Top ten European heatwaves since 1950 and their occurrence in the coming decades

Abstract

The Russian heatwave in 2010 killed tens of thousands of people, and was by far the worst event in Europe since at least 1950, according to recent studies and a novel universal heatwave index capturing both the duration and magnitude of heatwaves. Here, by taking an improved version of this index, namely the heat wave magnitude index daily, we rank the top ten European heatwaves that occurred in the period 1950–2014, and show the spatial distribution of the magnitude of the most recent heatwave in summer 2015. We demonstrate that all these events had a strong impact reported in historical newspapers. We further reveal that the 1972 heatwave in Finland had a comparable spatial extent and magnitude as the European heatwave of 2003, considered the second strongest heatwave of the observational era. In the next two decades (2021–2040), regional climate projections suggest that Europe experiences an enhanced probability for heatwaves comparable to or greater than the magnitude, extent and duration of the Russian heatwave in 2010. We demonstrate that the probability of experiencing a major European heatwave in the coming decades is higher in RCP8.5 than RCP4.5 even though global mean temperature projections do not differ substantially. This calls for a proactive vulnerability assessment in Europe in support of formulating heatwave adaptation strategies to reduce the adverse impacts of heatwaves.

Full text

Environ. Res. Lett. 10 (2015)124003 doi:10.1088/1748-9326/10/12/124003
LETTER
Top ten European heatwaves since 1950 and their occurrence in the
coming decades
Simone Russo
1,2
, Jana Sillmann
3
and Erich M Fischer
4
1
European Commission, Joint Research Centre, Ispra, Italy
2
Institute for Environmental Protection and Research (ISPRA), Rome, Italy
3
Center for International Climate and Environmental Research (CICERO), Pb. 1129 Blindern, N-0318 Oslo, Norway
4
Institute for Atmospheric and Climate Science, ETH Zurich, Universitatstrasse 16, 8092 Zurich, Switzerland
E-mail: simone.russo@jrc.ec.europa.eu
Keywords: heat wave magnitude index daily, extreme temperatures, European heatwaves, heat wave magnitude unit, Russian heatwave,
Finland heatwave, heatwave projections
Abstract
The Russian heatwave in 2010 killed tens of thousands of people, and was by far the worst event in
Europe since at least 1950, according to recent studies and a novel universal heatwave index capturing
both the duration and magnitude of heatwaves. Here, by taking an improved version of this index,
namely the heat wave magnitude index daily, we rank the top ten European heatwaves that occurred in
the period 1950–2014, and show the spatial distribution of the magnitude of the most recent heatwave
in summer 2015. We demonstrate that all these events had a strong impact reported in historical
newspapers. We further reveal that the 1972 heatwave in Finland had a comparable spatial extent and
magnitude as the European heatwave of 2003, considered the second strongest heatwave of the
observational era. In the next two decades (2021–2040), regional climate projections suggest that
Europe experiences an enhanced probability for heatwaves comparable to or greater than the
magnitude, extent and duration of the Russian heatwave in 2010. We demonstrate that the probability
of experiencing a major European heatwave in the coming decades is higher in RCP8.5 than RCP4.5
even though global mean temperature projections do not differ substantially. This calls for a proactive
vulnerability assessment in Europe in support of formulating heatwave adaptation strategies to reduce
the adverse impacts of heatwaves.
1. Introduction
Since 1950 large areas across Europe have experienced
many intense and long heatwaves producing notable
impacts on human mortality, regional economies, and
natural ecosystems (Meehl and Tebaldi 2004, Schär
et al 2004, García-Herrera et al 2010). So far it has been
difficult to compare them across regions, because
temperatures considered as normal by people accus-
tomed to hotter climates can be categorized as
heatwave in cooler areas if they are outside the area’s
normal temperature range (Lass et al 2011). This
problem has been overcome by percentile-based
indices (Alexander et al 2006)and by the novel heat
wave magnitude index (HWMI, Russo et al 2014)
summing up the excess temperatures beyond a certain
normalized threshold and merging duration and
temperature anomaly of intense heat events into a
single number. This enables comparison of heatwaves
with different length and peak magnitudes that have
occurred in different regions and in different years
(Hoag 2014).
However, in this study we show that the HWMI
has some limitation in assigning magnitude to very
high temperatures in particular in a changing climate.
More precisely, the one-to-one correspondence
between the sum of temperature of three consecutive
hot days (sub-heatwave)and probability saturates
when a sub-heatwave is composed by days with tem-
perature values exceeding the highest temperature
recorded during the reference period 1981–2010. The
problem of saturation could results in an under-
estimation of heatwave magnitude in a warming cli-
mate. To overcome this problem, the HWMI has been
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replaced by the heat wave magnitude index daily
