Enabling Climate Information Services for Europe
Report
Rainfall and Temperature extremes for Greek Cities
Activity:
Activity number:
CITIES
4, T4.3
Deliverable:
Report on rainfall and temperature
extremes for selected Greek cities
4.4
Deliverable number:
Author:
Ioannis K. Tsanis, Manolis G. Grillakis
Aristeidis G. Koutroulis, TUC
The work leading to this publication has received funding from
the European Union's Seventh Framework Programme
(FP7/2007-2013) under grant agreement n° 265240.
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Page 2 of 35
Key remarks ......................................................................................................................................... 4
1. Executive Summary .......................................................................................................................... 5
2. Introduction ....................................................................................................................................... 5
3. Methods ............................................................................................................................................ 6
4. Datasets and case study areas ......................................................................................................... 7
5. Results.............................................................................................................................................. 9
5.1 On the need of bias adjustment .................................................................................................. 9
5.2 Changes in the Annual Exceedance Probability for precipitation. ............................................. 12
5.3 Changes in high percentiles of daily precipitation...................................................................... 16
5.4 Transient changes in the maximum daily temperatures ............................................................ 18
5.5 Transient changes in the minimum daily temperatures ............................................................. 20
5.6 Changes in maximum daily temperature distribution ................................................................. 23
5.7 Changes in minimum daily temperature distribution .................................................................. 25
5.8 Changes in number of days annually exceeding 37oC and 40oC. ............................................. 28
6. Conclusions .................................................................................................................................... 30
7. Acknowledgements ......................................................................................................................... 30
References ......................................................................................................................................... 31
APPENDIX ......................................................................................................................................... 34 Page 3 of 35
Key remarks
•
Significant changes detected in temperature extremes over the 4 major Greek cities according to
the 8 analyzed ENSEMBLES RCMs under A1B and the 12 GCMs in the frame of CMIP5 under RCPs
2.6, 4.5 and 8.5.
•
The present “hot” days will become more common in the future. New records in extreme
temperature will appear.
•
Minimum temperatures are expected to increase by average.
•
The exceedance probability of high precipitation events is expected to increase.
•
High precipitation events will intensify in future.
•
Results show increase in the maximum daily temperature.
•
The variability of maximum temperatures is expected to become larger.
•
In all cases the 2010 – 2050 period is warmer (in JJA Tmax) that the control period 1955 – 2000,
followed by a warmer 2051 – 2100 period.
•
The changes in Athens city are projected to be more severe than the other studied locations.
•
Days with Tmax>37oC and Tmax>40oC are expected to increase in all examined cities except
Heraklion.
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1. Executive Summary
The objective of this report is to assess the future changes in extreme events related to
temperature and precipitation over the 4 biggest in terms of population Greek cities, Athens,
Thessaloniki, Patra, and Heraklio (in descending order). Weather extremes such as precipitation and
temperature can be a hazard for life and property. The Greek Civil Protection Agency has a great
interest in future scenarios in order to develop measures against natural calamities such as floods and
heat waves. The main interest is in major city centers where the effects of extreme events are more
profound and can be augmented due to development. The changes in intensity and seasonality of
rainfall and temperature extremes are investigated in the timeframe of 2010-2100. The corresponding
end user is Civil Protection Service of Region of Crete under the authority of the Ministry of
Environment Energy and Climate Change. Historical data of maximum daily temperature, cumulative
precipitation and rainfall intensity provided from the Hellenic National Meteorological Service, Model
data from the ENSEMBLES FP6 CMIP5 datasets and were used for the analysis. Appropriate biasadjustment techniques were applied in order to remove biases and reduce uncertainty in climate
change signals of future periods.
2. Introduction
Heatwaves are posing thread to human lives. Human body has a capacity to maintain its
temperature around 37oC, by sweat evaporation and the discard of latent heat from the skin surface
(Hajat et al., 2010). It is observed that there is a threshold comfort temperature at which the mortality
rate is minimal. It is observed that this threshold is closely associated with the average temperature
that a community experience (Martens, 1998). As the average temperature is closely linked to the
latitude or the altitude, threshold temperature is linked with geographic position (Keatinge et al., 2000;
Baccini et al., 2008; Martiello and Giacchi, 2010). Even if people are highly adaptive to heat
(Martens, 1998; Haines et al., 2006), there are limits to the temperature increase that humans can
adapt (EEA Report No 2, 2012).
The impact of heatwaves rather strong in cities and towns. This is due to the 'Urban Heat
Island' (UHI) effect which describes the increased temperature of the urban air compared to its rural
surroundings. The UHI effect may increase the difference of the temperature of the urban air
compared to its rural surroundings that may exceed the 10°C (Oke, 1982). Moreover, Li and BouZeid (2013) found that there are Synergistic Interactions between Urban Heat Islands and Heat
Waves mentioning that heat waves not only increase the ambient temperatures of an urban landscape
but also intensify the difference between urban and rural temperatures, i.e. the urban heat island
effect.
Global mean surface temperatures have risen by 0.74°C ±0.18°C as it was estimated on
records between 1906 and 2005. The rate of warming over the last 50 years is almost double than
over the last 100 years (0.13°C ± 0.03°C vs. 0.07°C ± 0.02°C per decade) (Trenberth et. al., 2007).
