1999,2001) is a crucial predictor for skilful SLP recon-
structions in southwestern Europe. Despite a few addi-
tional predictors, the exclusion of Andalusian
precipitation in the independent reconstruction leads to
lower skill, though still reliable SLP reconstructions
within the 16th and 17th centuries.
EOF 1 (50.0% of explained variance, see Fig. 7a) is
the most important pattern to explain winter precipita-
tion variability over Southern Spain and Morocco during
the late 17th century as well as during most of the 19th
and 20th century. Correlations of the first PC with pre-
cipitation during these periods are negative and highly
significant (see Fig. 6a). EOF 1 (Fig. 7a) is characterized
by one centre over the western Mediterranean and the
adjacent Atlantic and another centre over the North
Atlantic around Iceland. This pattern resembles the
NAO. Positive values of PC 1 are connected with a
strong Azores high, covering Spain/Morocco. Westerlies
are located further north. In its negative state (negative
‘NAO’) rather meridional circulation prevails which is
connected with positive precipitation anomalies over
southern Spain/Morocco.
Significant positive correlations of PC 2 (EOF 2 ex-
plains 23.9% of the total pressure variance, see Fig. 7b)
with precipitation over Southern Spain and Morocco are
found (see Fig. 6a). They occur periodically on quasi-
centennial time scales. The spatial pattern of EOF 2 is
featured by one centre of action west of Ireland. In its
positive state the westerlies reach as far south as
southern Spain/Morocco. They are connected with
anomalous advection of moisture from the Atlantic
Ocean to the European continent. Towards the end of
the 17th, 18th, 19th and 20th century highly significant
positive correlations occur while correlations occasion-
ally drop to zero between these periods.
PC 3 (EOF 3 explains 16.1% of the SLP variance, see
Fig. 7c) reveals highly significant negative correlations
during the first half of the 18th century (see Fig. 6a). As
seen from Fig. 7c, EOF 3 is associated with a ‘blocking’
pattern over the Baltic Sea and eastern Europe.
Interestingly, during the 18th century all three pat-
terns sequentially dominate winter precipitation vari-
ability over Southern Spain and Morocco: EOF 1 is the
most important pattern for precipitation variability at
the end of the 18th century but correlations decrease to
marginally significant values towards 1770. Meanwhile,
correlations of PC 2 with precipitation increase. Around
1770, PC 2 is highly positively correlated with precipi-
tation. Towards the first half of the 17th century, how-
ever, PC 3 played a key role in explaining precipitation
variability over southern Spain and Morocco. Prior to
1650 most correlations are not significant, this might be
partly be due to lower SLP reconstruction quality rather
than a climatic signal.
Fig. 6b displays 30-year running correlations of
winter (DJF) precipitation averaged over parts of central
Europe (5–10E/48–50N; see rectangles in Fig. 7) and
the time series of the three most important atmospheric
patterns (Fig. 7). We chose that NAO-insensitive region
a
b
c
Fig. 7 First three EOFs of winter (DJF) SLP reconstructions
1500–2002 of the Luterbacher et al. (2002; version including no
precipitation predictors). Contours represent normalized loadings.
The percentages refer to the proportion of explained variance of
each EOF
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 397
to contrast with the NAO-sensitive region of southern
Spain/Morocco (e.g. Knippertz et al. 2003; Hurrell and
Loon 1997; von Storch et al, 1993; Zorita et al. 1992).
For this area, PC 3 is clearly correlated best with pre-
cipitation containing highly significant negative corre-
lations over almost the whole period.
Similar to the correlations in Fig. 6a, PC 2 is posi-
tively correlated during the late 16th century, around
1650 and 1750 and during the period 1850–1950.
PC 1 is rarely significantly correlated with precipita-
tion averaged over this study area and correlation sign
changes frequently over the last 500 years. This suggests
that it is of minor importance for precipitation vari-
ability over central Europe.
To sum up, correlations of the PCs with precipitation
over southern Spain/Morocco and central Europe vary
during the last 500 years on decadal timescales. PC 1 is
the most important pattern for precipitation over
southern Spain/Morocco. PC 2 is only relevant during
some periods. Interestingly, PC 3 was important during
the first part of the 18th century. Regarding central
Europe PC 3 dominates precipitation throughout the
last 500 years. The two other patterns are less impor-
tant. However, PC 2 also correlates highly significantly
with central European precipitation during several
periods.
