Theor. Appl. Climatol. (2007)
DOI 10.1007/s00704-006-0275-z
Printed in The Netherlands
1
2
ARPA-SIM, Bologna, Italy
National Meteorological Administration, Bucharest, Romania
Climate change scenarios for surface temperature
in Emilia-Romagna (Italy) obtained using statistical
downscaling models
R. Tomozeiu1 , C. Cacciamani1 , V. Pavan1 , A. Morgillo1 , and A. Busuioc2
With 12 Figures
Received February 9, 2006; revised July 29, 2006; accepted August 25, 2006
Published online December 28, 2006 # Springer-Verlag 2007
Summary
Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A
statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the
Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on
Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height
at 500 hPa (Z500) and temperature at 850 hPa (T850), and
as predictands the seasonal mean values of minimum and
maximum surface temperature (Tmin and Tmax), 90th percentile of maximum temperature (Tmax90), 10th percentile
of minimum temperature (Tmin10), number of frost days
(Tnfd) and heat wave duration (HWD) at the station level.
First, the statistical model is optimised and calibrated using
NCEP=NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local
predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in
combination with MSLP, is an optimum predictor for winter Tmax90 and autumn Tmin10. Finally, MSLP is the best
predictor for spring Tmin while Z500 is the best predictor
for spring Tmax90 and heat wave duration index, except
during autumn. The ability of HadAM3P to simulate the
present day spatial and temporal variability of the chosen
predictors is tested using the control experiments. Finally,
the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2
scenario results at the local scale. Results show that significant increases can be expected to occur under scenario
conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and
with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario
than for the B2 scenario.
1. Introduction
Climate changes have a large impact on ecosystems, the environment and human activities. These
changes consist of shifts not only in mean values,
but also in the frequency and intensity of extreme
weather events. All these components must be
estimated in order to produce a complete evaluation of the impacts of climate change over a specific region.
During recent years, great attention has been
paid to the study of extreme weather events and
their impacts, especially after the occurrence of
several extreme events which affected large
regions, such as the summer floods of Central
Europe in 2002, and the intense climate anomaly
which affected almost the entire European continent in summer 2003, including both heat
waves and intense drought.
R. Tomozeiu et al.
A correct description of these events requires
information on the main surface fields at the local scale, which is also the scale at which input is
needed for impact studies. This is due to the fact
that the greatest impacts are often linked to the
occurrence of local phenomena, characterised by
very high spatial and temporal variability.
The best tools available from the climate
community to evaluate the pattern and intensity
of climate change are general circulation models
(GCMs). Given the current computational resources, the production of long (order 100 years)
integrations of these models makes it necessary
to run them at spatial resolution of the order of
100 km. Information at this scale can be used,
only to evaluate climate changes connected with
shifts in mean values over relatively large regions.
In order to evaluate changes at the local scale
and changes in the frequency of extremes, further efforts are needed to increase the spatial and
temporal resolution of the final prediction product. Dynamical, statistical–dynamical, and statistical downscaling methods are used to achieve
this objective.
Dynamical downscaling uses limited-area, highresolution models (regional climate models –
RCMs) driven by boundary conditions from a
GCM to derive local-scale information. RCMs
cover a selected region with limited spatial extent
at higher resolutions (up to 25 km). In the last
decade, RCMs have been used to reproduce the
European climate, both at present day (Giorgi
et al., 2004a) and in the future under different
possible scenarios (Christensen and Christensen,
2003; Giorgi et al., 2004b).
Statistical–dynamical downscaling uses observational data, to identify specific events representative of weather types characterising the climate
of a region; uses RCMs, to describe these events
and their possible impacts at the local scale, and
uses GCMs, to evaluate possible future changes
in the frequency of occurrence of these weather
types (Fuentes and Heimann, 2000).
Statistical downscaling (SD) is a complementary technique which develops statistical relationships that link large-scale atmospheric variables
(predictors) and local=regional climate variables
(predictands). This relationship is then applied
to the large-scale predictors simulated by GCMs,
under various emission scenarios, in order to obtain local climate-change information.
Some studies have compared the SD and RCM
approaches (Murphy, 2000; Huth et al., 2001;
Schmidli et al., 2006). The conclusion derived
from these studies is that SD and dynamical downscaling techniques are comparable for simulating
current climate but the climate change projections in some cases are quite different, especially
regarding the magnitude of the climate change
signal (temperature) or even in terms of the sign
of the climate signal (e.g. precipitation). Both approaches are currently widely used to produce
regional climate change scenarios, and the differences between them are used to better asses the
uncertainty associated with the use of these different techniques. Both approaches have advantages and disadvantages. The main advantage
of the dynamical approach is its physical basis,
even if there are some simplifications due to some
missing feedbacks. The main drawback is its
computational cost, which still substantially limits the possible RCM resolution and length of the
experiments produced, reducing the capability of
RCMs to capture the frequency and, to a greater
extent, the changes in frequency of extreme
weather events. In contrast, the main advantages
of SD techniques are that they are computationally inexpensive, can be used to derive variables
not available from RCMs, and allow the direct
downscaling of indices of frequency of extreme
weather events, even up to local scale.
The SD disadvantages refer to the fact that
they need long and homogeneous observational
time series for the fitting and validation of the
statistical relationship. There are different types
of statistical downscaling that can be grouped
in three categories: weather generators, weather
classification, and regression models. Weather
generators (Busuioc and von Storch, 2003; Katz
et al., 2003; Buishand et al., 2004) and SDs based
on weather classification (Zorita and von Storch,
1999; Palutikof et al., 2002) are focused on the
daily time scale. Regression models represent nonlinear or linear relationships between predictands
and large-scale predictors. The non-linear approaches include artificial neural networks, which
allow the fitting of a more general class of statistical model (Trigo and Palutikof, 2001; Cavazos
et al., 2002). Linear models are the most popular,
and include multiple regression (Palutikof et al.,
2002; Hellstr€om and Chen, 2003; Hanssen-Bauer
et al., 2003), models based on canonical correlation
Climate change scenarios for surface temperature in Emilia-Romagna
analysis (CCA) (Von Storch et al., 1993; Busuioc
et al., 2001; Hellstr€
om and Chen, 2003; Busuioc
et al., 2005) or singular value decomposition analysis (SVD) (Huth, 2002; Widmann et al., 2003).
A comparison between the three linear methods
(CCA, SVD and Multiple linear regression –
MLR) applied to eight winter daily temperature
at 39 stations in central parts of western Europe,
has been made by Huth (2002). One advantage of
CCA and SVD is that they seek pairs of patterns
that are optimally correlated making possible a
physical interpretation of the connection between
predictands and predictors. Because of this advantage this method is also used to validate GCM
output (e.g. Busuioc et al., 2001). The study also
reveals that CCA is a good method for regionalization while MLR provide good results
in the reproduction of time structure. Similar
studies of comparisons of results from different
downscaling techniques have been performed
in Scandinavia by Hanssen-Bauer et al. (2005).
They compared the above linear techniques and
concluded that overall no method is superior
as long as the information content in the predictors is similar. The choice of predictors, predictor domains and other strategic choices are
more critical for the results than the choice of
linear technique.
The development of statistical downscaling
methods, as well as the identification of the more
robust downscaling techniques, and their application in order to obtain scenarios of extremes and
mean values for different European regions was
one of the main objectives of the European project STARDEX (http:==www.cru.uea.ac.uk=cru=
projects=stardex=). STARDEX, together with
PRUDENCE and MICE, formed a cluster of
three projects, all related to changes in mean and
extremes of climate and their impacts in different
European areas.
In the present study, a statistical downscaling
method (SD) based on Canonical Correlation
Analysis (CCA) has been used in order to construct future scenarios of changes in seasonal
mean and extremes of minimum and maximum
temperature at 32 stations in Emilia-Romagna. In
order to build the model, that is to quantitatively
identify the relation between large-scale variability and local climate, the approach used in the
present paper is a ‘‘perfect – prog’’ approach,
that is, first, the statistical downscaling model
is built using observational data only, then, the
model is applied to the output of GCM experiments so as to reproduce local climate characteristics or to evaluate local future scenarios.