(HWMId)using a different formula in assigning mag-
nitude to a single day composing a heatwave. Here we
demonstrate the strength of the HWMId in classifying
heatwaves, by identifying several historical heatwave
events across Europe based on gridded daily tempera-
ture observations. The fact that all events were well
documented in the news illustrates that, in contrast to
some previous indices, HWMId captures events that
are perceived as heatwaves by a broader public.
As documented in many studies (Schär et al 2004,
Beniston et al 2007, Fischer and Schär 2010, Barriope-
dro et al 2011, Stott et al 2011, Sillmann et al 2013,
Russo et al 2014), the increase in global surface tem-
perature is expected to alter the magnitude and the fre-
quency of heatwave events also in Europe by the end of
the century. At a global scale, area affected by heat
extremes is projected to increase already in the next
decades (Battisti and Naylor 2009, Coumou and
Robinson 2013, Fischer et al 2013)at a rate that is
strongly dependent on the emission scenario already
by mid-century. Here we focus on Europe and investi-
gate whether such near-term changes in the occur-
rence of major heatwaves are also projected for Europe
and whether the difference between different repre-
sentative concentration pathways (RCPs)scenarios are
noticeable also at the European scale.
We estimate the magnitude and the probability of
occurrence of extreme heatwaves in the near-term
(2020–2040), based on the HWMId from ten EURO-
CORDEX regional climate projections under two dif-
ferent RCPs (RCP4.5 and RCP8.5). Thereby we inves-
tigate the area affected by heatwaves such as the ones
experienced in the last decades and assess how they are
affected by different RCPs.
2. Material and methods
2.1. Heat-wave magnitude index daily (HWMId)
The HWMId, is an improvement of the HWMI defined
in Russo et al (2014).Itisdefined as the maximum
magnitude of the heatwaves in a year; where:
Heatwave: period 3consecutive days with max-
imum temperature (Tmax)above the daily threshold
for the reference period 1981–2010. The threshold is
defined as the 90th percentile of daily maxima tem-
perature, centered on a 31 day window. Hence, for a
given day d, the threshold is the 90th percentile of the
set of data A
d
defined by
AT,1
d
yid
d
yi
1981
2010
15
15
,
=
==-
+
⋃⋃ ()
where
⋃
denotes the union of sets and
T
y
i
,
is the daily
Tmax of the day iin the year y;
HWMId magnitude: sum of the magnitude of the
consecutive days composing a heatwave, with daily
magnitude calculated as follow:
MT
TT
TT TT
TT
if
0if
2
dd
dyp
yp yp
dyp
dyp
30 25
30 75 30 25
30 25
30 25
⎧
⎨
⎪
⎩
⎪
=
-
-
>
() ()
with T
d
being the maximum daily temperature on day
d of the heatwave,
T
yp30 25
and Typ30 75 are, the 25th and
75th percentile values, respectivelly, of the time series
composed of 30 year annual Tmaxs within the
reference period 1981–2010.
By definition the slope of the
M
T
dd
()
is defined at
each specific location depending on Typ30 75 and
T
yp30 25
which are different in places with different climates.
HWMId unit: the denominator of the Md func-
tion, defined as the difference between Typ30 75 and
T
yp30 25
(see equation (2)), is the inter quartile range
(IQR)of the 30 yearly Tmaxs within the reference per-
iod 1981–2010. At each specific location, it represents
a non-parametric measure of the variability of the time
series composed by annual Tmaxs in 1981–2010. If a
day of a heatwave has a temperature value
Td T yp30 75
=its corresponding magnitude value cal-
culated by means of the Md funtion will be equal to
one. Hence, a daily heatwave magnitude unit is
equivalent to that of a day with temperature Typ30 75
and a corresponding anomaly equal to the IQR of the
yearly maximua in 1981–2010. According to this defi-
nition, if the magnitude on the day d is 5, it means that
the temperature anomaly on the day d with respect to
the
T
yp30 25
is 5 times the IQR which is the definite pre-
determined heatwave magnitude unit.
2.2. HWMId applied to daily minimum
temperature (Tmin)
The definition of heatwave given in section 2.1,asdone
in many other studies (e.g. Frich et al 2002,Alexander
et al 2006), is based on daily Tmax. However, during a
heat event, an impact-relevant measurament of the
heatwave magnitude takes into account also the cooling
effect during the night (Perkins and Alexander 2013).In
this study we compare the heatwaves defined through
Tmax with the ones defined by means of the HWMI
applied to daily Tmin calculated as the HWMId but with
Tmin instead of Tmax.
2.3. HWMId versus HWMI
As reported in Russo et al (2014)the HWMI definition
is based on the division of a heatwave to sub-
heatwaves. A sub-heatwave is defined as period of
three consecutive days above the daily threshold. The
sum of three daily Tmaxs of a sub-heatwave are
transformed in probability values (sub-heatwave mag-
nitude)by means of the empirical cumulative distribu-
tion function (ECDF)function (see figure 1(b)). In the
HWMI the magnitude of a heatwave was defined as
the sum of the magnitudes of the n sub-heatwaves and
the score of the HWMI was given by the maximum
magnitude of all heatwave magnitudes for a given year.
The HWMId differs from the previous HWMI version
(Russo et al 2014)for two main improvements:
2
Environ. Res. Lett. 10 (2015)124003 S Russo et al