Page 5 of 35
Simolo et al., (2012) analysed the European Climate Assessment (ECA) observational
dataset (Klein Tank et al., 2002), to find that there was an intensification of extremely warm events
over Europe in the recent past. Moreover, climate-change projections suggest that European summer
heatwaves will become more frequent and severe during this century, consistent with the observed
trend of the past decades. The exceptional severe heatwave of 2007 over southern east Europe could
be not a proof but an indicator of what eastern Mediterranean summers could look like in the summer
(Founda and Giannakopoulos, 2009. The most severe impacts arise from multi-day heatwaves,
associated with warm night-time temperatures and high relative humidity. Heatwaves include tropical
nights (minimum temperature exceeding 20°C) and hot days (maximum temperature exceeding 35°C)
(Fischer and Schär, 2010). Frias et al., (2012) analysed changes of maximum temperatures in
Europe, evaluated using two state-of-the-art regional EU ENSEMBLES project climate models. They
found that the increments for extremes (e.g. 40-year return values) will be two or three times higher
than those for the mean seasonal temperatures, particularly during spring and summer in Southern
Europe. These trends may also be underestimated, as shown by Min et al. (2013), who investigated
the ability of ENSEMBLES RCMS to represent the trends in extreme temperatures on their historical
runs. They found that both the ensemble of RCMs but also individual models, significantly
underestimate the observed trends over most of the north-western European land surface. They
concluded that care should be taken when using RCM data for adaptation decisions in the context that
measures should include greater extremes than those evinced by the RCMs.
Climate models’ output tend to systematically differ from observational data (Sharma et al.,
2007). The systematic deviation is to a high degree related to model imperfections due to the coarse
spatial resolution of the modelled processes. Such biases are often found in the entire spectrum of the
climatic variable histogram. Referring specifically to precipitation, biases can also affect the number of
drizzle days when, and the underestimation of high precipitation values (Leander and Buishand,
2007). The presence of such biases in GCM precipitation data seriously limits its applicability in
climate impact studies (Wood et al., 2004) and can result in unwanted uncertainty regarding projected
climate change impacts. Moreover, discrepancies can also be observed between local scale and large
scale data variability. A common technique used in impact studies is to inflate the large scale data
using the local data variability (Maraun, 2013). The inflation of the variability is an essential process,
especially when they are intended to be used to study changes in extreme climatic events.
3. Methods
Bias adjustment
In order to optimally remove the systematic bias of the modeled precipitation, a new
methodology that was developed in the frame of European funded projects, including ECLISE, and
applied to climate model outputs. The methodology belongs to the widely used family of quantile
mapping correction methods. The method uses different instances of gamma function that are fitted on
multiple discrete segments on the precipitation CDF, instead of the common quantile-quantile
approach that uses one theoretical distribution to fit the entire CDF. This imposes to the method the
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ability to better transfer the observed precipitation statistics to the raw GCM data. The selection of the
segment number is performed by an information criterion to poise between complexity and efficiency
of the transfer function (Grillakis et al., 2013). The methodology was tested on CMIP3 global climate
models resulting to a very good performance in reducing the systematic biases of the climate model
data. Details of the methodology and the validation are presented in Grillakis et al. (2013).
For the correction of the temperature biases, a quantile mapping methodology that uses
normal distribution was used (Samuel et al., 2011). The correction is performed by fitting a theoretical
CDF on the raw GCM data [eq. 1] and then by estimating its inverse CDF using the theoretical CDF
parameters of the observed data [eq. 2].
1
ctrl
 FGCM
Ti corr ctrl  Fobs
Ti raw ctrl 

[eq. 1]
where T raw ctrl is the minimum or maximum temperature and T corr ctrl is the corrected GCM precipitation
of the control period.
1
cal
 FGCM
Ti corr prj  Fobs
Ti raw prj 

[eq. 2]
where (T raw prj ) and (T corr prj ) is the projection’s raw GCM and corrected GCM precipitation,
respectively. The F(·) and F(·)-1 stand for CDF and its inverse respectively, while subscripts indicate
the dataset from which the normal distribution parameters were derived.
Annual Exceedance Probability
Changes in the recurrence intervals of specific magnitude events are studied using the Annual
Exceedance Probability (AEP) approach (Eash et al., 2013). Theoretical parametric distributions are
fitted on the annual maximum values of precipitation (the maximum of each year). Following the rule
that a theoretical curve can be extended to as far as the double size of the fitted sample, it is safe to
extrapolate the distributions to as far as 1% AEP. Nonetheless, the agreement between the observed
and the bias corrected historical data is also an indicator of the a) quality of the bias correction, b) the
uncertainty induced to the extrapolation, which should be taken into consideration.
4. Datasets and case study areas
Climate model data were obtained from ENSEMBLES (http://ensembles-eu.metoffice.com/)
project and the 5th phase of Coupled Model Intercomparison Project CMIP5 (Taylor et al., 2009). The
ENSEMBLES simulations were performed under the A1B emission scenario (Nakićenović, 2000),
while the CMIP5 data were obtained for specific Representative Concentration Pathways (RCPs),
(Moss et al., 2008; 2010) including RCP2.6, RCP4.5 and the high end RCP8.5.
Precipitation extremes and maximum, and minimum temperatures were studied for changes in
their frequency and intensity. Moreover, local observations were used to remove systematic biases
from the raw model data. The available data were split into three period, the past period 1955 – 2000,
and two future periods, 2010 – 2050 and 2051 – 2100. For the temperature analysis, two seasons
were then considered, the winter season of December – January - February (DJF), and the summer
season June – July – August (JJA). In these two seasons, the minimum and maximum temperatures
were examined correspondingly.
