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ATMOSPHERIC SCIENCE LETTERS
Atmos. Sci. Let. 10: 192–200 (2009)
Published online 11 September 2009 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/asl.233
The impact of climate change on daily precipitation
statistics in Jordan and Israel
Emily Black*
NCAS-Climate, University of Reading, Earley Gate, Reading, UK
*Correspondence to:
Emily Black, Department of
Meteorology, University of
Reading, Earley Gate, Reading,
UK.
E-mail: [email protected]
Received: 24 January 2009
Revised: 6 July 2009
Accepted: 6 July 2009
Abstract
A regional climate model is used to investigate changes in Israel and Jordan precipitation
at the end of the 21st century on daily to monthly timescales. The model predicts that this
region will get significantly drier at the peak of the rainy season, reflecting a reduction in
both the frequency and duration of rainy events. These changes may be associated with
a reduction in the strength of the Mediterranean storm track. Copyright  2009 Royal
Meteorological Society
Keywords:
Middle East; precipitation; climate change; regional model
1. Introduction
The IPCC fourth assessment report suggested that
the eastern Mediterranean region would become significantly drier under a future climate scenario,
with potentially devastating impact on the population (IPCC, 2007). Here, we use a regional model to
explore this prediction in more detail, focussing on
changes in the daily statistics of precipitation for the
Middle East.
There have been several studies of observed climate
change in Middle East. An examination of changes
in temperature and rainfall in Israel at stations with
long records showed that boreal winter rainfall in
Israel increased in the second half of the 20th century
(Ben-Gai et al., 1998, 1999). A study of precipitation
changes over Europe using observations and CMIP3
simulations suggested that an observed slight reduction
in precipitation over much of continental Europe
can be attributed to anthropogenic forcing (Mariotti
et al., 2008). However, a study of temperature and
precipitation extreme indices suggested that while
there have been significant trends in temperature
extremes, any trends in precipitation extremes are
weak and insignificant (Zhang et al., 2005).
Other studies have used model projections to investigate Middle East climate change during the 21st
century. A survey of precipitation predictions from
18 global climate models revealed that rainfall is predicted to decrease over the Middle East, with statistically significant decreases predicted by the end of the
21st century (Evans, 2008). Further investigation of
changes in precipitation processes suggested that this
drying may be accompanied by a change in the dominant precipitation mechanism, from being directly
driven by storm tracks to having a greater dependence
on the upslope flow of moist air masses (Evans, 2009).
These results are consistent with a study of water
Copyright  2009 Royal Meteorological Society
cycle changes in the CMIP3 models, which show a
widespread reduction in water availability over much
of southern Europe and the Mediterranean because
of decreased precipitation and increased evaporation
(Mariotti et al., 2008). A study of temperature and precipitation extremes in northern Israel using a regional
model suggested that under an A2 scenario, temperature will increase and precipitation will decrease over
Israel during the 21st century. Under a B2 scenario,
although temperature was predicted to increase significantly, no significant trend in precipitation was
predicted. These changes in the mean were projected
to be accompanied by increases in the frequency of
extreme temperature and precipitation events (Alpert
et al., 2008). An investigation of precipitation and
stream flow changes in the Middle East using a 20 km
resolution global climate model suggests that, under an
A1b scenario there will be a large enough decrease in
precipitation and increase in evaporation to cause the
fertile crescent to disappear (Kitoh et al., 2008).
The mechanisms behind these changes in the climate
have been the subject of several studies. A statistical
study of precipitation processes in the Middle East in
relation to the future and current climate suggested
that the predicted drying may be accompanied by
a change in the dominant precipitation mechanism
from being directly driven by storm tracks to having
a greater dependence on the upslope flow of moist
air masses (Evans, 2009). This would be consistent
with the weakening of the storm track in the southern
Mediterranean that has been predicted by several
studies (Bengtsson et al., 2006; Lionello and Giorgi,
2007; Pinto et al., 2007).