3.4.2 Relationship of precipitation extremes
and large-scale circulation
To investigate the relationship between winter precipi-
tation extremes (here defined as the 5% wettest or driest
winters of the 1500–2000 period) and atmospheric
circulation we calculated scaled composites (cf. Sect.
2.3.4) and the associated SLP anomaly patterns.
Figure 8displays scaled composite anomaly maps with
corresponding pressure anomalies for the 5% wettest
(Fig. 8a, b) and driest winters (Fig. 8c, d) over southern
Spain/northern Morocco (region is marked by black
rectangles in Fig. 8) over the last 500 years. Hence, both
scaled anomaly maps are based on 25 winters. The wet
pattern (Fig. 8a) reveals significant negative precipita-
tion anomalies over large parts of the Mediterranean
basin, while significant positive anomalies occur over
Scotland, Ireland, Norway and over the region extend-
ing from northern France to Finland. The associated
independent pressure anomaly pattern (Fig. 8b) shows a
ab
d
c
Fig. 8 Scaled anomaly composites of winter (DJF) precipitation
(a,c) and corresponding pressure anomalies (b,d; in hPa; relative
to 1901–2000). The composite precipitation maps are based on the
5% wettest (panel a) and driest winters (panel c) over southern
Spain/northern Morocco (black rectangle) of the period 1500–2000.
Units are dimensionless. The significance thresholds (±1.916) are
based on a modified t-test (Brown and Hall 1999)
398 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
dominating negative centre over northern Spain and the
adjacent Atlantic Ocean while positive pressure anom-
alies occur over Iceland.
Figure 8c shows the composite for the 5% driest
winters. It is largely the opposite of the pattern in
Fig. 8a: significant dry anomalies occur over the north-
ern parts of the Mediterranean basin, while Scotland
and large parts of Scandinavia experience wet condi-
tions. Over parts of central Europe no significant
anomalies can be observed. The associated pressure
anomalies (Fig. 8d) features largely the opposite pattern
of the wet anomalies with a positive centre over western
France and the adjacent Atlantic Ocean.
Figure 9a depicts scaled composite maps based on
the 5% wettest (driest) winters over central Europe
(black rectangle) over the period 1500–2000. Again, each
map is based on 25 winters. Almost all areas show
significant anomalies: while over Europe (excluding
western Scandinavia) very wet conditions prevail, sig-
nificant dry anomalies can be observed over north
Africa, Iceland and Norway. The associated anomaly
pressure pattern (Fig. 9b) is characterized by negative
SLP anomalies over the North Sea and positive SLP
departures over Iceland/Greenland.
In the case of very dry conditions over central Europe
(Fig. 9c) the opposite pattern dominates: Significant dry
conditions over most parts of Europe and significant
positive anomalies over Greenland/Iceland, Norway and
north Africa including southern parts of the western
Mediterranean. The corresponding SLP pattern
(Fig. 9d) depicts a strong positive anomaly over the
British Isles. A negative centre is located west of the
North African coast.
To sum up, winter precipitation anomalies over
southern Spain/Morocco are associated with pressure
anomalies centred over the Atlantic Ocean northwest of
the Iberian Peninsula. Further, these anomalies coincide
with anomalies over most parts of the Mediterranean,
Scotland and Scandinavia. Central European winter
precipitation extremes are connected with pressure over
the North Sea and the British Isles. They also show
significant connections to precipitation over Scandinavia
and the Mediterranean.
4 Discussion
4.1 Reconstructive skill
The reconstructive skill for gridded precipitation of the
17th century remains at the same level as during the 16th
century in most regions but increases substantially dur-
ab
d
c
Fig. 9 As Fig. 8, but for central Europe
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 399
ing the 18th and 19th century for the European recon-
structions (Fig. 3). A reason for this is the number, type,
quality and the location of predictors that have become
available towards the present time. The long instru-
mental series start during the 18th century when also
more tree-ring chronologies become available (the list of
predictors can be found in the electronic supplementary
material). Additionally, documentary precipitation
indices are not available continuously throughout the
whole reconstruction period back to 1500. An example is
the sharp increase in 1675 and decrease in 1715 of the
RE values over southern Spain/Morocco (not shown).