The models proposed here use a selection of
fields between mean sea level pressure (MSLP),
500 hPa geopotential height (Z500), and temperature at 850 hPa (T850) as predictors. The predictands are seasonally averaged minimum and
maximum air temperature (Tmin and Tmax, respectively), the 10th percentile of minimum air
temperature (Tmin10), the 90th percentile of maximum air temperature (Tmax90), the number of
frost days (Tnfd), and the heat wave duration
(HWD), all evaluated at the station level. The
influence of these predictors on the Italian climate during the winter season has been described
by Cacciamani et al. (1994), Pavan et al. (2005),
and Tomozeiu et al. (2005), and for summer by
Colacino and Conte (1995), while the present
study represents the first contribution on the
same issue with respect to the others seasons.
The main objective of this work is to produce
local climate change scenarios (period 2070–
2100) of mean and extreme air temperature in
Emilia-Romagna, using HadAM3P model simulations and statistical downscaling models. This
aim is achieved through the realization of the
following steps: (1) detect the connection between
large-scale circulation patterns derived from
NCEP=NCAR reanalysis and observed minimum=
maximum temperature in Emilia-Romagna; (2)
develop an optimal statistical downscaling model
based on the above relationship; (3) validate the
HadAM3P output in terms of its ability to reproduce the selected predictors and (4) construct
scenarios of extremes and mean temperature
based on optimal statistical downscaling models,
applied to HadAM3P output.
This paper is organized as follows: Section 2
presents data and methods used. The selection
of the predictors as well as the set-up of the SD
models using NCEP=NCAR reanalysis is presented in Sect. 3. The ability of the HadAM3P
model to simulate the predictors is presented in
Sect. 4. The robustness of the downscaling models is presented in Sect. 5. In Sect. 6, scenarios of
changes in mean and extreme temperature over
Emilia-Romagna for the period 2070–2100 compared to 1960–1990, are presented. The A2 and
B2 IPCC (Intergovernmental Panel on Climate
R. Tomozeiu et al.
Change) SRES (Special Report on Emissions
Scenarios) scenarios are considered. Conclusions
are presented in Sect. 7.
2. Data and method
2.1 Local scale data
The observational data set used in this study
consists of time series of daily minimum and
maximum temperature (Tmin and Tmax) at 40
stations located in Emilia-Romagna, a region of
Northern Italy. Figure 1 shows the map of the
region including orography (right) together with
its location within the Italian Peninsula (left).
Data have been collected by the offices of the
former Italian Hydrographic Service (recently incorporated by ARPA-SIM) at Bologna and Parma,
and cover the period 1958–2000. The data is
quality controlled (Pavan et al., 2003) and homogeneity tested (Tomozeiu et al., 2005). Taking into
account only the stations more than 80% complete and those that pass the homogeneity control, the number of stations for which extremes are
computed is reduced to 31 (Fig. 1, right). As can
be observed, the remaining stations are uniformly
distributed over the region. A set of six indices
the predictands are calculated at the seasonal
level using daily data: mean Tmax, 90th percentile
of maximum temperature (Tmax90), mean Tmin,
10th percentile of minimum temperature (Tmin10),
number of frost days (Tnfd) and heat wave duration index (HWD). The extreme indices are computed for each station and each season during the
period 1958–2000. Seasons are defined in the
standard format: Winter as December to February
(DJF); Spring as March to May (MAM); Summer
as June to August (JJA); Autumn as September
to November (SON). The number of frost days
(Tnfd) is defined as the number of days with
Tmin <0 C while the heat wave duration (HWD)
is defined as the maximum number of consecutive
days with Tmax greater than Tmax90. Spatial and
temporal variability of these observed indices
have been studied in a previous paper (Tomozeiu
et al., 2005).
2.2 Large scale data
The large-scale predictors include mean sea level
pressure (MSLP), geopotential height at 500 hPa
(Z500) and temperature at 850 hPa (T850), which
have been extracted from the NCEP=NCAR
re-analysis (Kalnay et al., 1996). This data set
refers to the monthly mean values of these variables at 2.5 2.5 horizontal resolution for the
window 90 W–90 E; 0 –90 N, and for the period 1958–2000.
The GCM simulations used are those produced
by the Hadley Centre using the HadAM3P model.
Fig. 1. Map of Italy indicating the position of Emilia-Romagna (left), the stations used in this study (marked by cicles) and
the orography of the Emilia-Romagna (shaded area) (right)
Climate change scenarios for surface temperature in Emilia-Romagna
They include present day (1960–1990) and A2
and B2 scenario experiments (2070–2100). The
HadAM3P is an atmosphere only general circulation model with 19 vertical levels and a horizontal resolution of 2.5 3.75 , comparable to a
spectral resolution of T42. The sea surface temperatures used to drive this model are obtained
from observations, for present day simulations,
and from the HadCM3 ocean-atmosphere coupled model, for scenario simulations. A detailed
description of the HadAM3P model and of the
experiments used in this paper is presented in
Pope et al. (2000), and Johns et al. (2003). The
daily Z500, MSLP, T850 data for the control run
and scenario experiments have been provided by
the STARDEX project at the same resolution as
NCEP=NCAR reanalysis (2.5 2.5 ). The model daily data were interpolated to 2.5 2.5
using the ‘‘bivar’’ method described by Akima
(1984).
2.3 The statistical downscaling (SD) method
A group of statistical models based on the
Canonical Correlation Analysis (CCA) have been
constructed in order to obtain future scenarios of
mean and extreme temperature at the local scale
in Emilia-Romagna using different subsets of all
possible predictors. The CCA method was first
introduced to climate research by Barnett and
Preisendorfer (1987). This method identifies predictor-predictand pairs of patterns, which maximise the correlation between two corresponding
patterns (Von Storch and Navarra, 1995). In addition, the method offers a physical interpretation
of the mechanism that controls the regional climate variability (Von Storch et al., 1993; Busuioc
et al., 2001). In order to reduce the noise of the
fields involved, before the CCA is applied, the
data sets are projected on EOFs (empirical orthogonal functions) and only those explaining the majority of the total observed variance are retained
(Von Storch and Navarra, 1995). A subset of CCA
pairs is then used in a multivariate linear model
in order to estimate the predictand anomalies
from the predictor anomaly field (Barnett and
Preisendorfer 1987; Von Storch et al., 1993;
Busuioc et al., 2001).
In the present paper, models are built for each
season and index, choosing each time a different
subset of predictors from the fields extracted
from the NCEP=NCAR reanalysis. All data are
de-trended before use. All models are calibrated
on the period 1958–1978 and 1994–2000 and validated on the period 1979–1993, and only the best
performing model is retained. The performance
(skill) of the downscaling model is quantified at
the station level in terms of: (i) Spearman rankcorrelation coefficient (CORR) which is just the
correlation coefficient calculated on the ranks of
the two time series, (ii) root-mean square-error
(RMSE) between observed and simulated index
with bias removed and (iii) BIAS, defined as
follows:
RMSE ¼
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u
u1 X
u
½indicesmod el ðiÞ indicesobs ðiÞ BIAS2
t N i 2 verification
period
ðiiÞ
BIAS ¼ hindicesmod el iverification
hindicesobs iverification
ðiiiÞ
The work done in order to select optimum statistical downscaling models, using predictors from
NCEP=NCAR reanalysis, shows that the skill of
the downscaling models is dependent on:
1) the predictors (large-scale field, single or
combined);
2) the domain (area) of predictors;
3) the number of EOFs retained for the CCA and
the number of CCA components used in the
regression model.
The selection of the predictors is done so as:
a) to have strong, robust and physically meaningful relationships with the predictands;
b) to have stable and stationary relationships with
the predictands;
Table 1a. Areas of definition of the predictors used within
the SD models
Code
Area
Area
Area
Area
Area
Area
Area (Long.=Lat.)
A
B
C
D
E
F
90 W–90 E=0 N–90 N
60 W–60 E=20 N–90 N
35 W–35 E=30 N–60 N
12.5 W–30 E=30 N–55 N
5 E–35 E=30 N–50 N
5 W–20 E=37.5 N–50 N
R. Tomozeiu et al.
Table 1b. Predictors used in the optimal SD model for each predictand and season
Predicatand
DJF
MAM
JJA
Tmax
Tmax90
Tmin
Tmin10
Fd
HWD
T850=area C
T850 þ MSLP=area B
T850=area C
T850=area C
T850=area C
Z500=area D
T850=area C
Z500=area F
MSLP=area D
T850=area C
T850=area C
Z500=area E
T850=area
T850=area
T850=area
T850=area
–
Z500=area
c) to be well reproduced by the HadAM3P
model;
d) the SD model is able to explain the observed
low-frequency variability and trends of the
predictands.