(i)the division in sub-heatwaves is not necessary
(ii)thedailymagnitudeisassignedbytheM
d
function
with values in
0, +¥
[
[
and not in a bounded
interval
0, 1
[]
as for the ECDF used in the HWMI.
Figure 1shows a schematic example on the calcu-
lation and comparison of the HWMId and HWMI for
two heatwaves with a duration of twelve days: one
indicated as HW1 occurred in Carcassone (Southern
France)in the summer of the 2003, and a second
namely HW2 definded by adding 5 °C at each tem-
perature value of HW1. Both heatwaves are composed
by twelve days grouped into four sub-heatwaves of
three days each (a, b, c, and d letters in figure 1). The R
code used to reproduce the example above has been
recently included in the ‘hwmi’and ‘hwmid’functions
of the R package called ‘extRemes’(Gilleland and
Katz 2011). By using the ‘hwmi’and ‘hwmid’
functions it is possible to reproduce all the calculations
illustrated in this study. As already discussed above
the HWMI, which has been the first climate indicator
merging duration and magnitude of temperature
anomalies of heat events into a single number, has
some limitaion on measuring the magnitude of sub-
heatwaves composed by high temperature values. In
particular, all the sub-heatwaves of HW2, even if com-
posed by days with different temperatures greater than
the maximum value of the summer 2003 in Carcas-
sone, have the same magnitude, corresponding to the
saturation value of 1 (see figures 1(a)and (b)). Any
other sub-heatwave with days warmer than the days of
the HW2 sub-heatwaves will still have a magnitude of
one. This problem has been overcome by the HWMId
using the non-bounded and increasing monotonic
function
M
T
dd
()
in equation (2)assigning greater
magnitude to the days of the HW2 with higher tem-
perature (see figures 1(a)and (b)).
Figure 1. HWMId and HWMI calculation at a station in Carcassonne (Southern France).(a)E-OBS time serie of the year 2003 of daily
maximum temperature in Carcassonne located in Southern France (open gray circles and blue letters)and daily threshold (black line).
The blue letters, represent the daily maximum temperature values of the 2003 heatwave experienced in Carcassonne and indicated as
HW1. The green letters, are the daily maximum temperature values of the HW2 heatwave obtained by adding 5 °C to each
temperature of the 2003 heatwave HW1. As HW1, HW2 is composed by 4 sub-heatwaves represented by green letters a, b, c, d. (b)The
dashed black line is the M
d
function used to calculate the HWMId daily magnitude (see equation (2)), the black line denotes the
empirical distribution of the sum of the three highest consecutive daily maximum temperatures of each year within the reference
period 1981–2010 used in the HWMI calculation (see Russo et al 2014). Blue and green letters refer to each single day (left)or to each
sub-heatwave (right)of the HW1 and HW2 heatwaves, respectively.
3
Environ. Res. Lett. 10 (2015)124003 S Russo et al

2.4. Models and observations
Daily maximum and minimum for continental surface
temperature data from a gridded version (E-OBS 11.0)
of the European Climate Assessment & Data (Haylock
et al 2008, ECA&D, www.ecad.eu)are applied to study
heatwaves in the present climate. The E-OBS is based
on a grid resolution of 0.25°×0.25°and the data
spans from 1950 to September 2015.
This data set is unique in its spatial extent, resolution
and the use of many more European observing stations
than in other European or global sets (Haylock
et al 2008). For future projections (period 2006–2040)
we used daily maximum and minimum temperature
from 10 high-resolution (0.11°)regional climate model
(RCM)simulations of the EURO-CORDEX (COordi-
nated Regional Downscaling EXperiment—European
Domain)multi-model scenario experiment. The model
output is interpolated on the E-OBS grid for compar-
ison with the observations. In the set of simulations, six
RCMs are driven by five different general circulation
models forced with two representative concentration
pathways (RCP4.5 and RCP8.5), adopted by the Inter-
governmental Panel on Climate Change (IPCC)for its
fifth Assessment Report (AR5, Christensen et al 2013).
For the period between 1980 and 2005 historical simula-
tions were used (Taylor et al 2012). In detail, the ensem-
ble model output used in this study is composed of the
following 10 simulations: four RCA model simulations
(Swedish Meteorological and Hydrological Institute)
forced with four global climate models (CNRM-CM5,
EC-EARTH, IPSL-CM5A-MR, and MPI-ESM-LR);two
COSMO-CLM simulations (CLM Community)driven
by lateral boundary conditions of two global climate
models (CNRM-CM5, and EC-EARTH);onesimula-
tion for RACMO (Royal Netherlands Meteorological
Institute), and one for HIRHAM5 (Danish Meteor-
ological Institute), driven by EC-EARTH global model;
one for WRF33 (Institut National de l’Environnement
Industriel et des Risques)forced with IPSL-CM5A-MR
global model and one MPI-CSC (The Max Plank Insti-
tute, Climate Service Center)simulation with the MPI-
ESM-LR. More information on these models and simu-
lations may be found at http://euro-cordex.net/
fileadmin/user_upload/eurocordex/EUROCORDEX-
simulations.pdf. The HWMId was calculated for the
period between 1981–2040 for all available simulations
and results are shown for two time slices of 30 year:
1981–2010 and 2011–2040.
2.5. Percentage of spatial area in heatwave
The percentageland area fraction in a specific year-
experiencing HWMId values greater than a given
magnitude level (HWMId