This fluctuation coincides with the availability of docu-
mentary indices from several Spanish cities during that
period (Martin-Vide and Barriendos 1995; Barriendos
1997,2005; Rodrigo et al. 1999,2001).
The regions with high reconstructive skill are also
linked to the location of the proxies. In central Europe
documentary indices are available (Pfister 1999;Ra
´cz
1999), whereas in eastern Europe precipitation-sensitive
tree-rings exist and for Spain and Morocco both tree-rings
and documentary indices can be found. Given the high
spatial variability of precipitation it is not surprising that
reconstructive skill varies substantially in space. Hence, it
is difficult to skilfully reconstruct precipitation in areas
where no predictor information is available due to a lack
of precipitation signals of remote regions for such regions.
For instance, there are no long predictors in Italy, which
may be the reason for the poor reconstructive skill over
that region (see Fig. 3). This highlights the need for well-
distributed predictors resolving the precipitation signal at
seasonal scale (e.g. Luterbacher et al. 2005).
The three regional reconstructions reveal slightly
higher RE values than the corresponding REs of the
European reconstruction (see Fig. 4and the electronic
supplementary material). This shows that by calibrating
regional predictors against a regional field there is a
potential for improving the reconstructions. However,
this is only feasible if suitable predictors are available in
the region, and in many cases it has been proven to be
impossible to rely on teleconnections. Luterbacher et al.
(2002a,2004) reported skilful SLP and temperature
reconstructions for Europe using remote predictors.
However, this study suggests that local predictors are
essential in many cases with precipitation as the target
variable. An exception may be southern Spain where the
European reconstruction (Fig. 3) reveales higher RE
values than the IPM reconstruction (see the electronic
supplementary material) that was produced using
exclusively local predictors. This can be explained with
the high precipitation sensitivity of that region to the
NAO. Proxies that record NAO variability but are
remote to southern Spain can still be meaningful for
precipitation reconstructions in that area. Hence, in this
case it is recommended not only to use local predictors
due to possible teleconnections.
As seen in Fig. 4, the RE values over Poland and the
eastern part of Germany are below zero for all centuries
although there are tree-ring predictors available and
documentary indices are located in Germany and the
Czech Lands. This may be related to features in the
Mitchell and Jones (2005) dataset. New et al. (2000)
report that there may be inhomogeneities in the station
observations that were used to develop the Mitchell and
Jones (2005) dataset. Further, before World War II the
Mitchell and Jones (2005) dataset relies on only two
station observations for the whole of Poland (New et al.
2000). According to Dai et al. (1997) two annual pre-
cipitation stations have only 50% of the variance in
common when located 80 km apart. During summer this
distance is smaller given the mainly convective nature of
precipitation. Especially in summer there are grid points
over Poland with very low RE values in the CEE
reconstruction (not shown). Hence, new stations in a
region the size of Poland can substantially alter grid
point values of the Mitchell and Jones (2005) dataset.
This may lead to underestimated RE values as the model
is fitted to a predictand field that was developed using
only two stations for the whole Poland (1901–1956).
When verification was performed during 1957–1983
many more stations were used to produce the predictand
field leading to different characteristics of the predict-
and. As the final models for the reconstruction are cal-
culated using the whole period 1901–1983 the quality of
the reconstructions may be higher than estimated by the
RE values. Supporting evidence of this interpretation
are the very low RE values in summer (<-0.2) as the
spatial precipitation variability is highest in this season
over this region.
Similar reasons could be important for the poor
reconstructive skill over Turkey, southern Italy and
southern France (see Fig. 3). In Turkey, for example,
there are only very few coastal station observations
available from the 1930s onward and none in the interior
of this country (New et al. 2000; Mitchell and Jones
2005; Turkes 1996,1998; Turkes and Erlat 2003;Tou-
chan et al. 2005; Xoplaki et al. 2004). A collection of
starting dates of Mediterranean precipitation measure-
ments can be found in Xoplaki et al. (2004).