Points a) and b) above are achieved by applying
the CCA using different time period definitions
for the calibration period, in order to check the
robustness of the CCA results. The analysis done
shows that the application of the method using
calibration periods of either 1958–1980 or 1981–
2000 results in the identification of similar predictor-predictand patterns and in comparable
strength of predictor-predictand connections, suggesting that the predictor-predictand relationships described in the following are robust and
stationary.
Finally, in all cases, the ability of the
HadAM3P model to reproduce the observed
variability of the predictors is checked by comparing the model EOFs with re analysis EDFs for
each predictor field.
The predictor definitions can be altered either
by changing the large-scale field used or the
domain used as space-window in the PC filter
(Benestad, 2001). In order to maximise the performance of the statistical model, several possible domain extensions have been examined in the
predictors definition, for each season and predictand. Table 1a reports the code used to identify
the different domains for the large-scale predictors, depending on the season and the predictand
considered, while Table 1b reports the predictor
definitions used within the best performing model for all predictands and seasons. This last table
reveals that, in general, all surface fields are best
predicted using T850 except for: winter Tmax90
and autumn Tmin10, best predicted by a combination of T850 and MSLP, spring Tmax90
and, winter, spring and summer HWD, best predicted by Z500.
SON
C
E
C
D
E
T850=area C
T850=area C
T850=area C
T850 þ MSLP=area C
T850=area C
T850=area E
The sensitivity of the models to the number of
EOFs retained for the CCA, and to the number of
CCA components used in the regression has also
been checked. A model is considered optimal if
it satisfies the condition that its skill (CORR,
BIAS, RMSE) does not change significantly after
the addition of one component (CCA). It is known
that, in general, the addition of more canonical
modes enhances the accuracy first, but after adding a certain number of EOF=CCA patterns the
accuracy declines slightly (Busuioc et al., 2001;
Huth, 2002). Results with respect to this issue are
shown for a limited number of cases for reasons
of brevity.
Finally, the SD models derived from the observations are applied to the HadAM3P output,
from present day experiments (1960–1990) and
experiments done using different future IPCC
SRES scenarios (2070–2100). In order to do this,
the model anomalies with respect to the control
model climate are projected onto each CCA predictor pattern. The full time series for the A2 and
B2 scenarios is computed by adding downscaled
anomalies to the observed mean. Climate changes
in the predictands presented above are calculated
as the difference between the mean of the downscaled values for the period 2070–2100 (A2 and
B2 scenarios) and for present day climate. The
statistical significance of the changes is also evaluated using the Student’s t-test.
3. The statistical downscaling models
3.1 Two examples of links between large-scale
patterns and local climate
As previously mentioned, a by-product of the CCA
method is that it automatically identifies the link
between large-scale variability and local climate.
Given the great number of models presented here
(different depending on season and predictand),
Climate change scenarios for surface temperature in Emilia-Romagna
Fig. 2. First two CCA pairs of T850 (a and c) and Tmin10 (b and d) for winter
for brevity, only a limited number of models are
described in detail, highlighting the dynamical
and physical interpretation of the predictor-predictand link. In the following, the predictand-predictors connection is presented for two extremes:
winter Tmin10 and summer HWD.
Figure 2 (a–d) shows the first two CCA pairs
of T850-Tmin10 connection obtained by retaining
the first four EOFs of winter T850 and four EOFs
of winter Tmin10. The variance explained by the
first four EOFs of T850 and Tmin10 is presented
in Table 2a. As can be observed, together the patterns explain around 90% of the total variance for
both predictor and predictand.
Figure 2a and b present the first canonical
correlation anomaly pattern (CCA1) of T850Tmin10, resultant from the above combination,
characterised by a correlation coefficient between
fields of 0.73 and by a fraction of explained
Table 2a. Fraction of total variance (%) explained by the
first four EOFs of winter T850 and Tmin10
EOF1
EOF2
EOF3
EOF4
T850 (%)
Tmin10 (%)
42
33
10
5
79
6
2
2
Table 2b. Mean skill over Emilia-Romagna of winter
Tmin10 for different SD set-up
No. EOFs predictor=
predictand; No. CCA
in SD
BIAS
RMSE
Spearman
correlation
4EOFs T850=4EOFs
Tmin10; 2CCA
6EOFs T850=7EOFs
Tmin10; 3CCA
0.74
0.38
0.6
0.71
0.39
0.4
R. Tomozeiu et al.
variance around 15% for T850. The CCA1 of
Tmin10 explains a great part of the total variance, 55%, and represents the main pattern of
covariability for the T850-Tmin10 couple. This
is associated with a negative (positive) anomaly
of T850 centred over the Eastern Mediterranean
and Turkey, extending to Northern Italy, with
anomalies of Tmin10 with the same sign over
Emilia-Romagna (Fig. 2b), with maximum amplitude located over the plains. The T850 anomaly
is typically associated with a large-scale circulation pattern characterised by a strong positive
Z500 anomaly which extends from Eastern
Europe to Russia, advecting cold continental
air over the Eastern part of the Mediterranean
and South-Eastern Europe. This pattern leads to
a northward shift of the European end of the
Atlantic mid-latitude jet, and over Northern Italy
it is often associated with clear sky nights and
with temperature inversions. During these episodes, anomalously low values of Tmin10 can
be observed over the plain, while minimum temperature can reach very high values over the
mountains, with anomalies up to þ10 C, well
over the 50th percentile. This justifies the fact that
the maximum amplitude of this pattern is located
over the plain. Under the opposite phase, this CCA
is linked with a negative Z500 anomaly over
Eastern Europe and Russia, with an increase in
T850 over the Eastern Mediterranean, positioned
well under the southern flank of the jet, and,
in Emilia-Romagna, with milder Tmin10 values,
especially over the plains, protected by clouds.
The second CCA (Fig. 2c and d) explains 40%
of the T850 total variance and 18% of the
Tmin10 total variance. The time series associated
with the predictor=predictand patterns are significantly correlated at 0.55. The T850 pattern
is characterised by a negative (positive) anomaly
centred over south-central Mediterranean and
Northern Africa. This leads to negative (positive)
anomalies of Tmin over Emilia-Romagna with
maximum amplitude over the mountains, and
large-scale circulation characterised by the presence of an anomaly in the intensity (weakening
in the phase represented in Fig. 2c) of the
westerly jet, with its main axis located over the
British Isles and Northern Europe. Under these
conditions, the temperature is normally stratified,
the region often experiences transient synoptic
systems of Atlantic origin, and greater anomalies
of minimum temperature are generally observed
over the mountains, more exposed to upper air
cold (warm) advection from North-West (South,
South-East).
The CCA1 and CCA2 of T850 are the most
important patterns controlling the variability of
winter T850. The third and fourth CCA patterns
are characterised by lower values of the predictorpredictand correlations than the first two CCA
pairs and present a dipolar structure (maps not
shown). The second row of Table 2b shows the
skill in terms of BIAS, RMSE and Spearman
correlation for the SD model obtained using only
the first two CCAs.
In order to examine the influence of the number of EOFs retained in the construction of CCA,
the analysis is repeated using the first six EOFs
of T850 and seven EOFs of Tmin10 explaining
97.5% from the total variance of predictors=
predictands. Then, the first three CCA patterns
with the associated time series (that are significantly correlated) are used in the construction of
the SD model. Analysing the skill from Table 2b
that presents both SD versions (row 2 and row 3)
it can be observed that, while no significant
changes are present in the BIAS and RMSE, an
improvement is observed in the Spearman correlation when a combination of four EOFs of both
predictor and predictand is used in the CCA analysis and only the first two CCA pairs are selected
in the construction of the SD model. This type of
sensitivity test has been done for each predictand=predictor couple in order to find the optimum SD model.
The relationship between HWD and Z500 during summer is presented in Fig. 3. In this case,
the first three EOFs of Z500, explaining together
80% of the total variance and the first three EOFs
of HWD, explaining together 75% of the total
variance, are used to filter the data prior to CCA.