With a few exceptions, previous studies of predicted
Middle East climate change have focussed on monthly
to seasonal timescales. Here, we complement this work
by focussing on changes in daily precipitation statistics. Output from a regional climate model (RCM) is
The impact of climate change on daily precipitation statistics
used to investigate how these statistics may change by
the end of the 21st century.
193
All observations of rainfall referred to in this study are
based on rain gauge measurements. Daily rainfall data
since 1985 from nine stations along the River Jordan
were provided by the Israeli Meteorological Service
(see Figure 2 for the locations of these stations).
Global Precipitation Climatology Centre (GPCC) data
were used for larger scale comparisons between the
observed data and modelled rainfall data. The GPCC
rainfall data is 0.5 × 0.5◦ gridded product, which is
based on rain gauge measurements (Schneider et al.,
2008). The modelled temperature data were compared
to NCEP reanalysis data (Kalnay et al., 1996).
http://precis.metoffice.com/docs/PRECIS Handbook.
pdf.
The RCM used in this study had a horizontal resolution of 0.44◦ (∼44 km), which permitted a sufficiently large domain, while representing significantly
more topographic variation than is possible in a global
model. The domain was chosen to include the whole
Mediterranean, so that the cyclones that bring most of
the rain to the Middle East did not travel through the
domain boundary (see Figure 1).
Two RCM 30-year integrations are discussed here:
a baseline scenario (1961–1990) and an A2 emission
scenario (2070–2100). The A2 scenario is a ‘business
as usual’ scenario in which large increases in greenhouse gas concentrations are projected by the end of
the 21st century. The A2 scenario is described fully in
the IPCC special report on emission scenarios (IPCC,
2001). Present-day land cover was assumed for both
integrations. The future scenario included changes in
SST and CO2 .
2.2. Regional modelling for the Middle East
2.2.2. Representation of the present-day rainfall
2. Data and methodology
2.1. Observed data
2.2.1. The regional model
The climate of the Middle East is modulated by
complex topography (Goldreich, 1994). This means
that the spatial variability in precipitation cannot be
well simulated by standard resolution global climate
models, which typically have a horizontal resolution
of 2.5◦ or coarser. For this reason, a RCM was used.
The RCM chosen for this study is based on HadAM3P,
which is a global, atmosphere-only model developed at
the Hadley Centre. The Hadley Centre regional models
have been applied successfully in both the Tropics and
extra-tropics (Durman et al., 2001; Hassell and Jones,
1999).
RCMs are applied over limited area and therefore require input both at the surface and lateral boundaries of the domain. The lateral boundary conditions were derived from integrations of
HadAM3P forced with surface boundary conditions
(sea-surface temperature (SST) and sea-ice fraction), which were derived from observations and
predictions generated by the global coupled model,
HadCM3. The surface boundary conditions for the
RCM (SST, sea-ice fraction and land cover) are
based on HadCM3 predictions and observations.
For further background on the regional model, see
Figure 1 compares December–February total rainfall
and mean temperature from the baseline scenario
with observations or reanalysis data over the Middle
East and a wider domain. This is supplemented
by Table 1, which gives the bias (BIAS ), pattern
correlation (P ) and root mean square error (RMSE ) for
temperature and precipitation, both over a large area
containing the Mediterranean, southern Europe and the
Middle East, and a smaller area containing the eastern
Mediterranean alone (regions shown on Figure 1). The
statistics are defined as follows:
BIAS = M − O
N
1 (Oi − Mi )2
N i =1
(Oi − O)(Mi − M )
P=
(Oi − O)2 (Mi − M )2
RMSE =
where M is modelled data, O is observed or reanalysis
data, N is the number of points and over bars denote
mean values.
The RCM does a reasonable job of simulating
the spatial pattern of rainfall over the Mediterranean
Table I. Comparison between observed and modelled temperature and precipitation (statistics defined in the text).