The performance of the reconstruction over
some parts of the European Alps is also reduced. At
9E/46N, RE values are negative (Figs. 3,4). One may
argue that this is an area located on the south side of, or
within the Alpine mountain range, which thus has dif-
ferent precipitation characteristics than the northern
side of the Alps where the predictors are located. From
within the Alpine mountain range no predictors could be
used. Hence, well-distributed and dense predictor cov-
erage as well as observation stations are particularly
important in mountainous regions with a complex ter-
rain and different precipitation regimes. However, the
neighbouring region of the Valais shows high recon-
structive skill (RE>0.4, see Fig. 4). This may be
attributed to the relative dryness and low variability of
that region. Frei und Scha
¨r(1998) argue that the Valais
is subject to ‘‘inner-alpine shielding’’ which makes its
precipitation characteristics rather continental. Further
possible reasons for the high reconstructive skill include
400 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
the proximity of the Valais to the northern side of the
Alps whereas the Alpine range becomes much wider
towards the east.
Interestingly, there are very low RE values for the
region of the Czech Republic although a documentary
index is available for that area (see Fig. 4). The reason
for this apparent contradiction could be the discontin-
uous documentary record. Two thirds of the 16th cen-
tury values are missing and for the 17th and 18th
century no values are available at all. This contrasts with
the area of Hungary where a continuous documentary
record could be used. Over that area RE values range
from 0.1 to over 0.4 throughout the whole reconstruc-
tion period.
4.2 Precipitation and large-scale circulation
The varying correlations of the major atmospheric
patterns and precipitation over southern Spain and
Morocco as well as over central Europe indicate non-
stationarities in the European/North Atlantic climate
system (Fig. 6). Touchan et al. (2005) found non-sta-
tionary and insignificant relationships between major
European-scale circulation patterns and eastern Medi-
terranean late spring/summer precipitation over the last
237 years. Further, Jacobeit et al. (2003) report that the
long-term evolution of increasing anticyclonicity over
Europe during July has strengthened during the last
50 years, becoming recently a unique phenomenon
within the last centuries and causing an unprecedented
decline in central European July precipitation during the
second half of the 20th century. Casty et al. (2005b)
report on non-stationary behaviour of climate regimes
over the North Atlantic/European region in recon-
structed and modelled SLP data back to 1659. Hence,
these studies found major instationarities in European
climate as well.
Winter precipitation over southern Spain and
Morocco is believed to be mainly determined by the state
of the NAO (e.g. Knippertz et al. 2003; von Storch et al.
1993; Rodriguez-Puebla et al. 2001; Zorita et al. 1992),
whose spatial pattern is well represented by EOF 1 dis-
played in Fig. 7a. During the second part of the 17th
century, PC 1 is most significantly correlated with pre-
cipitation although PC 2 is important too (Fig. 6a). This
period of the Maunder Minimum seemed to be controlled
by the NAO, which often remained in its negative state
(Luterbacher et al. 1999,2001,2002b).
However, our results show that the connection of
precipitation to large-scale atmospheric circulation is
not stable over time at decadal timescales and suggest
that different patterns (not only the NAO) have played
a role in determining precipitation variability over
southern Spain and Morocco (Fig. 6a). This is also true
for the period around 1930 when correlations drop to
partly insignificant levels. Interestingly, PC 2 correlates
significantly with precipitation during that period which
suggests that EOF 2 (Fig. 7b) was equally important to
the NAO-pattern (Fig. 7a) in terms of explaining win-
ter precipitation variability over southern Spain and
Morocco.
EOF 3, a continental ‘blocking’, and its correspond-
ing PC time series influenced large parts of central
European climate during the Maunder Minimum (1645–
1715; Luterbacher et al. 2001). The significant correla-
tions of PC 3 with precipitation during the first half of the
18th century suggest that the influence of EOF 3 reached
as far southwest as southern Spain and Morocco.
The pre-1650 period is characterized by overall low
correlations (Fig. 6a). From a dynamical perspective
this is rather surprising, as winter precipitation has to be
linked to large-scale circulation. However, this feature
may be associated with decreasing reconstructive skill of
both the SLP and the precipitation reconstructions. In
the European reconstruction, the network of documen-
tary indices becomes much denser over Spain, Portugal
and Greece from 1675 to 1715 (Alcoforado et al. 2000;
Barriendos 1997; Martin-Vide and Barriendos 1995;
Rodrigo et al. 1999,2001; Xoplaki et al. 2001). Never-
theless, even the 16th century reconstructive skill was
estimated to be high over southern Spain and Morocco
(see Fig. 3). Concerning the SLP reconstructions, the
longest pressure series used by Luterbacher et al. (2002a)
starts in 1670 (Paris; Slonosky et al. 2001a). That time
approximately coincides with the drop of the correla-
tions. The sharp decrease of the correlation between the
PC 1 of SLP and winter precipitation during the early
18th century may also be partially related to predictor
availability as data of the Paris pressure series is missing
during July 1713 to December 1763. The same is
reported for the London series, which starts in 1697
(Slonosky et al. 2001a). For this series the gap includes
the years 1709–1773. However, new pressure series
become available from Uppsala (1722) and Basel (1744).