Figure 3a and b show the CCA1 of the Z500HWD pair. The time series associated with these
patterns (not shown) are significantly correlated
(0.7) while the fraction of total variance explained by each pattern is 32% for Z500 and
49% for HWD. The CCA1 of Z500 (Fig. 3a) is
characterised by a dipole with maxima located
over Central Europe and minima over Northern
Africa. This is the typical anomaly circulation
pattern associated with long summer heat waves
over Central and Southern Europe, similar to the
Climate change scenarios for surface temperature in Emilia-Romagna
CCA HWD (JJA)
CCA1 Z500 (JJA)
50N
45N
40N
35N
30N
5W
0
5E
10E
15E
20E
25E
30E
35E
a
b
CCA1 Z500 (JJA)
CCA HWD (JJA)
50N
45N
40N
35N
30N
5W
0
5E
10E
15E
20E
25E
30E
35E
c
d
Fig. 3. The first two CCA pairs of Z500 (a and c) and HWD (b and d) for summer
large-scale anomaly which characterised summer
2003 over the Euro-Atlantic sector (Cassou et al.,
2005; Colacino and Conte, 1995). In EmiliaRomagna it leads to a substantial increase in the
length of the HWD index, with maxima of about
two days located over the hills in the central-eastern part of the region, the areas which are most
frequently affected by the convective break up of
dry spells.
The second CCA pair (Fig. 3c and d) is characterised by a Z500 pattern explaining 28% of the
total variance, which presents a dipolar structure
with centres located over North-Eastern Europe
and Northern Africa-Western Mediterranean, leading to a prevailing south-easterly wind anomaly
over Emilia-Romagna. This flow is typically associated with moist warm air from the Adriatic
Sea, which leads to an increase in surface air temperature everywhere but in a few locations where
it favours the onset of strong convective events.
These events may explain the local shortening
of the HWD index over the eastern plain. This
HWD pattern explains 16% of the total variance.
The time series associated with the second CCA
pair is significantly correlated at 0.55.
CCA1 and CCA2 are used in the construction
of the SD model in order to estimate summer
HWD for the 1979–1993 period.
3.2 Evaluation of the performance of the SD
models-mean and extreme indices
In this section a description of the performance
of all statistical downscaling models built for
different seasons and predictands in terms of
Spearman correlation, BIAS, and RMSE is presented. These skill coefficients are computed for
the period 1979–1993 while the construction of
the model has been made for the periods 1958–
1978 and 1994–2000.
The mean value of each skill indicator over
Emilia-Romagna is computed for each season
and index and is shown in Fig. 4. The Spearman
correlation values for Tmax and Tmin (Fig. 4a)
R. Tomozeiu et al.
Fig. 4. Mean skill over all stations of the statistical downscaling models for each season and predictand: Spearman
correlation (a), BIAS (b and c), RMSE (d and e)
emphasises that these fields are well downscaled
during winter, spring and summer (correlation
significant above 95%) and less so during autumn (correlation significant at 80%). The BIAS
(Fig. 4b) of Tmax varies between 0.1 C (summer)
and 0.4 C (winter) while that of Tmin reaches
0.6 C (winter). The RMSE (Fig. 4d) reveals similar values for both parameters, with lower values
in summer, around 0.2 C, and close to 0.3 C for
the other seasons.
The skill of the SD models for extreme
events of minimum temperature – Tmin10 and Tnfd
(Fig. 4a) – reveals good performances in all
seasons for Tmin10, with significant correlation,
BIAS and RMSE values (Fig. 4b and d) around
0.5 C (in winter and autumn), while Tnfd presents
good performances during winter and autumn.
The skill of the SD models for the 90th percentile of maximum temperature in Fig. 4a, b and d
indicates good performance during winter, spring
and summer, while small skill is obtained in autumn (correlation significant at 80%). Concerning the HWD index, spring is the season with the
best performances, followed by winter, while
during summer and autumn the performance is
poorer (Fig. 4a, c, e). An overview of the above
results reveals that the mean fields are better
downscaled than the extremes, although it should
be noted that a correlation value of 0.4 is significant at the 80% level, and that all fields, except summer and autumn HWD, are reasonably
downscaled (Fig. 4a).
The BIAS that occurs in the modelled indices
could be due to the different periods used in the
Climate change scenarios for surface temperature in Emilia-Romagna
construction and validation of the model (different climate). This BIAS has been removed when
the root-mean square error has been computed.
The work presented above helps to select the
optimum downscaling model for each index=
season and to establish which indices can be
downscaled.
4. The HadAM3P validation
The ability of HadAM3P to reproduce predictors under present-day climate conditions is
very important when assessing the reliability
of climate change projections under scenario
conditions. The simulations of the T850, MSLP
and Z500 provided by HadAM3P for the period
1960–1990 are compared with the same fields
provided by NCEP=NCAR reanalysis for the
same period. The comparison is done by performing an empirical orthogonal function (EOFs)
analysis and comparing the corresponding observed and simulated patterns. The analysis is
done for all seasons and using data averaged
over each season. The main characteristics of
the EOF patterns taken into consideration within
this study are:
the variance explained by each EOF pattern;
the spatial correlation between model and reanalysis EOF patterns.
The verification is done at the seasonal level and
for the area identified to be the ‘‘optimum’’ area
in the construction of the SD model (Table 1b).
The significance of the spatial correlation has
been tested using the Fisher’s Z transformation
(at 95% confidence level). In the following, the
verification of the seasonal predictors for the area
35 W–35 E and 30 N–60 N is presented.
Similar results to those presented below, have
been obtained using different validation techniques that range from the simple analysis of
long-term seasonal mean fields and daily standard deviation to more complex analyses such
as composite patterns of circulation types or
Principal Component analysis using singular
value decomposition of standardised anomalies.
Diagrams with different methods of validation
of these predictors and for the whole European
area are assembled on the STARDEX web site
(http:==www.cru.uea.ac.uk=cru=projects=stardex=
deliverables=D13=).
4.1 Z500 variability
Z500 is identified as the best predictor of the heat
wave duration index during winter, spring, and
summer, and of spring Tmax90. Comparison of the
HadAM3P data with the reanalysis reveals quite
an accurate model representation of the main
patterns of large-scale variability of Z500 in all
seasons. The analysis has focused on the phase
space representation by standard EOFs. Figure 5
presents the first pattern (EOF1) of Z500 for each
season, derived from reanalysis (a, c, e, g) and
model (b, d, f, h) data. The winter EOF1 pattern
derived from NCEP (Fig. 5a) is reproduced by
the model (Fig. 5b) but is shifted eastward, and
the spatial correlation between the two patterns is
statistically significant at 0.74. In particular, the
main centre of the positive anomaly is not centred
over the Atlantic just west of the European
coasts, implying a North-Westerly large-scale flow
over Central Europe, but exactly centred over
Central Europe. The amplitude of the pattern is
also more intense in the observations than in the
model data. A good resemblance is found for the
other three winter patterns (figures not shown),
with significant correlation values between patterns (around 0.9). The fraction of total variance
explained by the first four EOFs (Table 3) of the
model is very close to that of the reanalysis.
During spring, significant skill in terms of
spatial correlation is found for all four EOFs,
closer for NCEP EOF1 (correlation 0.9) and
EOF4 (correlation 0.73) while EOF2 and EOF3
are inverted and the correlation is around 0.6.
Figure 5c and d present, as an example, the spring
EOF1 of Z500 derived from NCEP and controlrun data. The dipolar structure of NCEP EOF1 is
well represented by the control run. The fraction
of variance explained by the first four EOFs is
higher for the model than for the reanalysis, the
model overestimates the variance explained by
the first two EOFs (Table 3).
In summer, the patterns are reasonably well
reproduced by the model, with a correlation
between EOFs lower than in winter and spring,
and statistically significant for the first three
EOFs (between 0.6 and 0.7). Although the EOF1
control run pattern shares similar characteristics
with the EOF1 NCEP pattern, some differences
can be identified. In particular, the main positive
anomaly centred over the British Isles in the re-
R. Tomozeiu et al.
Fig. 5. EOF1 of winter (a and b), spring (c and d), summer (e and f) and autumn (g and h) Z500 for the period 1960–1990 for
NCEP=NCAR reanalysis (left column) and HadAM3P model (right column)
analyses is located westward in the model, well
over the North Atlantic. Furthermore, the model
circulation is somewhat more intense than the
observed and has a main northerly direction over
Central Europe and Northern Italy, while in the
observations it is orientated in an easterly-north
Climate change scenarios for surface temperature in Emilia-Romagna
Table 3. Explained variance (%) of the first four EOFs of the seasonal Z500 (NCEP and control run)
Winter
EOF1
EOF2
EOF3
EOF4
Spring
Summer
Autumn
NCEP
ctr. run
NCEP
ctr. run
NCEP
ctr. run
NCEP
ctr. run
37.2
34.6
12.4
8.3
35
31
16
9.3
26
19.3
17.6
13.5
32
30
15
7.7
44
14
12.6
11
45
20
15
6
40
24
12
10
33
31
14
8
easterly direction. Finally, the model tends to overestimate the fraction of total variance explained
by the first four EOFs (Table 3).
