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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.
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 $\geqslant $ ≥ > 6, 9, … , 24) for the 2003, 1972, and 2010 European heatwaves with E-OBS data (black) and for the strongest heatwave simulated by each individual model in the periods 1981–2010 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) PDFs of 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 mark individual EURO-CORDEX models, comprised of 30 year values, whereas the bold colored curves represent the PDFs comprised of all 300 (30 years X 10 models) model years. 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) for HWMId $\geqslant $ ≥ > 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 doi:10.1088/1748-9326/10/12/124003
Top ten European heatwaves since 1950 and their occurrence in the
coming decades
Simone Russo
, Jana Sillmann
and Erich M Fischer
European Commission, Joint Research Centre, Ispra, Italy
Institute for Environmental Protection and Research (ISPRA), Rome, Italy
Center for International Climate and Environmental Research (CICERO), Pb. 1129 Blindern, N-0318 Oslo, Norway
Institute for Atmospheric and Climate Science, ETH Zurich, Universitatstrasse 16, 8092 Zurich, Switzerland
Keywords: heat wave magnitude index daily, extreme temperatures, European heatwaves, heat wave magnitude unit, Russian heatwave,
Finland heatwave, heatwave projections
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 19502014, 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 (20212040), 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
difcult 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 areas
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 19812010. 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
25 August 2015
20 October 2015
2 November 2015
27 November 2015
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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
(20202040), 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 dened
in Russo et al (2014).Itisdened as the maximum
magnitude of the heatwaves in a year; where:
Heatwave: period 3consecutive days with max-
imum temperature (Tmax)above the daily threshold
for the reference period 19812010. The threshold is
dened 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
dened by
⋃⋃ ()
denotes the union of sets and
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:
yp yp
30 25
30 75 30 25
30 25
30 25
() ()
with T
being the maximum daily temperature on day
d of the heatwave,
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 19812010.
By denition the slope of the
is dened at
each specic location depending on Typ30 75 and
yp30 25
which are different in places with different climates.
HWMId unit: the denominator of the Md func-
tion, dened as the difference between Typ30 75 and
yp30 25
(see equation (2)), is the inter quartile range
(IQR)of the 30 yearly Tmaxs within the reference per-
iod 19812010. At each specic location, it represents
a non-parametric measure of the variability of the time
series composed by annual Tmaxs in 19812010. 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 19812010. According to this de-
nition, if the magnitude on the day d is 5, it means that
the temperature anomaly on the day d with respect to
yp30 25
is 5 times the IQR which is the denite pre-
determined heatwave magnitude unit.
2.2. HWMId applied to daily minimum
temperature (Tmin)
The denition 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 dened through
Tmax with the ones dened 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 denition
is based on the division of a heatwave to sub-
heatwaves. A sub-heatwave is dened 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 gure 1(b)). In the
HWMI the magnitude of a heatwave was dened 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:
Environ. Res. Lett. 10 (2015)124003 S Russo et al
(i)the division in sub-heatwaves is not necessary
with values in
and not in a bounded
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 dended 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 gure 1). The R
code used to reproduce the example above has been
recently included in the hwmiand hwmidfunctions
of the R package called extRemes(Gilleland and
Katz 2011). By using the hwmiand hwmid
functions it is possible to reproduce all the calculations
illustrated in this study. As already discussed above
the HWMI, which has been the rst 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 gures 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
in equation (2)assigning greater
magnitude to the days of the HW2 with higher tem-
perature (see gures 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
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 19812010 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.
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, 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 20062040)
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 EXperimentEuropean
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 ve 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
fth 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,
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 lEnvironnement
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
simulations.pdf. The HWMId was calculated for the
period between 19812040 for all available simulations
and results are shown for two time slices of 30 year:
19812010 and 20112040.
2.5. Percentage of spatial area in heatwave
The percentageland area fraction in a specic year-
experiencing HWMId values greater than a given
magnitude level (HWMId
3, 4, 5, K, 24, K)
(gure 3(a)), is calculated with respect to the land area
of the entire EURO-CORDEX domain. In the specic
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 gure 3(b)of the yearly
percentage land area with HWMId 15we 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 gure 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 gure A2 are
calculated at each grid point as the difference between
the annual Tmax in a specic year and the mean value
of the time series composed by 30 year annual maxima
of the reference period 19812010.