3, 4, 5, K, 24, K)
(figure 3(a)), is calculated with respect to the land area
of the entire EURO-CORDEX domain. In the specific
case of the Russian heatwave in 2010, which was partly
outside the EURO-CORDEX domain, we only count
the grid points that are inside the domain.
2.6. Probability density functions (PDFs)and
ECDFs of percentage of spatial area in heatwave
To produce the PDFs in figure 3(b)of the yearly
percentage land area with HWMId 15we use a
gaussian kernel density estimate R with only a very
weak smoothing in order to have the same informa-
tion one would see in a histogram (Fischer et al 2013).
The ECDFs in figure 3(c)are derived by sorting the
data and associating at each value a probability
calculated as the ratio between the rank of the
considered value and (N+1), where N=300 (30 years
X 10 models)is the total number of model years.
The same procedure described above has been
used to calculate the ECDF at each HWMId level. The
values of temperature anomaly shown in figure A2 are
calculated at each grid point as the difference between
the annual Tmax in a specific year and the mean value
of the time series composed by 30 year annual maxima
of the reference period 1981–2010.
2.7. Uncertainties and hypothesis testing
The calculation of the standard error of the empirical
distribution shown in figure 3(c)and for the other ECDF
composed by 300 points measuring the percentage of
area with HWMId equal to or greater than a fixed level
(HWMId 3, 4, 5,¼), has been done by means of a
bootstrap (Efron 1979). Given a set of N(in our case
N=300)model years, we draw 300 points with
replacement from the data points and compute the
median of the drawn sample. We repeat the procedure
for 10 000 times. Finally, we summarize the resulting
distribution using the median and the range of the set
composed by 10 000 medians. The error bars (see
figures 3(d),4(b)) at each HWMId level are calculated as
the range of the sample composed by 10 000 meadians.
Moreover, we have used a Kolmogorov–Smirnov (K–S)
test to verify the hypothesis that RCP8.5 has a stronger
signal than RCP4.5 in spite of the short time horizon,
and that RCP4.5 again has stronger heatwave magni-
tudes than the historical simulations.
2.8. Ranking method
The heatwave ranking method used in this study is
based on two different aspects: the percentage of area
at different HWMId levels (3, 4, 5, 6, etc K)and the
HWMId peak. Given two heatwaves HW0 and HW1,
HW0 is greater than HW1 if the percentage of area
across all HWMId levels, is greater than that of HW1.
Where the ranking is not consistent across all
HWMId levels we rank the heatwaves by the HWMId
peak value.
3. Heatwave ranking since 1950
Figure 2(a)reports the historical newspaper quotes on
the strongest European heatwaves since 1950 identi-
fied here and shows the corresponding geographical
pattern of the HWMId, from the Ensembles-
4
Environ. Res. Lett. 10 (2015)124003 S Russo et al

OBServations gridded dataset (E-OBS)(Haylock
et al 2008). The fact that all the heatwaves identified
here were covered in newspaper articles illustrates that
the index captures events that are perceived as extreme
heatwaves by the general public. Similar patterns are
obtained by using the HWMI (figure A1)and the
HWMId applied to Tmin (figure A2), the latter
confirming the severity of most of these heatwaves
which have been characterized by the persistence of
extremely high night-time temperatures. The ranking
of the most severe heatwaves has been done by
following the criterium in section 2.8.
The firstextremeeventinthisobservationalrecord
occurred in 1954 in Russia when daily Tmaxs reached
38 °C(Chicago Tribune, 11 July 1954).Wefind that the
highest HWMId values during this event were confined
to Southern Russia (figure 2(a)) with maximum values
recorded over the grid points with high heatwave anom-
aly and long persistence (figure A3).In1969tempera-
tures were above normal over the polar circle with
maximum values greater than 35 °C and HWMId peak
equal to 26.5. ‘Oslo-Eggs were fried on railway tracks
crossing the polar circle. It was a pratical demonstration
of an intense heatwave which has hit Norway for several
weeks’(Chicago Tribune, 28 June 1969).However,this
heatwave was characterized by comparatively cool
nights with HWMId applied to Tmin exceeding the level
of 3 only over a few locations in Norway (figure A2).
The magnitude of the extreme heatwave experi-
enced in Finland in 1972 is comparable with that of the
well-documented 2003 heatwave in Central Europe
(Luterbacher et al 2004, Schär et al 2004, Fischer
et al 2007, Vautard et al 2007, García-Herrera
et al 2010, Barriopedro et al 2011, Stefanon et al 2012,
Figure 2. (a)Spatial distribution of the HWMId observed (E-OBS)values and spotted news of the top 10 European heatwaves since
1950; following the definition in section 2.1, at each grid point, the HWMId values represent the yearly maximum magnitude. (b)
HWMId estimation with EOBS data from January to September 2015 for the most recent heatwave.
5
Environ. Res. Lett. 10 (2015)124003 S Russo et al