During autumn, significant skill in terms of
spatial correlation has been found for all four
EOFs, higher for EOF2 (0.9) and EOF1 (0.75)
while EOF3 and EOF4 are inverted and the correlation is around 0.7 (statistically significant at
the 95% level) for both patterns. Figure 5g and h
present the patterns of the autumn EOF1 as derived from NCEP and control run data, respectively. Both the model and the reanalysis EOF1
pattern present a main positive anomaly centred
in the vicinity of the British Isles (West of Ireland
in the reanalyses and over Scotland in the model)
and the intensity of this anomaly is captured reasonably well. The main difference between the
two patterns is that the location of the most intense geopotential gradient is east of the positive
anomaly in the NCEP data, implying a northerly
flow over Central Europe and Northern Italy,
while in the model it is south-west of the main
positive anomaly, leading to a upper-air northeasterly circulation anomaly over Northern Italy
and Central Europe and an intense return flow
(easterly) anomaly over the North Atlantic. Finally,
the model tends to underestimate the variance
explained by EOF1 (Table 3) while the fraction
of variance explained by the first four EOFs of
control run data is equal to that explained by the
NCEP patterns.
The features of the simulated patterns of Z500
presented above from both model and reanalysis
are in general agreement, although some differences are seen.
4.2 MSLP variability
Mean sea level pressure (MSLP) is the best predictor for a limited number of extreme temperature
winter, spring and autumn indices (see Table 1b).
In the following, the representation by the model
of the large-scale MSLP variability is validated
for the area 35 W–35 E, 30 N–60 N, and for the
seasons of interest, in the same way as for T850
and Z500. This analysis emphasises that the general features of the simulated large-scale variability are reasonably well reproduced in the model.
During winter, the spatial correlation of the corresponding patterns varies between 0.7 (EOF1)
and 0.9 (EOF4). The winter EOF1 (not shown)
resembles a similar configuration as winter Z500,
shifted to the east (as in case of Z500). The
variance explained by this pattern is 41% in
the model and 45% in the reanalysis, while the
fraction of total variance explained by the first
four EOFs is 93% in the model and 94% in the
reanalysis.
During spring and autumn the model has good
skill in representing MSLP variability. In spring,
the first four EOFs of the model explain 91% of
the total variance while the corresponding group
of NCEP EOFs explains 86%. The spatial correlation is high, between 0.8 and 0.9, while the
RMSE varies between 0.01 and 0.4. In particular,
the EOF1 for this season, shown in Fig. 6a and b,
have a spatial correlation of 0.7. The model pattern presents a high pressure anomaly centred
slightly east of the observed and is associated
with a more intense zonal gradient, with the low
pressure in the north-east of the domain being
overestimated. As a result, the surface pressure
gradient over Europe is higher in the model than
in reality, presenting a less optimistic picture than
the upper air Z500 analysis for the same season.
However, it can be said that the model captures
the general features of the surface flow over the
region of interest, is similar to the Eastern Atlantic
pattern, and is quite well represented. The first
four EOFs (not shown) of autumn MSLP explain
a fraction of the total variance equal to that explained by the corresponding reanalysis patterns
R. Tomozeiu et al.
Fig. 6. EOF1 of spring MSLP (a and b), and winter T850 (c and d), period 1960–1990, for NCEP=NCAR reanalyses (left
column) and HadAM3P model (right column)
(91%). The spatial correlation of the patterns again
reveals a sufficiently good representation of the
four EOFs of MSLP (0.7–0.9).
4.3 T850 variability
The patterns of the first four EOFs of winter
T850 derived from HadAM3P data explain together 85.4% of the total variance, very close to
the value derived from the reanalysis (85%). The
spatial correlation between model and reanalysis
reveals that all four winter patterns are very well
simulated, the correlation varying between 0.92
(EOF1) and 0.80 (EOF4). The EOF1 of winter
T850 derived from NCEP (49% variance) and
HadAM3P (42% variance) are represented in
Fig. 6c and d. They show the presence of a
greater extension of the Northern European temperature anomaly into Central Europe including,
to a certain extent, Northern Italy. This is consistent with the differences observed in the upper air
circulation anomalies associated with the Z500
EOF1 pattern.
The HadAM3P model simulates reasonably well
all four EOFs of spring T850 (maps not shown).
The correlation between the patterns is statistically significant and varies between 0.6 (EOF1)
and 0.9 (EOF4). The fraction of variance explained by the first four EOFs of the model is
76%, while those explained by the first four
EOFs of reanalysis is 72%.
The analysis of the first four EOFs of T850
during summer, reveals an overestimation of the
variance in the model (80% explained by the four
EOFs) with respect to reanalysis data (66%). The
best simulated patterns in summer are EOF2
(correlation 0.72) and EOF3 (correlation 0.6) of
the model which coincide with EOF2 and EOF4
of NCEP. For EOF1, the spatial correlation is
lower than for EOF2 and EOF3, but is still statistically significant at 0.4. The general problems
in the representation of T850 pattern of variability are a reflection of the differences noted earlier
between the main observed and modelled upper
air circulation anomalies as described by the
Z500 field.
During autumn, the fraction of total variance
explained by the first four EOFs of the model is
76% while that explained by reanalysis is 72%.
The spatial correlation emphasises that the first
Climate change scenarios for surface temperature in Emilia-Romagna
three EOFs are well reproduced by the model, with
the correlation between modelled and observed
varying between 0.7 and 0.8.
5. Downscaling of predictands
for the present climate (1960–1990)
In order to test the ability of the HadAM3P model
to simulate the variability of local climate for
the period 1960–1990 and the robustness of the
SD models, these are applied to the HadAM3P
control-run data. Mean value, BIAS, trend and
standard deviation are evaluated using the SD
models for each predictand=station and compared
with those computed from the observed data during the period 1960–1990. The results show that
for all indices the mean seasonal value is, in general, well reproduced. An example of the comparison between observed and downscaled data is
presented in Fig. 7a and b, for seasonal mean
values of Tmin and Tmax at two stations, one in
the plain (Bologna – 51 m), and the other in the
mountains (Sestola – 1020 m). The downscaled
values reproduce the seasonal cycle very well,
in both minimum and maximum temperature.
Similar results are obtained for other stations,
and the mean over all stations of the difference
between downscaled and observed data (BIAS)
of Tmin=Tmax is 0.8 C during winter and lower
for the other seasons. The mean values of the
extreme indices (Tmin10, Tmax90) are also well
reproduced by the SDs in all seasons, better for
Tmax 90, when the mean BIAS over all stations
and seasons does not exceed 0.5 C (spring), than
for Tmin10, when the mean BIAS over all stations does not exceed 1 C (plots not shown).
Concerning the number of frost days and heat
wave duration indices, the mean values of downscaled data are close to those of the observed
except for winter Tnfd, with differences around
five days observed at some stations situated in
the plain area.
Trend analysis of downscaled and observed
data reveals that in general the signal is captured
by the SDs, sometimes with lower intensity in
the downscaled than in the observational data.
Figure 7c presents the trend coefficient for minimum temperature at the stations of Bologna and
Sestola ( C=decade). As can be observed, except
for summer minimum temperature at the Sestola
Fig. 7. Observed and downscaled values of seasonal mean minimum (a) and maximum (b) temperatures, Tmin trend (c) and
standard deviation (d) for all seasons, at Bologna and Sestola stations
R. Tomozeiu et al.
station, the sign of the trend is captured although,
sometimes, with lower intensity. Similar results
are obtained for maximum temperature, as well
as for extreme indices.