2.7. Uncertainties and hypothesis testing
The calculation of the standard error of the empirical
distribution shown in gure 3(c)and for the other ECDF
composed by 300 points measuring the percentage of
area with HWMId equal to or greater than a xed 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
gures 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 KolmogorovSmirnov (KS)
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-
ed here and shows the corresponding geographical
pattern of the HWMId, from the Ensembles-
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 identied
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 (gure A1)and the
HWMId applied to Tmin (gure A2), the latter
conrming 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 rstextremeeventinthisobservationalrecord
occurred in 1954 in Russia when daily Tmaxs reached
38 °C(Chicago Tribune, 11 July 1954).Wend that the
highest HWMId values during this event were conned
to Southern Russia (gure 2(a)) with maximum values
recorded over the grid points with high heatwave anom-
aly and long persistence (gure 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 (gure 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 denition 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.
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 (gure 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 gure 2(a)) and are compar-
able with the values of the heatwave in summer of
2003. In 197576 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 ve 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 (gure 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 gure 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 (gures 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 (gures 4(a),A2). These two
events had also comparable temperature anomalies
and persistence of consecutive days above the 90th
percentile threshold (gure A3).
Another event occurred in Europe in 2006. This
heatwave had a few peaks spreading throughout Eur-
ope (gure 2(a)). In 2007 the death toll rises in South-
ern Europes heatwave(The Guardian, see gure 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,gures 2(a),3(a),A2,A3).
Finally, in the 2014, as reported by Finland Times,
The Met Ofce 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 conrmed 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
until the end of July (see gure A3).InAustria,
Slovakia, Croatia, Serbia, Romania, and Southern
Ukraine the heat event started at the end of July and
persisted till the rst ten days of August (gure A3)
The HWMId estimates for this heatwave have been
Table 1. List of record-breaking heatwave events in the period 19502014 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 specic event
the spatial extent is estimated as the land area fraction exceeding a xed HWMId value. The area fraction is expressed in percentage. The
HWMId peak is the highest spatial HWMId value recorded during each specic 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
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 gure 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
gure 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 gure 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 periods19812010 and 20112040.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
20112040 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 19812010 (blue curves)and 20112040 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 specic 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.
Environ. Res. Lett. 10 (2015)124003 S Russo et al
4. Model evaluation (19812010)
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
observed temperature extremes in Mediterranian
regions and an underestimation over Scandinavia. They
show that if heatwaves in the model are dened with
respect to the observed percentiles, their persistence is
directly inuenced 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 dening 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 (gure 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 (19812010)representing present-day
climate show that many models at different HWMId
levels capture a 2003-type heatwave in the period
19812010, withonly one out of ten models simulating,
at each HWMId level, a heatwave greater in spatial
extent than observed in the 2003 (gures 3(a),5(a):
model4 ). The corresponding HWMId applied to Tmin
for the heatwaves in gure 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 (gures 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 gures 3(a),5(a)). When using daily
Tmin, only one model (gure 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 gures 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 gure 3(a).(For more details see also
gures 5and A4).(b)and (c)Colors indicate the same periods and scenarios as in gure 3(a).
Environ. Res. Lett. 10 (2015)124003 S Russo et al
coming two decades (Collins et al 2013)we nd that
the probability of experiencing a major European
heatwave is increasing and is larger in theRCP8.5 than
It is expected that along with warming tempera-
tures a signicant percentage of land fraction will see
signicantly 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 specic model-year. (a)present period (19812010)in the historical simulations (19812005)and RCP4.5 (20062010).(b)and (c)
Next two decades (20112040)under the RCP4.5 and RCP8.5 scenarios, respectively.
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 nd
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)
(gure 2(a)), whereas three of the ten models show one
under the RCP8.5 (gures 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
(gures 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, (gures 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 (gures 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 specic type of heatwave.