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The analysis was conducted for the four major Greek cities, Athens, Thessaloniki, Heraklion,
Patra (ranked in population descending order) shown in Figure 1. Long record of precipitation and
minimum/maximum temperatures of the Hellenic National Meteorological Service (HNMS) were
obtained for each city from stations located close to the city centers. The GCMs/RCMs data were bias
adjusted against to station records before the statistical analysis.
Figure 1: The four case study areas.
Athens, the Greek capital city is centre of economic, financial, industrial, political and cultural
life in Greece. It is located in central Greece and its population is more than 4 million (2011 census).
The urban area covers over 400 km2 resulting to a population density of about 10,000 citizens/km2.
Thessaloniki is the second largest Greek city with a population of 790,824 (2011). It covers 110 km2,
with a density of 7,000 citizens/km2. It is a major Greek economic, industrial, commercial and political
centre. It is locate to the northern Greece and comprises a major transportation hub for the rest of
south Eastern Europe. It is also a popular tourist destination in Greece.
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Patra is Greece's third largest urban area and the regional capital of Western Greece, in
northern Peloponnese, 215 km west of Athens. The city is built at the foothills of Mount Panachaikon,
overlooking the Gulf of Patras. Patra has a population of 214,000 over an area of 333 km2 that results
to a density of about 640 citizens/km2.
Heraklion is the fourth largest city and the administrative capital of the island of Crete, Greece.
It is the fourth largest cities in Greece with population of almost 174,000 over an area of 120 km2 that
result to a density of 1,450 citizens/km2. The city is among the most popular tourism destinations in
Greece.
Table 1: ENSEMBLES RCMs and CMIP5 GCMs used in the study.
ENSEMBLES RCMs
No
Institute
RCM
Driving GCM
1
2
3
4
5
6
7
8
CNRM
DMI
ICTP
KNMI
METNO
MPI-M
SMHI
SMHI
RM5.1
HIRHAM5
REGCM3
RACMO2
HIRHAM
REMO
RCA
RCA
ARPEGE
BCM
ECHAM5
ECHAM5
BCM
ECHAM5
ECHAM5
BCM
No
1
2
3
4
5
6
7
8
9
10
11
12
CMIP5 GCMs
Institute
CCCMA
NOAA GFDL
NOAA GFDL
NOAA GFDL
IPSL
MIROC
MIROC
MIROC
MPI-M
MPI-M
MRI
NCC
Name
CanESM2
GFDL-CM3
GFDL-ESM2G
GFDL-ESM2M
CM5A-MR
MIROC-ESM-CHEM
MIROC-ESM
MIROC5
MPI-ESM-LR
MPI-ESM-MR
MRI-CGCM3
NorESM1-M
RCPs
2.6
2.6, 8.5
2.6, 4.5, 8.5
4.5, 8.5
2.6, 6.0, 8.5
2.6
2.6, 4.5, 8.5
2.6, 4.5
2.6, 4.5, 8.5
2.6, 4.5, 8.5
2.6, 4.5, 8.5
2.6, 4.5, 8.5
5. Results
5.1 On the need of bias adjustment
Temperature
The average of the maximum summer (JJA) and minimum winter (DJF) temperatures were
estimated. Discrepancies were observed between observations and RCM realizations. For the
minimum temperature, the differences were estimated between 2.66oC (Elliniko) and -3.40oC
(Heraklion) while maximum temperatures deviated between -5.87oC (Elliniko) and 0.6oC (Heraklion)
(Table 2). The large biases between observations and simulations emphasize the need of adjustment.
Table 2 summarizes the average values of bias adjusted datasets. Similar results are obtained by
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analyzing the maximum summer (JJA) and minimum winter (DJF) temperatures of CMIP5 GCMs
(Table 3). Respectively to the temperature, Table 4 exhibits the discrepancy of average precipitation
between the RCM/GCM models and the observed precipitation. The bias adjustment techniques
described in Grillakis et al. (2013) and Samuel et al. (2011) were applied to precipitation and
temperature data, respectively. The techniques were applied at a calendar month basis, in order to
also adjust the seasonality.
Table 2: Long term mean minimum and maximum temperatures for DJF and JJA, from
observations (1955 - 2000) and ENSEMBLES RCMs average (1955 - 2000, 2010 - 2050 and 2051 2100) for the four considered stations’ locations. The raw RCM data are given at the upper part
of the table, while the downscaled-bias adjusted are given in the lower part.
Average of minimum DJF
temperatures [oC]
Obs
RCMs
RCMs
RCMs
1955 –
1955 – 2010 – 2051 –
2000
2000
2050
2100
Average of maximum JJA
temperatures [oC]
Obs
RCMs
RCMs
RCMs
1955 –
1955 –
2010 – 2051 –
2000
2000
2050
2100
Raw RCM data
Elliniko
7.70
10.36
11.30
12.52
30.94
27.16
29.07
30.45
Heraklion
9.61
6.21
7.17
8.46
28.22
24.89
26.83
27.97
Thessaloniki
2.16
3.00
3.88
5.52
30.74
27.45
29.76
31.27
Patra
6.64
4.3
5.13
6.43
30.04
28.91
31.89
32.75
Corrected RCM data
Elliniko
7.70
7.70
8.82
10.52
30.94
30.91
33.28
34.91
Heraklion
9.61
9.61
10.39
11.55
28.22
28.39
31.10
32.91
Thessaloniki
2.16
2.16
3.05
4.70
30.74
30.72
32.80
34.62
Patra
6.64
6.64
7.38
8.72
30.04
30.04
32.03
33.45
The effect of bias adjusting is obvious from the results presented in Table 2. Obtained transfer
functions were used to adjust projected temperatures of ENSEMBLES RCMs and CMIP5 GCMs for
the two future time slices (Table 2). Table 2 shows the projected changes in mean values of the
temperature time-series for the four case studies. For the ENSEMBLES RCMs, the corrected
projections indicate an increase for the first period (2010-2050) ranging from 0.74 oC to 1.12 oC in
average minimum DJF and from 1.99 oC to 2.82 oC for the average maximum JJA temperatures,
amongst the studied cities. The increase is more profound for the second half of the 21st century,
where the respective increases are estimated between 1.94 oC to 2.82 oC for minimum and 1.99 oC to
2.71 oC for maximum temperature. The same procedure was applied to the GCM output data.