Miralles et al 2014, Russo et al 2014), but was not con-
sidered in previous catalogs of the strongest European
heatwaves (Fischer et al 2007, Vautard et al 2007, Lass
et al 2011, Stefanon et al 2012). According to news cov-
erage the weather was exceptionally warm in summer
1972 in Finland with locations recording yearly Tmax
anomalies greater than 8 °C and anomalously hot days
persisting for more than 18 days (figure A3). An excess
mortality of 840 deaths (2% of all annual deaths)in the
summer of 1972 in Finland was directly attributable to
the heatwave (Näyha 1981,2007). The heatwave spa-
tial extent, peak, and duration according to the
HWMId were greater than the previous ones in 1954
and in 1969 (table 1and figure 2(a)) and are compar-
able with the values of the heatwave in summer of
2003. In 1975–76 the UK experienced the famous
drought that was memorable for its severity over most
of the British Isles, and also for its exceptional persis-
tence. In particular, in 1976 ‘the United Kingdom
sweltered in temperatures exceeding 32.2 °C for 15
consecutive days. Further five days saw temperatures
reaching 35 °C’(The Telegraph, 22 July 2011). During
the night, as in Norway in 1969, the surface cooled
rapidly, with HWMId values greater than 3 only over
Southern UK and Northern France (figure A2).
Further major heatwaves are found across Europe
(Italy 1983, UK 1983, Greece 1987, among others)but
all of them were smaller in extent and with a lower
HWMId spatial maximum than the top ten heatwaves
presented in figure 2(a)and in table 1.
In 1994, The New York Times reported the head-
line: ‘Europe Wilts, Records Fall In Heatwave’. The
1994 heatwave was most pronounced in Germany and
Poland. Finally in the summer of 2003 the heat-related
death toll was estimated between 20 000 and 70 000
people (Robine et al 2008, Barriopedro et al 2011,
Christidis et al 2015). According to many studies the
2003 heatwave was the second strongest event in Eur-
ope since 1950 (Barriopedro et al 2011, Christidis
et al 2015). Here we show that the spatial area extent in
heatwave covered in 2003 was lower than that in 1972
at almost all HWMId levels (figures 2(a),3(a), and
table 1). On the contrary, in the 2003 the spatial area in
heatwave during the night was greater than in 1972 at
almost all HWMId levels (figures 4(a),A2). These two
events had also comparable temperature anomalies
and persistence of consecutive days above the 90th
percentile threshold (figure A3).
Another event occurred in Europe in 2006. This
heatwave had a few peaks spreading throughout Eur-
ope (figure 2(a)). In 2007 the ‘death toll rises in South-
ern Europes heatwave’(The Guardian, see figure 2(a)).
All of the previous records were broken in the 2010 in
Russia during the worst European event in the obser-
vational era. It broke the night and day records in spa-
tial extent, average, peak, and duration, in comparison
with all the previous events. In particular, the 2010
Russian heatwave shows a spatial extent and a spatial
HWMId maximum around double than that of the
heatwave in Europe in 2003 and in Finland in 1972
(table 1,figures 2(a),3(a),A2,A3).
Finally, in the 2014, as reported by Finland Times,
‘The Met Office in a twitter feed on 25 July said the
previous weeks mean temperature was the highest in
the country for more than 50 years. The mean tem-
perature in that period stood at 20.2 °C. Looking back
at the statistics, the 26th week of 1972 was the warmest
in the past 54 years’.(Finland Times, 11 August 2014).
This is confirmed by the HWMId values showing that
the Scandinavian heatwave occurred in 2014 was not
as strong as the one that occurred in 1972.
3.1. The most recent European heatwave of
summer 2015
The summer of the current year (2015)was very hot
in Europe with an intense heatwave experienced by
many countries. In Switzerland, Italy, Germany and
part of Spain, the 2015 heatwave started in late June
andatsomelocationpersistedforaround30days
until the end of July (see figure A3).InAustria,
Slovakia, Croatia, Serbia, Romania, and Southern
Ukraine the heat event started at the end of July and
persisted till the first ten days of August (figure A3)
The HWMId estimates for this heatwave have been
Table 1. List of record-breaking heatwave events in the period 1950–2014 with E-OBS data including also data until September 2015 for
the most recent heatwave. The latter is an additional information to the originally considered top 10 heatwaves. For each specific event
the spatial extent is estimated as the land area fraction exceeding a fixed HWMId value. The area fraction is expressed in percentage. The
HWMId peak is the highest spatial HWMId value recorded during each specific event.
Year Loc. HWMId Area (%)Area (%)Area (%)Area (%)
Peak HWMId 6 HWMId 9 HWMId 15 HWMId 24
2010 Russia 71.9 36.38 29.13 22.54 14.07
2003 Cent. Eu 44.7 11.61 9.17 5.44 1.65
1972 Finland 38.2 26.42 18.35 6.57 0.96
1976 UK Brit. 35.8 4.55 2.98 1.21 0.23
1969 Norway 26.5 2.26 1.20 0.38 0.02
2015 Cent. Eu 26.0 11.94 5.67 0.56 0.01
2007 Greece 22.9 16.80 7.90 1.35 0
1994 Benelux 21.3 7.42 3.89 0.46 0
2014 Scandin. 21.2 11.58 3.65 0.3 0
1954 SW Rus. 19.7 9.3 1.9 0.05 0
2006 Cent. Eu 18.9 5.05 1.28 0.05 0
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Environ. Res. Lett. 10 (2015)124003 S Russo et al