Furthermore, note that in case that the absence of a
2010 heatwave in the period 19812010 is due to a
model deciency, 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 (20112040)the fraction
of European area in heatwave is increasing with
respect to the present-day period (19812010)under
both the RCP4.5 and RCP8.5 scenarios (gures 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 gures 3(d)and 4(b)for
HWMId calculated with daily maximum and Tmins,
respectively. The KS 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 signicance 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-
nicance, in the period 20112040 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
(gures 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 classication 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 (20212040). This enhanced probability of
extreme heatwaves may regionally be masked or ampli-
ed by internal variability, which means that parts of
Europe may experience a very rapid increase or no
increase in heatwaves for 12 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
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 signicant
information for adaptation strategies to decision-
makers (Hoag 2014).
We acknowledge the E-OBS dataset from the EU-FP6
project ENSEMBLES (http://ensembles-eu.metofce.
com)and the data providers in the ECA&D project
( We acknowledge the Task Force for
Regional Climate Downscaling (TFRCD)of the World
Climate Research Programme (WCRP), which created
change projections for Europe within the timeline of the
Fifth Assessment Report (AR5)and beyond (http://
html). JS is supported by ClimateXL (project no.243953)
funded through the Norwegian Research Council.
Environ. Res. Lett. 10 (2015)124003 S Russo et al
July 1954
June 1969
July 1972
July 1976
Aug 1994
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.
Environ. Res. Lett. 10 (2015)124003 S Russo et al
Figure A2. As gure 2but for HWMId calculated with daily minimum temperatures.
Environ. Res. Lett. 10 (2015)124003 S Russo et al
Figure A3. Temperature anomaly calculated following the denition in section 2.6 and heatwave duration of the top 10 European
heatwaves and of the most recent heatwave of summer 2015.
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... • July 2006 -a record-breaking heat wave over western Europe (Rebetez et al., 2009;Russo et al., 2015); ...
... • August 2003 -a record-breaking heat wave over Europe (Met Office, 2003;Stott et al., 2004;Rebetez et al., 2009;Russo et al., 2015); and ...
... • August 1994 -a severe heat wave over Central Europe (Russo et al., 2015;Climate Change Post, 2018). ...
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Heat events may be humid or dry. While several indices incorporate humidity, such combined indices obscure identification and exploration of heat events by their different humidity characteristics. The new HadISDH.extremes global gridded monitoring product uniquely provides a range of wet and dry bulb temperature extremes indices. Analysis of this new data product demonstrates its value as a tool for quantifying exposure to humid verses dry heat events. It also enables exploration into “stealth heat events”, where humidity is high, perhaps enough to affect productivity and health, while temperature remains moderate. Such events may not typically be identified as “heat events” by temperature-focused heat indices. Over 1973–2022, the peak magnitude of humid extremes (maximum daily wet bulb temperature over a month; T w X) for the global annual mean increased significantly at 0.13 ± 0.04°C (10 yr) ⁻¹ , which is slightly slower than the global annual mean T w increase of 0.22± 0.04°C (10 yr) ⁻¹ . The frequency of moderate humid extreme events per year (90th percentile daily maxima wet bulb temperature exceedance; T w X90p) also increased significantly at 4.61 ± 1.07 d yr ⁻¹ (10 yr) ⁻¹ . These rates were slower than for temperature extremes, TX and TX90p, which respectively increased significantly at 0.27 ± 0.04°C (10 yr) ⁻¹ and 5.53 ± 0.72 d yr ⁻¹ (10 yr) ⁻¹ . Similarly, for the UK/Europe focus region, JJA-mean T w X increased significantly, again at a slower rate than for TX and mean T w . HadISDH.extremes shows some evidence of “stealth heat events” occurring where humidity is high but temperature remains more moderate.
... As an univocal and optimal definition of heat wave index is still under debate (Perkins and Alexander 2013), here we use the Heat Wave Magnitude Index daily (HWMId, Russo et al 2015), designed to take into account both heat wave duration and intensity. The HWMId is defined as the maximum magnitude (equation (1)) of all the heatwaves occurring in a period (summer in this case), where a heatwave is defined to occur when at least three consecutive days have maximum temperature Td above the calendar 90th percentile centred on a 31 d window for the reference period . ...
... Historically well documented heat waves (1972,2003,2007,2010,2022, see e.g. Russo et al 2015) are satisfactorily captured, as well as the most severe droughts (such as those in 1955, 1976, 2003, 2015, 2018, 2022 ...