The changes in Table 3 refer to model average of each RCP used, to give a brief insight of
the projected temperature change. The corrected minimum DJF temperatures of the GCMs indicate a
robust signal of increase for both 2010-2050 and 2051-2100 periods.
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Table 3: Long term mean minimum and maximum temperatures for DJF and JJA, from
observations (1955 - 2000) and CMIP5 GCMs average from all (1955 - 2000, 2010 - 2050 and
2051 - 2100) for the four considered stations. The raw GCM data are given at the upper part of
the table, while the corrected are given in the lower part.
AVERAGE OF MINIMUM DJF TEMPERATURES
Raw GCM data
RCP8.5
RCP2.6
RCP4.5
9.25
10.70
11.33
3.64
10.11
11.05
11.98
4.78
11.97
HERAKLION
9.61
12.59
14.04
13.79
13.27
13.53
14.35
14.39
14.49
15.34
THESSALONIKI
2.16
2.03
3.46
3.69
0.18
2.91
3.68
4.32
1.14
4.81
PATRA
6.64
7.49
8.81
9.54
3.64
8.28
9.17
10.17
4.78
10.12
RCP8.5
RCP4.5
7.70
RCP6.0
RCP2.6
ELLINIKO
RCP6.0
GCMs
1955 – 2000
GCMs 2051 – 2100
Obs
1955 –2000
GCMs 2010 – 2050
Corrected GCM data
ELLINIKO
7.70
7.68
9.25
9.09
8.82
9.10
9.77
9.91
9.70
11.20
HERAKLION
9.61
9.59
11.32
11.03
11.72
11.18
11.82
11.85
13.40
13.53
THESSALONIKI
2.16
2.16
3.32
3.33
3.42
3.38
3.54
3.97
4.27
5.20
PATRA
6.64
6.63
8.05
7.83
7.80
7.84
8.53
8.50
8.70
9.66
AVERAGE OF MAXIMUM JJA TEMPERATURES
Raw GCM data
GCMs
1955 – 2000
RCP2.6
RCP4.5
RCP6.0
RCP8.5
RCP2.6
RCP4.5
RCP6.0
RCP8.5
GCMs 2051 – 2100
Obs
1955 –2000
GCMs 2010 – 2050
ELLINIKO
30.94
35.26
29.46
28.70
30.83
28.91
29.83
29.72
33.36
31.81
HERAKLION
28.22
30.02
26.69
26.64
24.44
26.34
27.05
27.51
26.04
28.68
THESSALONIKI
30.74
38.07
30.75
29.64
29.43
29.13
31.61
30.96
32.24
33.08
PATRA
30.04
38.90
31.38
30.96
30.83
31.02
31.72
32.12
33.36
34.34
Corrected GCM data
ELLINIKO
30.94
30.91
33.28
33.06
33.55
33.43
33.67
34.30
36.18
36.90
HERAKLION
28.22
28.39
31.45
30.60
30.74
30.95
32.02
32.02
33.32
34.70
THESSALONIKI
30.74
30.72
32.77
32.58
33.64
33.00
33.41
33.71
36.79
36.37
PATRA
30.04
30.04
32.14
31.87
32.75
32.28
32.42
32.96
35.47
35.38
Precipitation
Annual maximum precipitation was considered to check for the models ability to capture the high
precipitation events. As with temperature, maximum annual precipitation is also underestimated by
both ENSEMBLES RCMs and selected CMIP5 GCMs. In Table 4, the average raw and bias corrected
Page 11 of 35
time-series of annual maximum precipitation values of all RCMs and GCMs are compared against
observations, for each one of the four analysed stations. The discrepancies observed, emphasize the
need of bias adjustment of the precipitation in both the RCM and the GCM data.
Table 4: Averages for the annual maximum observed precipitation and for the raw and bias
corrected precipitation (historical period). ENSEMBLES RCM and CMIP5 GCM experiments’
means are presented.
[mm/day]
Athens
Heraklion
Thessaloniki
Patra
Observed
49.73
52.52
42.95
56.16
Raw ENSEMBLES RCMs
41.23
34.29
32.61
44.05
Bced ENSEMBLES RCMs
48.19
51.10
41.88
55.00
Raw CMIP5 GCMs
25.00
19.36
27.36
24.56
Bced CMIP5 GCMs
48.32
50.34
50.34
53.73
5.2 Changes in the Annual Exceedance Probability for precipitation.
The deviation of AEP derived from bias adjusted model data of the historical period against
the ones derived from observations, was examined. Figure 1 shows that the AEP curves of observed
and (bias adjusted0 model does not exhibit significant difference, especially in the cases of Heraklion
and Thessaloniki. In all cases, the 95% confidence envelope of the model corrected data is narrower
that the respective observed due to the higher number values (sample size). The 45 years of data
utilized 45 precipitation values for the observed and 45 values from 8 RCMs, i.e. 360 values (sample
size) for the model corrected data.