Figure 7d presents the values of the standard
deviation of seasonal minimum temperature at
Bologna and Sestola. The inter-annual variability
of the downscaled data is underestimated in comparison with the observed, as in most other statistical downscaling models.
6. Climate change scenarios of mean and
extreme temperature in Emilia-Romagna
The statistical downscaling models built using
observational data are finally applied to the following HadAM3P scenario experiments: A2,
a medium-high emissions scenario, with atmospheric CO2, concentrations reaching 715 ppm,
and B2, a medium-low scenario, with CO2 reaching 562 ppm. The predictor anomalies used as
input in the statistical downscaling models have
been computed as differences between the scenario full fields and the control run climate.
Regarding the predictand anomalies, downscaled
from A2 and B2 experiments, these have been
added to the observed climate in order to obtain
the full field time series of the indices for the
period 2070–2100. Changes are computed as the
difference between mean values of each index in
the period 2070–2100 and those in the control
run (1960–1990). The significance of changes
has been statistically tested using the Student’s
t-test. The analysis begins with a discussion of
changes in seasonal minimum and maximum
temperature, derived from the downscaling models for each station for both A2 and B2 scenarios,
followed by the scenarios of extreme temperature
indices.
6.1 Changes in seasonal minimum
temperature (Tmin)
Climate change projections for the A2 and B2
scenarios indicate the possibility of an increase in
minimum temperature in all seasons. Figure 8a
presents changes in Tmin, averaged over all stations, for each scenario (A2 and B2) and each
season. As can be observed, the increase is always
greater in A2 than in B2, except in spring, and is
always weaker in spring than in the other seasons.
Fig. 8. Projected changes in seasonal averaged minimum
(a) and maximum (b) temperatures for A2 and B2 scenarios. Mean over all stations
At the station level, changes are greater for the
stations situated in the plain while small spatial
differences in changes (except summer) are noted
over hills and mountains. A2 scenario changes in
Tmin reach a maximum of 3.5–4 C in winter,
summer and autumn at stations situated in the plain
and 2.5 C at the stations over hill=mountains.
These results are statistically significant at the
95% level for all stations.
During spring, the changes in Tmin are less
intense than in the other seasons, but are still
statistically significant, with a maximum around
0.7 C for the stations situated in the plain. At
some stations over the Apennines, minimum temperature is projected to decrease, although the
result is not statistically significant. It is possible
that the choice of MSLP as the only predictor
for this SD model is responsible for the smaller
change of this index under scenario conditions
than in all other seasons. In fact MSLP, which
mostly reflects changes in large-scale circulation
and not radiation-induced temperature changes,
may be less affected by changes in atmospheric
composition than upper air temperature (Schubert,
1998; Huth, 2004). Given that this was identified
as the predictor of choice, leading to the best
Climate change scenarios for surface temperature in Emilia-Romagna
performing SD model, it was decided to retain
this variable.
In the B2 scenario the projected changes reach
a maximum value of 2.5 C in winter and autumn
and 3.5 C during summer.
6.2 Changes in seasonal maximum
temperature (Tmax)
The seasonal mean values of Tmax, as in the case
of Tmin, are projected to increase in all seasons.
Figure 8b presents the changes in maximum
temperature averaged over all stations for both
scenarios (A2 and B2). Comparing these increases with those in minimum temperature, the
projected changes of Tmin are slightly greater
than Tmax during winter and autumn, suggesting, for these seasons, a general decrease in
the daily temperature range, which is consistent
with the expected changes under scenario conditions. The same is not true in spring and summer when the changes are greater for Tmax than
for Tmin.
At the station level, Tmax changes are characterised by small spatial differences over the
whole region during spring, while in winter and
autumn the predicted increase of Tmax is higher
in the plain than over the hills and the mountains.
In summer, the changes tend to be more intense
over the mountains, while similar values are detected in the plain and over the hills.
A comparison between A2 and B2 reveals, as
in the case of mean minimum temperature, that
changes are more intense in the A2 than in the
B2 scenario. The values of the changes reach a
maximum of 3 C and 2.5 C in winter and of
4.5 C and 4 C, respectively, in spring. In both
seasons, the changes are statistically significant
at nearly all stations, with the few exceptions located in the western part of the Apennines. During summer, significant changes are projected to
occur at all stations in both experiments, with a
mean change around 5 C (Fig. 8b) and local
values up to 8 C.
In autumn, under both scenarios, positive and
significant changes in Tmax are projected to occur
at almost all stations, with values up to 5 C and
3 C for the A2 and B2 scenario, respectively.
An analysis of the probability density function
of seasonal minimum and maximum temperature
for present day (1960–1990) and future (2070–
2100) reveals a shift to the right of the distribution in both minimum and maximum temperature.
Regarding the shapes of the minimum and maximum temperature distributions, these tend to become wider in autumn and sharper in winter and
spring under scenario conditions. As for summer,
the minimum temperature distribution widens,
while the maximum temperature remains more
or less similar.
The results of changes in Tmin and Tmax are
consistent with those presented for the mean surface air temperature over the Italian peninsula by
Giorgi et al. (2004b), based on nesting RegCM
with the HadAM3H model, for both A2 and B2
scenarios. The authors compared the scenarios
for both models – RegCM and HadAM3H –
and found that the most pronounced warming
in Italy may occur in summer, and that the mean
over the region is around 5 C in RegCM and
6 C in HadAM3H. For the other seasons, the
projected increase is around 4 C. In all seasons,
the increase is found to be greater in the A2 than
in the B2 scenario. Räisänen et al. (2004) analysed the changes in the highest yearly maximum
temperature (Tmax) and in the lowest yearly
minimum temperature (Tmin), derived from the
regional climate change simulations done at
the Swedish Rossby Centre, and concluded that
the changes are expected to be larger for the
A2 than the B2 scenario and in most cases
in the ECHAM=OPYC3-driven (RE) than in
the HadAM3H-driven (RH) simulations. In all
the scenario runs, the warming in Central and
Southern Europe peaks in summer when it locally reaches 10 C in the RE and around 6–7 C
in the RH.
6.3 Changes in seasonal Tmin10
Projected Tmin10 changes are significant in all
seasons and most intense during winter, followed
by spring and summer. Seasonal changes in
Tmin10 are presented in Fig. 9, for the A2 scenario. Shading indicates the value of the change
in Tmin10 (2070–2100 minus 1960–1990) while
black dots indicate stations characterised by statistically significant results at the 95% level. As
can be noted, significant increases during winter
(Fig. 9a) are expected to occur everywhere, with
maximum values (up to 7 C) in the plain area.
The general pattern of the change closely resem-
R. Tomozeiu et al.
Fig. 9. Pattern of projected changes in Tmin10 (A2 scenario): winter (a), spring (b), summer (c) and autumn (d). Shading
represents the value of changes, while stations characterised by significant results (at 0.05 level) are marked with black cycles
bles that of the Tmin10 CCA1 pattern, suggesting
that the observed changes in this index are mostly due to an increase in T850 over the eastern
part of the Mediterranean, associated with a negative Z500 anomaly over Eastern Europe and
Russia (see Sect. 3.1). The mean change averaged
over all stations is projected to be around 4.5 C.
Similar results are found for the B2 scenario, although in this case changes are less intense than
for A2.
Spring Tmin10 (Fig. 9b) is projected to increase
significantly, with maximum changes around 3 C
and 2.4 C in the A2 and B2 scenarios, respectively. During summer, Tmin10 (Fig. 9c) shows
significant changes at all stations, with positive
values for a great part of the region (maximum
around 5 C) and negative values (up to 1 C) at
some stations in the central plain and the Adriatic
coast (Codigoro, Alfonsine, Modena, ReggioEmilia, and Parma). The summer change averaged
over all stations is projected to be about 2 C
less than for Tmin. During autumn (Fig. 9d), the
changes are also significant over the whole region
but less intense than in the other seasons (maximum around 1 C). Some negative changes, not
statistically significant, are projected at some stations in the Apennine region.
Comparing the projected changes in winter
Tmin10 (Fig. 9a) with those of winter Tmin
(Fig. 10b), the observed changes in Tmin10 are
more intense than in Tmin suggesting a sharpening of the Tmin probability distribution in this
season. Similar results have been obtained for
spring, while during summer the magnitude of
changes are slightly smaller in Tmin10 than for
mean Tmin (maps not shown).