done by using the E-OBS Tmax and Tmin data (see
section 2.4)available until September 2015. Accord-
ing with the HWMId spatial distribution during the
day (see figure 2(b)and table 1)this heatwave had
lower magnitude than that occurred in the summer
of 2003. Its largest anomaly and duration were
recorded in Northern Italy and Swizerland (see
figure A3)anditsspatialextentatdifferentHWMId
levels was comparable withthe one of the heatwaves
occurred in Greece in 2007, in central Europe in
1994 and in Scandinavian in 2014 (see table 1).
Differently from these two events the heatwave of
summer 2015 was characterized by a slow cooling
during the night (see figure A2).
Figure 3. Empirical probability and cumulative distirbution functions (EPDFs and ECDFs)of the percentage land area in heatwaves. (a)
Percentage of EURO-CORDEX land area covered by HWMId maximum values greater than a given magnitude level (HWMId

6, 9, K,
24)for the 2003, 1972, and 2010 European heatwaves withE-OBS data (black)and for the strongest heatwave simulated by each individual
modelin the periods1981–2010 and 2011–2040.The numbers from 0 to 9 refer to heatwaves simulated by individual models (see table A.1).
Hence, as an example, at each HWMId level the heatwave with the strongest magnitude and spatial extent simulated by the model 0, in
2011–2040 and under the RCP8.5 scenario, is represented by the red symbol 0. (b)PDFsof the percentage land area covered by grid points
with HWMId values equal to or greater than 15. The PDFs are shown for the period 1981–2010 (blue curves)and 2011–2040 under RCP4.5
(green curves)and RCP8.5 (red curves)scenarios.The transparent couloured lines markindividualEURO-CORDEX models,comprisedof
30 year values, whereas the bold colored curves represent the PDFs comprised of all 300 (30 years X 10 models)mo delyears. Dotted and
dashed lines denote the observed (E-OBS)2003 and 2010 events, respectively. (c)As (b)but for ECDF of 300 model years. Each open circle
represent the percentage of area (value on x-axis)and the corresponding probability for a single model year. The horizontal dashed and
dotted lines denote the probability to have 2003 and 2010 type heatwave in 30 year period and under a specific scenario. (d)Percentage of
area versus different HWMId levels. The open colored circles and the error bars represent the median and the range of the bootstrap applied
at each HWMId level to the ECDF of 300 model years (see panel (c)forHWMId

15).(e)Occurrence of a 2003-type heatwave in present
and near future periods of 30 year simulated by each model according to the ranking method in section 2.8.
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Environ. Res. Lett. 10 (2015)124003 S Russo et al