The 2% annual exceedance probability is a common recurrence interval used in the
infrastructure construction, such as sewer networks of open channels in Greece. Thus the estimation
of the 2% AEP was also explicitly estimated. Detailed results for Annual exceedance probability of 2%,
which corresponds to 50 years return period, are included in Appendix Table A2.
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Figure 2: Annual exceedance probability of observed and bias corrected historical ENSEBLES
RCMs simulations for each study site. Dashed lines correspond to the 95% confidence
intervals for the optimal fitting curve.
The analysis was then extended to the climate projections. For all cases, significant increase
in the AEP is projected. The most noteworthy change is for Athens – Elliniko, followed by Patra,
Thessaloniki and finally Heraklion. It is worth noting that the change in the AEP is limited to the first
projected period in the cases of Athens and Heraklion, where for the second period AEP curves stays
almost unchanged (Figure 3).
Page 13 of 35
Figure 3: Annual exceedance probability of ENSEBLES RCMs historical and A1B simulations
over two future period for each study site. Dashed lines indicate the 95% confidence intervals
for the best fitting curve.
In the case of CMIP5 data (Figure 4), three scenarios - RCP2.6, 4.5 and 8.5 - were examined.
From the analysed GCMs, only one GCM provided data for RCP6.0, thus it was omitted from the
analysis.
For Athens-Elliniko, RCP2.6 scenario indicates a substantial increase in all exceedance
probabilities for 2010-2050. The 2051-2100 however, a slight decrease in the exceedance
probabilities comparing to the aforementioned period. For the RCP4.5 scenario the exceedance
probabilities are largely increased in the 2010-2050 period and then are slightly increased to higher
values in the 2051-2100. Under the RCP8.5 scenario finally, the exceedance probabilities are
expected to substantially increase in the 2010-2050 period, and then significantly increase further in
the period 2051-2100.
For Heraklion, both RCP2.6 and RCP4.5 scenarios exhibit similar patterns i.e. a substantial
increase in all exceedance probabilities for 2010-2050 and a slight decrease in the exceedance
probabilities comparing to the previous period, for. 2051-2100. For the RCP8.5, both projection
periods exhibit notable change in the exceedance probability.
Page 14 of 35
Figure 4: Annual exceedance probability from CMIP5 historical model data for the three RCP
over two future period simulations for each station. Blue lines correspond to AEP based on
observations, orange based on model data of the 2010-2050 period and red based on model
data of the 2050-2100 period. Dashed lines indicate the 95% confidence intervals for the
optimal fitting curve.
For Thessaloniki, all the considered scenarios show a large increase in the exceedance
probability for the 2010-2050 period, and a further smaller increase for the 2051-2100 period. In the
case of RCP8.5 the further increase of the 2051-2100 is more significant.
AEP of maximum precipitation for Patra is projected to increase during 2010-2050 comparing
to the historical period, however, the specific increase is projected to be retained at the same levels in
the 2051-2100 period. Detailed results for Annual exceedance probability of 2% - 50 years return
period - are given in Appendix Table A3.
Page 15 of 35
5.3 Changes in high percentiles of daily precipitation.
Another estimator of change in the precipitation extremeness is the change in certain
percentiles of daily precipitation. Three high percentiles were analyzed, the 99th, 99.5th and the 99.9th
percentile. The changes in high precipitation as estimated from the ENBEMBLES RCM data indicate a
reduction in the 99th percentile as the analysis moves from the historical to 2010-2050 and 2051-2100
projection periods (Figure 5). On the other hand, 99.9th percentile is projected to increase in the future
for all stations except Heraklion, where the highest 99.9th percentile is projected for the 2010-2050
period (Figure 5).The values of Figure 5 are included in Table A4 of Appendix.
Figure 5: The 99th, 99.5th and the 99.9th percentile of daily precipitation values according
observations and ENSEMBLES RCMs.
Similar analysis was conducted based on the CMIP5 GCM data (Figure 6).
For Athens, all three percentile levels are expected to change in all RCP scenarios. The
changes are more profound in 99.9th percentile. Here, in contrast to the AEP analysis, the RCP8.5
scenario shows very small changes in all percentiles. It has to be stressed here that the RCP6.0
analysis is based on a single GCM model.
For Heraklion, the 99th and the 99.5th percentile is projected to be slightly modified in all the
analysed RCP scenarios. The 99.9th percentile is projected to present more significant change in
2010-2050 period.
The same pattern of Heraklion applies also for Thessaloniki and Patra cases, where large
changes are projected for RCP8.5 for the first projection period, while the second period, the changes
are moderate positive or negative with respect to 2010-2050 period.
Page 16 of 35
Figure 6: The 99th, 99.5th and the 99.9th percentile of daily precipitation values according to
observations and CMIP 5 GCM simulations.
Page 17 of 35
5.4 Transient changes in the maximum daily temperatures
The average of the maximum daily temperature (recorded at 18:00 UTC) of the summer (JJA)
months is analysed for the four stations considered. Figure 7 shows the time series of average
maximum daily temperature for JJA derived from all RCMs as well as the model spread, for each
station’s data. In all cases, the trend is strong positive. Moreover, all case present similar spread
among the analysed models, except the Athens-Elliniko that exhibits a slightly larger spread.