Hence, between 1713 and 1722 the pressure reconstruc-
tions are based solely on temperature information. To
summarize, the changes of the correlation coefficients up
to around 1750 may at least partly be related to pre-
dictor availability.
Figure 6b depicts the running correlations of winter
precipitation averaged over parts of central Europe and
the time series of the three most important patterns
(Fig. 7a–c). Correlation of central European precipita-
tion with the time series of PC 1 (or the NAO) is not
continuously significant for the last 500 years (Fig. 6b).
This agrees with findings by Hurrell and Loon (1997)
who found central European winter precipitation to be
rather insensitive to changes of the NAO during 1900–
1994. Our results suggest that this is true for the past
500 years. Casty et al. (2005a) calculated 31-year running
correlations of Alpine precipitation (December–March)
with the NAO back to 1659. They found significant
negative correlations during the periods 1860–1920 and
after 1950. However, before those periods, correlations
are hardly significant, which agrees with our results. At
the end of the 19th century, our correlations (Fig. 6b)
also show negative correlations, though hardly signifi-
Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions 401
cant. We attribute the different results to the different
areas that were analysed. We used a region further north
to the Alps (cf. Fig. 7) while Casty et al. (2005a) inves-
tigated the Alpine area including the south side of the
Alps where precipitation contains a Mediterranean sig-
nal of the NAO (Hurrell and Loon 1997).
The correlation of the time series of EOF 2 with winter
precipitation over central Europe (Fig. 6b) fluctuates on
multi-decadal timescales with highly significant correla-
tions around 1900, 1750 and 1650, and during the second
half of the 16th century. As seen from Fig. 7b the main
pressure centre of the associated pattern is located west
of Ireland. In case of high pressure the westerlies are
blocked and northerly relatively dry winds prevail. In
case of low pressure, mild air is advected from the
Atlantic and brings moisture to central Europe.
Over the whole 500 years, the PC 3 is correlated best
with precipitation. Significant negative correlations are
found over almost the whole period. This corroborates
the validity of both the SLP and the precipitation
reconstructions over Europe back to 1500. A pressure
centre over northeastern Europe characterizes the asso-
ciated EOF 3 pattern (Fig. 7c). In the case of positive
SLP anomaly, dry and cold conditions prevail over
central Europe. The negative state is associated with wet
conditions in the northern Alps. These situations are
known to be responsible for heavy winter snowfall over
that region resulting in positive glacier mass balance
(Wanner et al. 2000).
4.3 Relationship of precipitation extremes
and large-scale circulation
The scaled winter anomaly composites presented in
Fig. 8a, c show a typical NAO pattern (e.g. Hurrell and
Loon 1997; Wanner et al. 2001): wet (dry) conditions
over southwestern Europe coincide with dry (wet)
periods over northern Europe. Between these regions
possible anomalies cancelled while producing the com-
posite. The distinct patterns with highly significant
anomalies suggest that this precipitation distribution
during extreme winters re-occurred during the last
500 years. The negative pressure anomaly over northern
Spain and the adjacent Atlantic Ocean (Fig. 8b) facili-
tates advection of moist air from the Atlantic and the
overall influence of the low triggers precipitation over
southern Spain and Morocco. This is in line with the
reconstructed wet anomalies (Fig. 8a). In case of dry
periods (Fig. 8c), southern Spain and Morocco are
dominated by high pressure, which suppresses precipi-
tation. Anomalous easterly winds over southern Europe
prevent moist oceanic air from reaching southern Spain
and Morocco. Provided this pressure distribution, that
region is uncoupled from the westerlies that are located
further north and cause northern Europe to experience
very wet conditions.
Taking into account the anomaly patterns from cen-
tral Europe (Fig. 9a, b) it can be argued that low pressure
over the North Sea triggers precipitation over central and
eastern Europe. The Mediterranean and northern Africa
are relatively dry as lows infrequently reach that region.