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Climate change has a strong impact on the environment in mountain areas, where ecosystems have adapted to climatic conditions that change with elevation. In this study, the response of temperature and precipitation climatic indices in the complex orography setting of the Great Alpine Region is discussed. The high‐resolution gridded dataset that is presented has been produced with the Weather Research Forecasting (WRF) convection permitting regional model. Two 30‐year periods have been considered (1979–2008, 2039–2068), obtained by downscaling global climate simulations with historical setting and RCP8.5 emission scenario. Both daily temperature and precipitation statistics have been found to be significantly different in the two periods, consisting in an overall projected warming and drying of the region. The dependence of the projected changes on elevation is highlighted, indicating a larger warming at medium and high elevations as well as a limited or nonexisting drying at high elevations. Physical mechanisms at the base of those differences are presented and discussed.
... To assess the impact of extreme weather, we calculated a water balance index and a heat wave index. For this, we used the Standardized Precipitation Evaporation Index (SPEI; Vicente Serrano et al., 2010), and the heat wave index (HWI) defined by Russo et al. (2015). Both indices have the advantage that they allow for comparisons between different regions and between years. ...
... where T d is the maximum daily temperature on day d during the heatwave. T 30y25p are the 25th and T 30y75p the 75th percentile values of T max from the 30 year reference period (Russo et al., 2015). The HWMId function in the package extRemes (Gilleland, 2022) was used to obtain the HWI. ...
... The last 2 decades have been characterized by an increased number of summer heat waves (HWs), some of them of unprecedented magnitude and impact (e.g., Schär and Jendritzky, 2004;García-Herrera et al., 2010;Barriopedro et al., 2011;Russo et al., 2015). HWs are the most visible sign of ongoing global warming in central Europe (IPCC, 2023), which lead to an increased awareness in our society and stakeholders (Lee et al., 2015;Moser, 2016). ...
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Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of a unique high-resolution, convection-permitting (2.8 m), multi-GCM (global climate model) ensemble with COSMO-CLM (Consortium for Small-scale Modeling Climate Limited-area Modelling) regional simulations (1971–2100) over Germany regarding heat extremes and related impacts. We find a systematically reduced cold bias especially in summer in the convection-permitting simulations compared to the driving simulations with a grid size of 7 km and parametrized convection. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2 ∘C (3 ∘C) warmer world, a 26 % (100 %) increase in the heat wave magnitude index is anticipated. Human heat stress (universal thermal climate index (UTCI) > 32 ∘C) and region-specific parameters tailored to climate adaptation revealed a dependency on the major landscapes, resulting in significantly higher heat exposure in flat regions such as the Rhine Valley, accompanied by the strongest absolute increase. A nonlinear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI > 32 ∘C, tropical nights, very hot days). Providing region-specific and tailored climate information, we demonstrate the potential of convection-permitting simulations to facilitate improved impact studies and narrow the gap between climate modeling and stakeholder requirements for climate adaptation.
... Regions prone to hit by heat wave,including North America (Wanyama et al. 2023), Europe (Russo et al. 2015), East Asia , Africa (Engdaw et al. 2022), India (Panda et al. 2017), Southeast Asia (Dong et al. 2021) and Australia (Jyoteeshkumar reddy et al. 2021), have received considerable attention in previous studies regarding the spatio-temporal variations of heat wave frequency, duration and intensity (Perkins 2015;Perkins and Alexander 2013). However, heat wave research in some areas, like Africa and Southeast Asia, is hampered by the lack of observational data (Li 2020;Meque et al. 2022). ...