Figure 7: Mean annual maximum daily JJA temperatures and spread, as they were projected by
the ENSEMBLES RCMs.
In the case similar trend analysis based on the CMIP5 GCM data, the RCP2.6 scenario
projects strong increasing pattern until 2050s and then stabilizes until 2100s (Figure 8). In RCP4.5,
stabilization of the increase is also projected but later comparing to the RCP2.6, at about 2070s. In the
case of RCP8.5, the trend is strong positive until 2100 without signal of temperature stabilization.
Regarding the spread amongst the GCM data, the RCP2.6 shows the larger, especially in the
Page 18 of 35
stabilization period. RCP4.5 shows less spread than RCP2.6 but is more peaky, while the RCP85 is
the one with the most robust signal, having the less spread of amongst the examined scenarios.
Figure 8: Mean annual maximum daily JJA temperatures, as they were projected by the CMIP5
GCMs.
The average rate of increase (as it was estimated by linear regression in the 2006 – 2100
data) per decade of Figure 6 and Figure 7 data, is given in Table 5.
Page 19 of 35
Table 5: Rate of annual (the one highest value of each year) maximum of daily temperature
increase in ENSEMBLES and CMIP5 models.
Rate of increase
[oC/decade]
Athens - Elliniko
Heraklion
Thessaloniki
Patra
ENSEMBLES
0.64
0.35
0.43
0.44
RCP2.6
0.12
0.16
0.16
0.09
CMIP5 (2010 – 2100)
RCP4.5 RCP6.0 RCP8.5
0.27
0.56
0.76
0.31
0.53
0.82
0.25
0.68
0.74
0.24
0.58
0.69
5.5 Transient changes in the minimum daily temperatures
Changes are also projected to be posed to the minimum daily temperature of the winter
season (temperature at 6:00 UTC). The minimum daily temperature (averaged for each year for DJF
months) is analyzed for two future periods 2010-2050 and 2051-2100 using the ENSEMBLES RCMs.
The analysis shows a robust increasing trend for all the analyzed stations (Figure 9), similar among
the four case studies, except Athens – Elliniko where the signal is stronger. The rate of change is
projected from as low as 0.23oC/decade to as high as 0.40oC/decade amongst the stations (Table 6)
as it was estimated by linear regression on the model ensemble.
The change in the minimum temperature in CMIP5 GCM derived data follows the pattern of
maximum precipitation, with RCP2.6 and RCP4.5 exhibiting stabilization after a period of increase,
which lasts until 2050s and 2070s respectively (Figure 10). In the case of RCP8.5, the trend is
strongly positive until 2100 without signal of stabilization. The spread of the ensemble of the GCMs is
smaller in the case of RCP2.6 comparing to the maximum temperature analysis. Moreover, the spread
in each station’s data is similar among all the RCP scenarios. The trend of change in oC per decade is
shown in Table 7, as it is estimated from linear regression for the 2006 - 2100 period.
Page 20 of 35
Figure 9: Mean annual minimum daily DJF temperatures, as they were projected by the
ENSEMBLES RCMs.
Page 21 of 35
Figure 10: Mean annual minimum daily DJF temperatures, as they were projected by the CMIP5
GCMs.
Page 22 of 35
Table 6: Rate of annual (the one lowest value of each year) minimum of daily temperature
increase in ENSEMBLES and CMIP5 models.
Rate of increase
[oC/decade]
Athens - Elliniko
Heraklion
Thessaloniki
Patra
ENSEMBLES
0.40
0.23
0.35
0.24
RCP2.6
0.19
0.15
0.08
0.19
CMIP5 (2010 – 2100)
RCP4.5 RCP6.0 RCP8.5
0.25
0.27
0.56
0.24
0.53
0.59
0.20
0.32
0.52
0.17
0.28
0.48
5.6 Changes in maximum daily temperature distribution
The distribution of maximum daily temperatures of JJA is analyzed. The analysis is based on
fitting a normal distribution on the temperature data. The analysis reveals significant changes in the
mean, but also in the spread of the distribution. Figure 11 shows the ENSEMBLES RCMs distributions
for the historical and two projected periods. The peak of each distribution corresponds to the mean
value. The width defines the spread of the distribution, with the wider distributions to a set of values
with higher variability. The right tails of the distributions (in the case of maximum temperatures), define
the extreme maximum temperatures.
In the case of Athens – Elliniko for the 2010-2050 period, the distribution is expected to widen,
while the mean values expected to move to higher values. The increase is preserved for the 20512100 period, where the distribution further widens and the mean moves further right. The most
interesting observation though is the tail sections of the distributions. The historical period’s right tails
temperatures are expected to appear more often in the 2010-2050 period, while are expected to
become almost a normal condition in 2051-2100. Record hot maximum temperatures are also
projected to appear, as the right tail of the distribution is moved to higher temperature.
Patra case study is expected to experience significant changes in mean and extreme
temperatures, especially in 2051-2100 period. Heraklion and Thessaloniki cases are also expected to
experience noteworthy changes in mean and spread of the distributions.
The analysis is extended to the CMIP5 GCM data. The RCP2.6 scenario shows changes to be
posed in the 2010-2050 period (Figure 12). The changes are related to the mean temperature and the
spread. As in ENSEMBLES RCMs’ analysis, the spread tends to increase for the projected data.