6.4 Changes in the seasonal number
of frost days (Tnfd)
Under both scenarios, the number of frost days
(Tnfd) is projected to undergo a significant decrease during all seasons (no frost day is either
observed or projected in summer). Figure 10a
shows the changes in winter Tnfd (A2 scenario).
As can be observed, significant decreases are
projected to occur at all stations, with values locally more intense in the plain (except at two
stations) and hill regions than over the mountains.
This pattern is similar to that of winter Tmin
Climate change scenarios for surface temperature in Emilia-Romagna
6.5 Changes in seasonal Tmax90
Fig. 10. Pattern of projected changes in winter Tnfd (a) and
winter Tnav (b) for A2 scenario (c) Maximum and minimum
values attained by Tnfd over Emilia-Romagna, in both A2
and B2 scenarios
(Fig. 10b) under the A2 scenario, suggesting that
the main reason for the decrease in this index is due
to the general rise in the daily minimum temperature. Regarding the other seasons, Fig. 10c presents the minimum and maximum values of the
seasonal changes projected to occur for Tnfd,
under the A2 and B2 scenarios. As can be observed, in winter the number of frost days is projected to decrease up to 40 and 30 days in spring
up to 25 and 20 under the A2 and B2 scenarios,
respectively, while in autumn is the decrease is
lower at around 10 days. This is due to the fact
that the seasonal mean Tmin is greater in autumn
over the whole region than in winter and spring
(see Fig. 7a). All results are statistically significant at the 95% level over the whole region.
Under both A2 and B2 scenarios, Tmax90 is
projected to increase everywhere, except at a few
stations located over the Apennines (Bedonia,
Sestola, and Pavullo nel Frignano) where a significant decrease is found in all seasons. Figure 11
presents the changes in Tmax90 for winter (a),
spring (b), summer (c) and autumn (d) for the
A2 scenario. As can be observed, the season with
greatest increase is summer, followed by autumn
and spring.
In winter, the increase of Tmax90 reaches 1 C
(Fig. 11a), the pattern of changes are similar to
those of winter Tmax, but less intense. For spring,
the changes in Tmax90 reach values around
2.5 C (Fig. 11b) with a similar but less intense
pattern to that of spring Tmax. During summer,
Tmax90 is projected to decrease at two stations
located over the western part of the Apennines
(signal of decrease in this area is present in all
seasons) and to increase in all other stations, with
maximum values of increase in the south-eastern
part of the region (up to 8 C). Changes in summer Tmax90 are similar to those in summer Tmax
(map not shown) in terms of magnitude and pattern. Concerning autumn, a significant increase
is projected in Tmax90 with values up to 5 C
(Fig. 11d), very close to those of changes in autumn Tmax. The comparison between the maps
of seasonal Tmax90 and Tmax changes suggests
that, as in the case of Tmin, it is possible to predict a sharpening of the Tmax probability distribution under scenario conditions in both winter
and spring.
A comparison between A2 and B2 reveals similar changes, but with greater intensity in the A2
than in the B2 scenario. For example, the changes
averaged over the region for the B2 experiment
are equal to 1.7 C in autumn, 1 C in spring and
around 0.5 C in winter.
6.6 Changes in seasonal HWD
In general, HWD is projected to increase in all
seasons for both the A2 and B2 scenarios.
The signal is greater in summer when the maximum change reaches a value of 23 days. Taking
into account that the performance of the statistical model (Fig. 4) is not statistically significant during summer and autumn, the following
R. Tomozeiu et al.
Fig. 11. As in Fig. 9, but for Tmax90 (A2 scenario)
Fig. 12. Pattern of projected changes in spring heat wave duration (a) and spring Txav (b) for A2 scenario
discussion is focused only on results for winter
and spring.
During these seasons, positive changes in HWD
are projected to occur in both scenarios, more
intense during spring than in winter when the
increase is around two days. Figure 12a presents
the pattern of changes in HWD (A2 scenario)
during spring. As can be observed, the increase is
statistically significant at all stations and reaches
a maximum of about 10 days in the northern part
of the region. The pattern of change is very similar to that for spring Tmax (Fig. 12b) with maximum change in the central part of the region.
7. Conclusions
In the present study, statistical downscaling models (SDs) are built and used in order to provide
future scenarios of temperature, mean and extremes, in Emilia-Romagna. The SD models are
based on canonical correlation analysis, a technique that also helps to understand the large-scale
mechanism that controls the local climate variability. The projection of changes in temperature
under scenario conditions are constructed using
as large-scale data input the HadAM3P model
simulations, from the control, the A2 and the
B2 IPCC SRES scenarios.
Climate change scenarios for surface temperature in Emilia-Romagna
The work done in order to select the optimum
mode with respect to RMSE, BIAS and Spearman
rank correlation values for each predictand reveals that:
1) T850 is the best large-scale predictor for reproducing local variability of seasonally averaged minimum and maximum temperature in
Emilia-Romagna, with the exception of spring
Tmin which is best simulated using MSLP
only. T850 is also the best predictor for modelling the temporal and spatial variability of
almost all local extreme temperature indices.
All the same, winter Tmax90 and autumn
Tmin10 are best estimated using a combination of T850 and MSLP, while spring Tmax90
and nearly all seasons of HWD (except autumn) are best predicted using only Z500
(see Table 1b);
2) the skill of the models is dependent on the
combinations of EOFs=CCAs used to build
the SD model and on the domain (area) of
predictors;
3) the skill of the models varies between seasons,
and indices, their performance being generally better for mean values than for extremes.
The index with lower skill is the heat wave
duration for summer and autumn;
4) HadAM3P is able to reasonably simulate the
variability of the predictors – T850, MSLP and
Z500 – in all seasons, although some differences have been noted between the model generated and the observed patterns and between
the fractions of variance explained by the corresponding patterns.
The main conclusions concerning the changes
projected to occur in Emilia-Romagna under
A2 scenario conditions can be summarized as
follows:
significant increases are projected in minimum
temperature in all seasons. The signal has a
greater magnitude in winter, summer and autumn than in spring. The average over all stations of the projected change is about 2 =2.5 C
for all seasons, while the minimum=maximum
values of increases vary from season to season,
and are less intense in spring than in the other
seasons;
significant increases are projected in maximum temperature in all seasons, more intense in
spring and summer when the mean over all
stations of projected changes reaches 3 C and
5 C, respectively. During winter and autumn
the magnitude of changes is around 2 C (mean
over all stations);
significant increases are projected in Tmin10 in
all seasons, more intense in winter, when the
mean change over all stations is around
4.5 C. For spring and summer the magnitude
of increase is around 2 , while in autumn it is
around 1 C;
the number of frost days will decrease, more in
winter (up to 40 days), and spring (up to 25
days), and less in autumn (up to 10 days);
significant increases are projected for Tmax90
in all seasons, most intense in summer and autumn, when the mean change over all stations
is predicted to be around 4 C and around 2 C,
respectively. During spring the increase will
be around 1 C, while during winter it will be
less than 1 C (mean over the region), when the
mean value of Tmin is greater over the whole
region;
significant increases are projected to occur for
the HWD index over the whole region during
spring, with values up to 10 days. With regards
to other seasons, HWD shows significant increases, especially during summer, but the SD
model does not have significant skill and this
particular projection must be regarded with
caution.
Similar signals of increases in mean and extreme
temperature are projected to occur under B2 scenario conditions, but be less intense than under the
A2 scenario.
These results are consistent with those obtained
within the STARDEX project for other European
regions, such as Greece, the German Rhine, and
the Iberian Peninsula using other statistical downscaling techniques (http:==www.cru.uea.ac.uk=cru=
projects=stardex=reports=STARDEX_FINAL_
REPORT.pdf) or within the MICE project
(www.cru.uea.ac.uk=cru=projects=mice) using
dynamical downscaling techniques. In these studies, more intense decreases are projected to occur
in winter Tnfd across Europe than in EmiliaRomagna, with values between 60 to 120 days
per year, and the greatest reductions in Northern
Europe. In addition, the annual number of ‘very
hot’ days (index similar to HWD) is projected to
R. Tomozeiu et al.
increase by several weeks across the UK and
Scandinavia, while increases up to 120 days are
projected to take place in other parts of Europe.
Finally, a set of PRUDENCE RCM experiments
reveals that HWD will increase over Europe and
the intensity of extreme temperatures will increase more rapidly than mean temperature by
the end of 21st century (Beniston et al., 2005).