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Compound heat wave (CompoundHW) has attracted extensive attention for its prolonged extreme heat from daytime to nighttime during its process. However, the performance of identifying and characterizing CompoundHW across different datasets has not been systematically evaluated. Here, we compared the similarities and differences of the ERA5, Berkeley Earth, CHIRTS and CPC datasets in identifying and characterizing CompoundHW. Results showed that the match of CompoundHW identification between datasets was consistent in both temporal and spatial dimensions, with the highest match observed between the ERA5 and CHIRTS datasets. Match of CompoundHW identification exhibited significant correlation with the density of observation stations, with matching rates above 50% in regions with dense observation networks, but extremely low match in regions with sparse data coverage. The rising trends of the CompoundHW metrics were captured by all datasets, especially in parts of North America, Europe, western Russia and Asia. Despite differences in the amplitude of CompoundHW changes across the four datasets, over 42% of global regions concurred on the changes in CompoundHW frequency, duration, and magnitude, and more than 27% agreed on the changes in the proportion of CompoundHW occurrences. Inconsistencies of CompoundHW changes were predominantly observed in regions with low matching rates, indicating that precise identification of CompoundHW is the basis for characterizing the changes in CompoudHW characteristics accurately. This study highlights the importance of multiple datasets comparison in heat wave research, especially in metrics defined by multiple climate variables and regions with sparse observational data.
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The heatwave in 2022 in South Asia disrupted the lives of millions of people and posed challenges to human health, energy, water, and food security. However, mega heatwaves' causes, impacts, and occurrence (like in 2022) remain largely unrecognized. Here, we analyzed the 2022 heatwave, its mechanisms, and future likelihood using observational datasets and climate model simulations. In the last few years, the frequency and duration of heatwaves have significantly increased in South Asia. South Asia faced five continuous heatwave spells that lasted about 35 days during late February and April 2022, affecting a large part of the region. The year 2022 heatwave was unprecedented that caused a deficit in soil moisture and crop yield. Moreover, our results show that the excessive radiative heating of arid and semi-arid regions resulted in a high geopotential height and low pressure in South Asia during the 2022 mega heatwave. The climate model simulations show that such mega heatwaves are projected to become more frequent under the warmer world, and their time of emergence could be as early as the 2030s under the highest emission scenario. Earlier occurrences of mega heatwaves in the future will pose considerable adaptation challenges for food and water security in the region.
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interview on HWMI index published in the Journal of Geophysical Research in Oct. 2014
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An extreme heat wave occurred in Russia in the summer of 2010. It had serious impacts on humans and natural ecosystems, it was the strongest recorded globally in recent decades and exceeded in amplitude and spatial extent the previous hottest European summer in 2003. Earlier studies have not succeeded in comparing the magnitude of heat waves across continents and in time. This study introduces a new Heat Wave Magnitude Index (HWMI) that can be compared over space and time. The index is based on the analysis of daily maximum temperature, in order to classify the strongest heat waves that occurred worldwide during the three study periods 1980-1990, 1991-2001 and 2002-2012. In addition, multi-model ensemble outputs from the Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves, under different Representative Concentration Pathways (RCP), adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report (AR5). Results show that the percentage of global area affected by heat waves has increased in recent decades. Moreover, model predictions reveal an increase in the probability of occurrence of extreme and very extreme heat waves in the coming years: in particular, by the end of this century, under the most severe IPCC AR5 scenario, events of the same severity as that in Russia in the summer of 2010 will become the norm and are projected to occur as often as every two years for regions such as southern Europe, North America, South America, Africa and Indonesia.
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The recent European mega-heatwaves of 2003 and 2010 broke temperature records across Europe1, 2, 3, 4, 5. Although events of this magnitude were unprecedented from a historical perspective, they are expected to become common by the end of the century6, 7. However, our understanding of extreme heatwave events is limited and their representation in climate models remains imperfect8. Here we investigate the physical processes underlying recent mega-heatwaves using satellite and balloon measurements of land and atmospheric conditions from the summers of 2003 in France and 2010 in Russia, in combination with a soil–water–atmosphere model. We find that, in both events, persistent atmospheric pressure patterns induced land–atmosphere feedbacks that led to extreme temperatures. During daytime, heat was supplied by large-scale horizontal advection, warming of an increasingly desiccated land surface and enhanced entrainment of warm air into the atmospheric boundary layer. Overnight, the heat generated during the day was preserved in an anomalous kilometres-deep atmospheric layer located several hundred metres above the surface, available to re-enter the atmospheric boundary layer during the next diurnal cycle. This resulted in a progressive accumulation of heat over several days, which enhanced soil desiccation and led to further escalation in air temperatures. Our findings suggest that the extreme temperatures in mega-heatwaves can be explained by the combined multi-day memory of the land surface and the atmospheric boundary layer.