Moreover, the maximum temperature is projected to stabilize for that scenario, with the distributions of
2051-2100 period to almost coincide those of 2010-2050. Another aspect of this stabilization pattern
was also presented previously in Figure 8, where the annual averages of the maximum temperature
are found to stabilize after 2050s. For RCP4.5, the second projected period 2051-2100 exhibit change
in mean and spread comparing to the 2010-2050, but in lesser degree than the change between
historical and 2010-205 periods. Finally, regarding the RCP8.5 scenario, the signal of change is strong
positive (towards new extremes in maximum temperature, increase of the average maximum
temperature and increase of the maximum temperatures spread.
Page 23 of 35
Figure 11: Distribution of JJA maximum daily temperatures in historical (black lines), 20102050 period (green lines) and 2051-2100 (blue lines), as they were projected by ENSEMBLES
RCMs. Red lines define observed temperature distributions.
Page 24 of 35
Figure 12: Distribution of JJA maximum daily temperatures in historical (black lines), 20102050 period (green lines) and 2051-2100 (blue lines), as they were projected by the CMIP5 GCM
data. Red lines define observed temperature distributions.
5.7 Changes in minimum daily temperature distribution
The minimum temperatures are also examined for changes in distribution. It is found that in
the case of ENSEMBLES RCMs, the mean of the distribution changes in all case studies to higher
temperatures. However, the largest change is expected to the 2051-2100 period, rather than 20102050 as it was previously shown in maximum temperatures analysis (Figure 13). Moreover, the
changes in the spread are projected to be small in comparison to maximum temperature distributions.
By examining the CMIP5 GCM analysis of Figure 14, it can be seen that the mean of the minimum
temperature distributions is projected to increase in all cases and for both projected periods.
Page 25 of 35
Nonetheless, in contrast to ENSEMBLES RCMs’ analysis, it is found to retain or, in many cases,
reduce the spread of the distributions in all cases and RCP scenarios. This implies that the minimum
observed temperatures are expected to become higher, but more concentrated around the mean.
Moreover, as the left part of the distribution is moved to the right, the chance of winter days
experiencing colder minimums is reduced.
Figure 13: Distribution of DJF minimum daily temperatures in historical (black lines), 2010-2050
period (green lines) and 2051-2100 (blue lines), as they were projected by ENSEMBLES RCMs.
Red lines define observed temperature distributions.
Page 26 of 35
Figure 14: Distribution of JJA maximum daily temperatures in historical (black lines), 20102050 period (green lines) and 2051-2100 (blue lines), as they were projected by the CMIP5 GCM
data. Red lines define observed temperature distributions.
Page 27 of 35
5.8 Changes in number of days annually exceeding 37oC and 40oC.
The number of days exceeding 37oC and 40oC are also examined. For the ENSEMBLES
RCMs it is found that the most significant changes are projected to be posed in Athens – Elliniko. The
average number of days per year over 37oC are projected to increase from 1.3 to 13.2 and 35.3 days
for historical, 2010-2050 and 2051-2100 periods respectively (Figure 15). Similarly, the days over
40oC are expected to increase from almost zero, to 15 in 2051-2100. It has to be noted that maximum
temperature in observations is measured at 18:00 UTC. Thessaloniki and Patra is expected also to
experience increase, however, the number of days over those two thresholds is expected to be
significantly lower than Athens. For Heraklion, there is not expected almost any change.
Figure 15: Days that maximum temperature is expected to exceed 37oC and 40oC as is derived
from the ENSEMBLES RCMs’ data analysis.
The respective changes in CMIP5 GCM data show increase of 37oC and 40oC over days in all
cases. In contrast to the ENSEMBLES RCMs, the CMIP5 GCM data show significant increase to the
number of days over the two thresholds for all case studies in the RCP85 scenario. This change is
projected to happen suddenly in the 2051-2100 period rather than escalate the 2010-2050 projection.
Page 28 of 35
Figure 16: Days that maximum temperature is expected to exceed 37oC and 40oC as is derived
from CMIP5 GCM data analysis.
Page 29 of 35
6. Conclusions
The present report summarizes the findings regarding the changes in different aspects of
temperature and precipitation extremes, during an effort to assess and communicate the needs of the
user of the climate information output. Specific annual exceedance probabilities for maximum annual
precipitation are projected to increase in future at all analysed study areas.The 99.9th percentile of the
daily precipitation is expected to increase, indicating that high precipitation events may intensify in the
future. Significant changes are detected in temperature extremes over the 4 major Greek cities
according to the 8 analyzed ENSEMBLES RCMs under A1B and CMIP5 GCMs under 3 RCP
scenarios. The present “hot” days will become more common in the future and new records in extreme
temperature may appear. The variability of maximum temperatures is expected to become larger. At
the same time minimum temperatures are expected to increase. Athens can be described as a hotspot
of change in extreme high temperatures while Heraklion is expected to have the less impact amongst
the studied sites. Days with Tmax>37oC are expected to increase in all examined cities (except
Heraklion). Days with temperature > 40oC are going to increase in all examined cities (except
Heraklion), however Athens will experience the largest increase. In all cases/data sources and
scenarios the 2010 – 2050 period maximum daily temperatures are projected to increase comparing to
the control period 1955 – 2000, followed by a warmer 2051 – 2100 period. Only RCP2.6 describes
temperature stabilization after 2050s. Another significant outcome is the relevance between the
different philosophies’ scenarios that were used. The A1B scenario which belongs to the SRES family
of scenarios is found to be close to but slightly milder in severity of impacts than the RCP8.5 scenario
for the specific regions of study.