4. Model evaluation (1981–2010)
Weuseanensembleof10 RCMs(see methods)to
evaluate the simulated heatwave magnitude and extent
against observations. Heatwaves in the same set of RCMs
but driven by reanalysis data, have been evaluated in a
recent study (Vautard et al 2013)showing that most
modelsexhibitanoverestimationofsummertime
observed temperature extremes in Mediterranian
regions and an underestimation over Scandinavia. They
show that if heatwaves in the model are defined with
respect to the observed percentiles, their persistence is
directly influenced by this bias. In fact, a model that
overestimates the 90th percentile threshold of the
simulated temperature with respect to the observations
will show an obvious overestimation of heatwave
duration. This effect is corrected in the previous study
(Vautard et al 2013)and here by HWMId defining a
heatwave as a period of consecutive hot days with daily
Tmax above the 90th percentile threshold of the
respective model and not with respect to the observa-
tions. The implicit bias correction included in HWMId
calculation, gives high skill to this index in comparing
heatwaves simulated by different climate models. All the
models simulate heatwaves in northern, Southern, East-
ern, and Western Europe and in particular over the
regions where the top ten heatwaves occurred in the
present (figure 5(a)).
But are the models able to simulate heatwaves of the
magnitude of those in 2003 and 2010 to provide a reli-
able estimate of future summer climate (Beniston 2004,
Fischer and Schär 2010, Diffenbaugh and Scherer 2011,
Quesada et al 2012)? The simulated HWMId values in a
thirty year period (1981–2010)representing present-day
climate show that many models at different HWMId
levels capture a 2003-type heatwave in the period
1981–2010, withonly one out of ten models simulating,
at each HWMId level, a heatwave greater in spatial
extent than observed in the 2003 (figures 3(a),5(a):
model4 ). The corresponding HWMId applied to Tmin
for the heatwaves in figure 3(a)show similar results,
with three out of ten models simulating, at each
HWMId level, a heatwave greater in spatial extent than
observed in the 2003 (figures 4(a)and (c),seemodels1,
4, 7). None of the models is able to capture the spatial
extent of the 2010 Russian heatwave at anyone of the
HWMId level. (see figures 3(a),5(a)). When using daily
Tmin, only one model (figure 4(a),seemodel4)at dif-
ferent magnitude levels shows a spatial area greater than
that measured in the 2010.
5. Heatwaves in the next two decades
While global mean temperatures differ not substan-
tially between the RCP8.5 and RCP4.5 scenarios in the
Figure 4. As figures 3(a),(d),(e)but for HWMId applied to Tmin (see section 2.2).(a)The strongest heatwave in 30 year period with
Tmin simulated by each model (0, 1, 2, etc K)are coherent in space and time with those in figure 3(a).(For more details see also
figures 5and A4).(b)and (c)Colors indicate the same periods and scenarios as in figure 3(a).
8
Environ. Res. Lett. 10 (2015)124003 S Russo et al

coming two decades (Collins et al 2013)we find that
the probability of experiencing a major European
heatwave is increasing and is larger in theRCP8.5 than
RCP4.5.
It is expected that along with warming tempera-
tures a significant percentage of land fraction will see
significantly more intense hot extremes (Clark
et al 2006, Fischer et al 2013), with a probability of
occurrence of extreme heatwaves increasing by a fac-
tor of 5 to 10 (Beniston et al 2007, Barriopedro
et al 2011, Rahmstorf and Coumou 2011, Coumou
and Robinson 2013, Christidis et al 2015). However,
Figure 5. Spatial distribution of the HWMId values of the strongest heatwave in a 30 year period simulated by each of the ten models in
a specific model-year. (a)present period (1981–2010)in the historical simulations (1981–2005)and RCP4.5 (2006–2010).(b)and (c)
Next two decades (2011–2040)under the RCP4.5 and RCP8.5 scenarios, respectively.
9
Environ. Res. Lett. 10 (2015)124003 S Russo et al