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Many climatic extremes are changing, and decision-makers express a strong need for reliable information on further changes over the coming decades as a basis for adaptation strategies. Here, we demonstrate that for extremes stakeholders will have to deal with large irreducible uncertainties on local to regional scales as a result of internal variability, even if climate models improve rapidly. A multimember initial condition ensemble carried out with an Earth system model shows that trends towards more intense hot and less intense cold extremes may be masked or even reversed locally for the coming three to five decades even if greenhouse gas emissions rapidly increase. Likewise, despite a long-term trend towards more intense precipitation and longer dry spells, multidecadal trends of opposite sign cannot be excluded over many land points. However, extremes may dramatically change at a rate much larger than anticipated from the long-term signal. Despite these large irreducible uncertainties on the local scale, projections are remarkably consistent from an aggregated spatial probability perspective. Models agree that within only three decades about half of the land fraction will see significantly more intense hot extremes. We show that even in the short term the land fraction experiencing more intense precipitation events is larger than expected from internal variability. The proposed perspective yields valuable information for decision-makers and stakeholders at the international level.
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This study provides an overview of projected changes in climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The temperature- and precipitation-based indices are computed with a consistent methodology for climate change simulations using different emission scenarios in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) multimodel ensembles. We analyze changes in the indices on global and regional scales over the 21st century relative to the reference period 1981–2000. In general, changes in indices based on daily minimum temperatures are found to be more pronounced than in indices based on daily maximum temperatures. Extreme precipitation generally increases faster than total wet-day precipitation. In regions, such as Australia, Central America, South Africa, and the Mediterranean, increases in consecutive dry days coincide with decreases in heavy precipitation days and maximum consecutive 5 day precipitation, which indicates future intensification of dry conditions. Particularly for the precipitation-based indices, there can be a wide disagreement about the sign of change between the models in some regions. Changes in temperature and precipitation indices are most pronounced under RCP8.5, with projected changes exceeding those discussed in previous studies based on SRES scenarios. The complete set of indices is made available via the ETCCDI indices archive to encourage further studies on the various aspects of changes in extremes.
Socio-economic stress from the unequivocal warming of the global climate system could be mostly felt by societies through weather and climate extremes. The vulnerability of European citizens was made evident during the summer heatwave of 2003 (refs,) when the heat-related death toll ran into tens of thousands. Human influence at least doubled the chances of the event according to the first formal event attribution study, which also made the ominous forecast that severe heatwaves could become commonplace by the 2040s. Here we investigate how the likelihood of having another extremely hot summer in one of the worst affected parts of Europe has changed ten years after the original study was published, given an observed summer temperature increase of 0.81 K since then. Our analysis benefits from the availability of new observations and data from several new models. Using a previously employed temperature threshold to define extremely hot summers, we find that events that would occur twice a century in the early 2000s are now expected to occur twice a decade. For the more extreme threshold observed in 2003, the return time reduces from thousands of years in the late twentieth century to about a hundred years in little over a decade.
Despite their adverse impacts, definitions and measurements of heat waves are ambiguous and inconsistent, generally being endemic to only the group affected, or the respective study reporting the analysis. The present study addresses this issue by employing a set of three heat wave definitions, derived from surveying heatrelated indices in the climate science literature. The definitions include three or more consecutive days above one of the following: the 90th percentile for maximum temperature, the 90th percentile for minimum temperature, and positive extreme heat factor (EHF) conditions. Additionally, each index is studied using a multiaspect framework measuring heat wave number, duration, participating days, and the peak and mean magnitudes. Observed climatologies and trends computed by Sen's Kendall slope estimator are presented for the Australian continent for two time periods (1951-2008 and 1971-2008). Trends in all aspects and definitions are smaller in magnitude but more significant for 1951-2008 than for 1971-2008. Considerable similarities exist in trends of the yearly number of days participating in a heat wave and yearly heat wave frequency, suggesting that the number of available heat wave days drives the number of events. Larger trends in the hottest part of a heat wave suggest that heat wave intensity is increasing faster than the mean magnitude. Although the direct results of this study cannot be inferred for other regions, the methodology has been designed as such that it is widely applicable. Furthermore, it includes a range of definitions that may be useful for a wide range of systems impacted by heat waves.