7. Acknowledgements
We acknowledge the World Climate Research Programme's Working Group on Coupled
Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1
of this paper) for producing and making available their model output. For CMIP the U.S. Department of
Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support
and led development of software infrastructure in partnership with the Global Organization for Earth
System Science Portals. The ENSEMBLES data used in this work was funded by the EU FP6
Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged.
Page 30 of 35
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APPENDIX
TABLE A1: ANNUAL EXCEEDANCE PROBABILITY DISTRIBUTIONS USED
Athens - Elliniko
Heraklion
Makedonia - Thessaloniki
Patra
Observed
GEV-MAX
GEV-MAX L moments
GEV-MAX L moments
Pareto
1955-2000
2010-2050
2051-2100
GEV-MAX
GEV-MAX L moments
GEV-MAX
EV2-max
GEV-MAX L moments
GEV-MAX L moments
GEV-MAX
GEV-MAX L moments
GEV-MAX L moments
GEV-MAX
GEV-MAX L moments
GEV-MAX L moments
Table A2: The 2% Annual Exceedance Probability AEP [mm/day] (50 year Return Period) for the ENSEMBLES models’ average.
Athens
Heraklion
Thessaloniki
Patra
1955-2000
96
108
84
105
2010-2050
153
150
107
153
2051-2100
159
142
124
138
Table A3: The 2% Annual Exceedance Probability AEP [mm/day] (50 year Return Period) for the CMIP5 models’ average. The different RCPs were considered
separately.
Athens
Heraklion
Thessaloniki
Patra
1955-2000
2010-2050
2051-2100
RCP2.6
RCP4.5
RCP6.0
RCP8.5
RCP2.6
RCP4.5
RCP6.0
RCP8.5
RCP2.6
RCP4.5
RCP6.0
RCP8.5
RCP2.6
RCP4.5
RCP6.0
RCP8.5
101.1
163.2
146.0
101.9
141.1
150.0
101.4
171.4
126.8
101.0
154.9
131.5
99.5
155.8
142.0
106.9
164.6
152.2
97.5
186.2
189.1
104.5
134.4
152.4
98.4
137.9
147.7
98.8
163.1
180.7
104.4
111.9
143.6
100.3
142.8
166.5
103.8
143.9
146.8
103.8
143.6
138.3
101.4
160.8
115.6
104.6
130.6
133.3
Page 34 of 35
Table A4: Changes in 99th, 99.5th and 99.9th percentiles of daily precipitation according to the ENSEMBLES RCMs.
99th percentile
99.5th percentile
99.9th percentile
Obs
Athens
21.2
19552000
21.7
20102050
22.2
20512100
19.9
Obs
29.8
19552000
31.0
20102050
32.5
20512100
31.2
Obs
50.8
19552000
56.7
20102050
61.4
20512100
63.2
Heraklion
24.9
25.6
25.3
22.3
34.0
34.0
33.8
30.5
58.0
57.6
63.9
58.4
Thes/niki
21.4
21.3
21.0
20.5
27.7
28.3
28.5
29.2
45.5
45.9
50.4
54.3
Patra
29.0
29.1
30.0
28.9
37.3
38.3
39.2
38.8
65.3
62.2
68.1
69.2
Table A5: Changes in 99th, 99.5th and 99.9th percentiles of daily precipitation according to the CMIP5 GCMs average for each RCP run.
Patra
Thes/ki
Heraklion
Athens
99th percentile
99.5th percentile
99.9th percentile
1955-2000
2010-2050
2051-2100
1955-2000
2010-2050
2051-2100
1955-2000
2010-2050
2051-2100
RCP2.6
21.48
22.80
22.79
31.24
32.32
32.92
56.97
62.09
64.46
RCP4.5
21.49
22.60
22.47
30.72
32.44
32.50
57.53
61.55
63.94
RCP6.0
21.20
19.08
16.80
32.20
27.35
23.77
56.72
54.81
50.29
RCP8.5
21.35
21.66
20.69
31.04
31.89
30.36
57.55
62.32
58.48
RCP2.6
25.45
26.48
26.39
34.07
35.83
36.60
57.19
63.59
64.93
RCP4.5
25.53
25.29
24.10
33.91
34.40
33.64
56.48
64.25
61.74
RCP6.0
25.52
29.16
23.93
34.73
38.57
33.18
60.12
68.04
63.61
RCP8.5
25.50
26.37
25.66
34.35
36.24
36.23
56.99
66.09
69.92
RCP2.6
25.50
27.03
26.27
34.37
36.98
35.61
56.54
64.49
63.44
RCP4.5
25.66
26.90
27.18
34.29
37.48
37.88
57.35
67.41
71.64
RCP6.0
25.32
23.71
23.77
34.48
30.22
31.79
54.99
53.23
58.25
RCP8.5
RCP2.6
25.50
29.36
26.37
30.64
25.66
30.29
34.35
38.51
36.24
40.24
36.23
39.92
56.99
62.44
66.09
66.61
69.92
68.03
RCP4.5
29.55
30.23
30.49
38.39
39.89
41.25
62.13
69.11
71.59
RCP6.0
29.00
26.20
24.54
39.08
33.78
33.50
64.70
54.24
55.20
RCP8.5
29.50
29.10
27.78
38.60
38.77
37.14
61.86
65.55
66.94
Page 35 of 35