Acknowledgments
This work was supported by the European Commission as
part of the STARDEX (STAtistical and Regional dynamical
Downscaling of EXtremes for European regions) contract
EVK2-CT-2001-00115. The HadAM3P climate model output were provided by the Hadley Centre for Climate Prediction and Research, UK. The comments of two anonymous
reviewers were helpful in improving the quality of the paper.
References
Akima H (1984) On estimating partial derivatives for
bivariate interpolation of scattered data. Rocky Mt J
Math 14: 1
Barnett T, Preisendorfer R (1987) Multi-field analogue prediction of short-term climate fluctuations using a climate
state vector. J Atmos Sci 35: 1771–1787
Benestad RE (2001) A comparison between two empirical
downscaling strategies. Int J Climatol 21: 1645–1668
DOI: 10.1002=joc.703
Beniston M, Stephenson DB, Christensen OB, Ferro CAT,
Frei C, Goyette S, Halsnaes K, Holt T, Jylha K, Koffi B,
Palutikof J, Scholl R, Semmler T, Woth K (2005) Future
extreme events in European climate: an exploration
of regional climate model projections. ClimaticChange,
PRUDENCE special issue (submitted)
Buishand TA, Shabalova MV, Brandsma T (2004) On the
choice of the temporal aggregation level for statistical
downscaling of precipitation. J Climate 17: 1816–1827
Busuioc A, Chen D, Hellstr€
om C (2001) Performance of statistical downscaling models in GCM validation and regional climate change estimates: application for Swedish
precipitation. Int J Climatol 21: 557–578
Busuioc A, von Storch H (2003) Conditional stochastic model for generating daily precipitation time series. Climate
Res 24: 181–195
Busuioc A, Tomozeiu R, Cacciamani C (2005) Statistical
downscaling model for winter extreme precipitation events
in Emilia-Romagna region. Int J Climatol (submitted)
Cacciamani C, Nanni S, Tibaldi S (1994) Mesoclimatology
of winter temperature and precipitation in the Po Valley of
Northern Italy. Int J Climatol 14: 777–814
Cassou C, Terray L, Phillips AS (2005) Tropical Atlantic influence on European heat waves. J Climate 18: 2805–2811
Cavazos T, Comre AC, Liverm DM (2002) Inter-seasonal
variability associated with wet monsoons in southeast
Arizona. J Climate 15: 2477–2490
Christensen JH, Christensen OB (2003) Climate modelling:
severe summertime flooding in Europe. Nature 42:
805–806
Colacino M, Conte M (1995) Heat waves in the Central
Mediterranean. A synoptic climatology. Nuovo Cimento
18: 295–405
Fuentes U, Heimann D (2000) An improved statistical–
dynamical downscaling scheme and its application to
Alpine precipitation climatology. Theor Appl Climatol
65: 119–135
Giorgi F, Bi X, Pal JS (2004a) Mean, interannual variability
and trends in a regional climate change experiment over
Europe. I. Present-day climate (1961–1990). Clim Dyn
22(6=7): 733–756
Giorgi F, Bi X, Pal JS (2004b) Mean, interannual variability
and trends in a regional climate change experiment over
Europe. II. Climate change scenarios (2071–2100). Clim
Dyn 23: 839–858. DOI 10.1007=S00382-004-0467-0
Hanssen-Bauer I, Førland EJ, Haugen JE, Tveito OE (2003)
Temperature and precipitation scenarios for Norway:
comparison of results from dynamical and empirical
downscaling. Climate Res 25: 15–27
Hanssen-Bauer I, Achberger C, Benestad RE, Chen D,
Førland EJ (2005) Statistical downscaling of climate
scenarios over Scandinavia. Climate Res 29: 255–268
Hellstr€
om C, Chen D (2003) Statistical downscaling based on
dynamical downscaled predictors: application to monthly
precipitation in Sweden. Adv Atmosp Sci 20: 951–958
Huth R (2002) Statistical downscaling of the daily temperature in Central Europe. J Climate 15: 1731–1742
Huth R (2004) Sensitivity of local daily temperature change
estimates to the selection of downscaling models and
predictors. J Climate 17: 640–652
Huth R, Kysely J, Dubrovsky M (2001) Time structure of
observed, GCM-simulated, downscaled, and stochastically generated daily temperature series. J Climate 14:
4047–4061
Johns TC, Gregory JM, Ingram WJ, Johnson CE, Jones A,
Lowe JA, Mitchell JFB, Roberts DL, Sexton DMH,
Stevenson DS, Tett SFB, Woodage MJ (2003) Anthropogenic climate change for 1860 to 2100 simulated with
the HadCM3 model under updated emission scenarios.
Climate Dyn 20: 583–612
Kalnay E et al. (1996) The NCEP=NCAR 40-year – reanalysis Project. Bull Amer Meteor Soc 77: 437–472
Katz R, Parlange MB, Tebaldi C (2003) Stochastic modelling of the effects of large-scale circulation on daily
weather in the south-eastern U.S. Climate Change 60:
189–216
Murphy JM (2000) Prediction of Climate change over
Europe using statistical and dynamical downscaling techniques. Int J Climatol 20: 489–501
Palutikof JP, Goodess CM, Watkins SJ, Holt T (2002)
Generating rainfall and temperature scenarios at multiple
sites: examples from the Mediterranean. J Climate 15:
3529–3548
Pavan V, Tomozeiu R, Selvini A, Marchesi S, Marsigli C
(2003) Controllo di qualita dei dati giornalieri di temperatura minima e massima e di precipitazione. Quaderno
Tecnico ARPA-SIM 15: 66 pp
Climate change scenarios for surface temperature in Emilia-Romagna
Pavan V, Marchesi S, Morgillo A, Cacciamani C, DoblasReyes FJ (2005) Downscaling of DEMETER winter seasonal hindcasts over Northern Italy. Tellus 57A: 424–434
Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000)
The impact of new parameterizations in the Hadley Centre
model: HadAM3. Climate Dyn 16: 123–146
Räisänen J, Hansson U, Ullerstig A, D€
oscher R, Graham LP,
Jones C, Meier HEM, Samuelsson P, Willen U (2004)
European climate in the late twenty-first century:regional
simulations with two driving global models and two
forcing scenarios. Climate Dynamics 22: 13–31
Schmidli J, Frei C, Vidale PL (2006) Downscaling from
GCM precipitation: a benchmark for dynamical and
statistical downscaling. Int J Climatol (in press)
Schubert S (1998) Downscaling local extreme temperature
changes in south-eastern Australia from CSIRO Mark2
GCM. Int J Climatol 18: 1419–1438
Tomozeiu R, Pavan V, Cacciamani C, Amici M (2005)
Observed temperature changes in Emilia-Romagna: mean
values and extremes. Climate Res (in press)
Trigo RM, Palutikof JP (2001) Precipitation scenarios over
Iberia: a comparison between direct GCM output and different downscaling techniques. J Climate 14: 4422–4446
Von Storch H (1995) Spatial Patterns: EOFs and CCA. In:
von Storch H, Navarra A (eds) Analysis of climate
variability. Application of statistical techniques. Springer
pp 227–258
Von Storch H, Zorita E, Cubasch U (1993) Downscaling of
climate change estimates to regional scales: an application
to the Iberian winter time. J Climate 6: 1161–1171
Widmann M, Bretherton CS, Salathe EP Jr (2003) Statistical
precipitation downscaling over the Northern United States
using numerically simulated precipitation as a predictor.
J Climate 16: 799–816
Zorita E, von Storch H (1999) The analogue method as a
simple statistical downscaling technique: comparison with
more complicated methods. J Climate 12: 2474–2489
Authors’ addresses: Rodica Tomozeiu (e-mail: rtomozeiu@
arpa.emr.it), Carlo Cacciamani (e-mail: ccacciamani@
arpa.emr.it), Valentina Pavan (e-mail: [email protected]),
Antonella Morgillo (e-mail: [email protected]), ARPAServizio IdroMeteorologico Regionale,Viale Silvani 6,
40122 Bologna, Italy; Aristita Busuioc (e-mail: busuioc@
meteo.inmh.ro), National Meteorological Administration,
Sos. Bucuresti-Ploiesti 97, Sector 1, Bucharest, Romania.