The Scandinavian mountain range experiences dry
anomalies as well. This may be due to weakened
westerlies or even prevailing easterly winds. East of the
Scandinavian mountains these winds along with general
low pressure influence, cause wet conditions.
In case of dry anomalies over central Europe (Fig. 9c,
d) the opposite pattern dominates. High pressure over
the British Isles brings moist oceanic air to the Scandi-
navian mountain range while the remaining part of
Scandinavia is left dry. High pressure also impedes
precipitation over central and eastern Europe as well as
large parts of Spain and Turkey. Only parts of the
Mediterranean and North Africa have wetter conditions
than normal, which may be explained by low pressure
influence.
5 Conclusions and outlook
We present seasonally resolved gridded precipitation
reconstructions over the last 500 years for the European
region. This kind of dataset was hitherto not available.
It was developed using PCR techniques. Despite dis-
cussed constraints (e.g. predictor availability and re-
gional precipitation regimes) these precipitation
reconstructions provide a very useful basis on which past
precipitation variability can be analysed. They agree well
with independent reconstructions for selected areas.
Winter precipitation reconstructions showed highest
reconstructive skill compared to the other seasons.
Moreover, the quality of the reconstructions decreases
back in time. Still, various regions showed high recon-
structive skill back to 1500.
We demonstrated that there are major instationarities
in the relationship between precipitation over southern
Spain/Morocco and the three most important patterns
of atmospheric circulation. During the last 500 years
different patterns have been important to explain pre-
cipitation variability. The same conclusion can be drawn
for central Europe.
Using scaled composite analysis we showed that winter
precipitation extremes over southern Spain/northern
Morocco are connected to atmospheric pressure anoma-
lies over the Atlantic northwest of the Iberian Peninsula
and support recent findings, though for a longer time
period. For central European precipitation anomalies,
SLP over the North Sea and the British Isles are impor-
tant. Joint analysis of precipitation extremes of the last
500 years over these regions and pressure anomalies re-
vealed consistent patterns that can be synoptically well
interpreted. This further supports the validity of the pre-
cipitation reconstructions.
For the first time it is possible to perform detailed
precipitation studies on a regional and continental scale.
Apart from the presented analyses of the precipitation/
pressure stability, subsequent studies could involve tem-
402 Pauling et al.: Five hundred years of gridded high-resolution precipitation reconstructions
perature as well (Casty et al. 2005c). Further applications
of this new dataset include investigations of the causes of
historic glacier fluctuations. Using this dataset, Steiner
et al. (2005) reported that several combinations of sea-
sonal temperature and precipitation led to the advances
and retreats of European glaciers during the ‘Little Ice
Age’. Raible et al. (2005) compared both these recon-
structions and temperature with model simulations. Such
analyses can help improve our understanding of natural
and anthropogenic climate variability. Moreover, as
reconstructions are available for all seasons, studies will
now be possible dealing with seasonal climate change such
as seasonal shifts.
Acknowledgements This work is part of the EU-project SOAP:
simulations, observations and palaeoclimate (data: climate vari-
ability over the last 500 years) the Swiss part being funded by the
Staatssekretariat fu
¨r Bildung und Forschung (SBF) under contract
01.0560. Publication of this work was also supported by the
Marchese Francesco Medici del Vascello foundation. Ju
¨rg Lut-
erbacher is supported by the Swiss National Science foundation
through its National Center of Competence in Research in Climate
program, project PALVAREX. Carlo Casty is funded by the
European Commission under the Fifth Framework Programme
Contract Nr. EVR1-2002-000413, project PACLIVA. The pre-
dictand data has been kindly provided by the Climatic Research
Unit in Norwich, United Kingdom, and by the Tyndall Centre for
Climate Change Research. The authors also wish to thank two
anonymous reviewers for their helpful comments, Anita Orme for
English corrections and the following persons for access to their
data: Rudolf Bra
´zdil, Michael Grabner, Hans Linderholm, Walter
Oberhuber, Rob Wilson (all tree-ring data), Mariano Barriendos,
Rudolf Bra
´zdil, Ru
¨diger Glaser, Christian Pfister, Lajos Ra
´cz,
Fernando Rodrigo, Elena Xoplaki (all documentary data) and also
many data contributors to the World Data Center for Paleocli-
matology, Boulder, Colorado, USA.
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