Barriopedro et al (2011)have shown that the 2010
heatwave was so extreme that analogues will remain
unusual for the next few decades under the A1B IPCC
scenario (Christensen et al 2007). Likewise, we find
that, following the ranking method in section 2.8
based on the HWMId calculated with daily Tmaxs,
none of the 10 RCMs show a 2010-type heatwave in
Europe under the RCP4.5 (Christensen et al 2013)
(figure 2(a)), whereas three of the ten models show one
under the RCP8.5 (figures 3(a),5(c): models 3, 5, 6).
These three heatwaves, at the same time and locations,
show very high magnitude also during the night; with
a percentage of area, measured at each magnitude
level, greater than that recorded in Russia in the 2010
(figures 4(a),A4(c))
Moreover, almost all the models show that the
probability of occurrence of an event like that of sum-
mer 2003 is expected to increase in the coming dec-
ades, occurring at least once in 30 year period under
both RCP4.5 and RCP8.5 scenarios, (figures 3(a)and
(e),4(a)and (c),5(b)and (c),A4 ). This signal, accord-
ingly with at least 8 of the 10 models is greater under
the RCP8.5 than the RCP4.5 (figures 3(e),4(c)).
However, since we are using only one realization
per GCM-RCM chain, we are not able to estimate a
range of uncertainty expressing the likelihood of each
single model in capturing a specific type of heatwave.
Furthermore, note that in case that the absence of a
2010 heatwave in the period 1981–2010 is due to a
model deficiency, the occurrence in the coming dec-
ades may also be biased low.
The PDF and the corresponding ECDF of the spa-
tial area covered by grid points with HWMId equal to
or greater than 15 (value exceeded by all the HWMId
peak values of the top ten present heatwaves in table 1),
show that in the next decades (2011–2040)the fraction
of European area in heatwave is increasing with
respect to the present-day period (1981–2010)under
both the RCP4.5 and RCP8.5 scenarios (figures 3(b)
and (c)). In order to test the uncertainties and the
robustness of these rusults we have applied a bootstrap
of 10 000 samples to the ECDFs calculated at each level
of magnitude. The median and the range of the set
composed by 10 000 ECDF medians versus the magni-
tude levels are represented in figures 3(d)and 4(b)for
HWMId calculated with daily maximum and Tmins,
respectively. The K–S test (see section 2.7)applied to
the set composed by the 10 000 ECDF medians, calcu-
lated at each magnitude level, show that the null
hypothesis that RCP4.5 is equal to the historical simu-
lation is rejected at 1% level of significance in favor of
the alternative hypothesis that RCP4.5 show a greater
signal than historical simulations. The same test
applied to the HWMId, with Tmax and Tmin data, in
the coming decades shows that, at 1% level of sig-
nificance, in the period 2011–2040 the expected per-
centage of area will be greater under the RCP8.5 than
the RCP4.5 scenario, indicating that a small change
in average global temperature leads to a dramatic
change in intensity and frequency of extreme events
(Karl et al 2008). Finally, in the very near future the
strongest heatwave may occur anywhere in Europe
(figures 5(b)and (c),A4(b)and (c)), indicating that
respective adaptation strategies are needed in all Eur-
opean countries.
6. Conclusions
Our results provide a formal classification of the
strongest heatwaves occurring in Europe since 1950,
showing that according to our newly introduced metric
the heatwave occurring in Finland in 1972 had larger
spatial extent but smaller peak magnitude and duration
as the one in 2003 in Central Europe. The RCMs show
reasonable skill in simulating present-day extreme
heatwaves, and indicate that anthropogenic increase in
greenhouse gas concentrations implies an increased
probability of extreme heatwaves in Europe in the next
two decades (2021–2040). This enhanced probability of
extreme heatwaves may regionally be masked or ampli-
fied by internal variability, which means that parts of
Europe may experience a very rapid increase or no
increase in heatwaves for 1–2 decades. However, in the
long run heatwaves that are unusual in the current
climate will become more common along with rising
global mean temperatures and could occur in any
country in Europe. Given the documented effects of the
top ten heatwaves in the present, a high risk of associated
adverseimpactsinthenearfutureissuggested.Thenew
HWMId, which takes into account the severity of
temperature extremes as well as the duration of the
heatwave, and which also solves the saturation problem
found in the HWMI, promises to be very useful in
classifying future heatwaves and in providing significant
information for adaptation strategies to decision-
makers (Hoag 2014).
Acknowledgments
We acknowledge the E-OBS dataset from the EU-FP6
project ENSEMBLES (http://ensembles-eu.metoffice.
com)and the data providers in the ECA&D project
(http://ecad.eu). We acknowledge the Task Force for
Regional Climate Downscaling (TFRCD)of the World
Climate Research Programme (WCRP), which created
theCORDEXinitiativetogenerateregionalclimate
change projections for Europe within the timeline of the
Fifth Assessment Report (AR5)and beyond (http://
euro-cordex.net/About-EURO-CORDEX.1864.0.
html). JS is supported by ClimateXL (project no.243953)
funded through the Norwegian Research Council.
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Appendix
July 1954
June 1969
July 1972
July 1976
Aug 1994
0234816
HWMI
Aug 2003
July 2006
July 2007
July 2010
Aug. 2014
Figure A1. Spatial distribution of the HWMI observed (E-OBS)values of the top 10 European heatwaves since 1950.
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Environ. Res. Lett. 10 (2015)124003 S Russo et al

Figure A2. As figure 2but for HWMId calculated with daily minimum temperatures.
12
Environ. Res. Lett. 10 (2015)124003 S Russo et al

Figure A3. Temperature anomaly calculated following the definition in section 2.6 and heatwave duration of the top 10 European
heatwaves and of the most recent heatwave of summer 2015.
Table A.1. List of the ten EURO-CORDEX used models.
Model number Institute RCM Driving GCM
0 SMHI RCA4 CNRM-CM5
1 CLMcom CCLM4-8-17 CNRM-CM5
2 CLMcom CCLM4-8-17 EC-EARTH
3 SMHI RCA4 EC-EARTH
4 KNMI RACMO22E EC-EARTH
5 DMI HIRHAM5 EC-EARTH
6 IPSL-INERIS WRF331F IPSL-CM5A-MR
7 SMHI RCA4 IPSL-CM5A-MR
8 MPI-CSC REMO2009 MPI-ESM-LR
9 SMHI RCA4 MPI-ESM-LR
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