DJF
MAM
JJA
SON
0.29 (0.27)
−20.1 (−2.51)
0.88 (0.71)
0.30 (0.36)
5.54 (9.94)
0.85 (0.87)
0.13 (0.32)
1.88 (1.19)
0.96 (0.92)
0.10 (0.21)
1.31 (0.63)
0.97 (0.86)
Precipitation
RMSE
Bias
Pattern correlation
0.54 (1.66)
−37 (−74.9)
0.72 (0.89)
0.49 (1.32)
1.16 (−61.4)
0.68 (0.84)
Temperature
RMSE
Bias
Pattern correlation
0.09 (0.17)
0.75 (−0.19)
0.98 (0.98)
0.09 (0.26)
0.96 (0.94)
0.98 (0.88)
The figures in brackets are for the eastern Mediterranean, and the main figures are for the whole Mediterranean (rectangular regions shown in Figure 1).
Copyright  2009 Royal Meteorological Society
Atmos. Sci. Let. 10: 192–200 (2009)
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194
E. Black
Figure 1. Regional model domain and comparison between observed and simulated rainfall and temperature for
December–February. Top row: regional model domain – each dot shows a regional model grid point. The large and small
rectangles show the location of the rest of the plots on this figure and also the areas for which the statistics given in Table 1 are
calculated. Rows 2 and 3: GPCC precipitation (left) and RCM precipitation (right). Grey indicates no data. Rows 3 and 4: ERA40
temperature (left) and RCM temperature (right). The data are gridded to ERA40 resolution for comparison.
and southern Europe. The observed rainfall maxima
over the Alps, the shore of the Black Sea and the
North Mediterranean coast are all evident in the RCM
Copyright  2009 Royal Meteorological Society
simulation, although the actual rainfall amounts are
lower than those observed. This is consistent with a
pattern correlation of 0.72 during DJF (see Table 1).
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The impact of climate change on daily precipitation statistics
195
Figure 2. How well the regional model represents the statistics of the weather for our region of interest. The bold red line is
the RCM baseline integration seasonal cycle and the fine black lines are the rainfall stations. The statistics shown are, from top
to bottom: total rainfall, rainy days, rain per rainy day, maximum rainfall, rainfall probability given rain the day before and rainfall
probability given no rain the day before. The map on the right shows the location of the rainfall stations (red stars), the RCM time
series (rectangle) and the RCM grid (black crosses).
In the other seasons, the spatial variability is as well
or better simulated (P ranges from 0.68 to 0.85).
The general underestimation of rainfall at the rainfall
maxima is reflected by the negative bias (BIAS =
−37 mm).
The spatial variability of seasonal mean temperature
is also well represented, with the model capturing the
zonal transition from high temperatures in Italy and
southern France to lower temperatures in Turkey. This
is reflected by a pattern correlation of 0.98 during DJF.
There is a positive bias in the temperature, particularly
in the summer months (BIAS = 1.88 K during JJA).
Focussing on the eastern Mediterranean, it can be
seen that the model correctly simulates the observed
zonal and meridional temperature gradients, and this
is reflected in the high pattern correlations (0.98). It
Copyright  2009 Royal Meteorological Society
also captures some aspects of the spatial variability in
rainfall including the sharp west–east gradient from
the relatively wet Mediterranean coastal region to the
arid east, and the increase in rainfall from the arid
south to a wetter climate in the Turkish highlands. The
quality of the representation of the spatial variability
is reflected by a pattern correlation of 0.89 for the
eastern Mediterranean.
Although the model captures some aspects of the
spatial variability of eastern Mediterranean rainfall, it
underestimates the amount at the peak of the rainy
season, in some areas by a factor of two or more
(BIAS = −74.9 mm during DJF). This underestimation of rainfall in Jordan and Israel during this season is a feature of other regional models, including
RegCM2, RegCM3 and MM5. In a recent study using
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MM5, with a 27 km resolution, the mean annual total
rainfall near the Mediterranean coast was ∼290 mm,
compared to an observed value of ∼550 mm (Evans,
2009). In another study where RegCM (50 km resolution) was driven by boundary conditions based on
HadCM3, the winter precipitation in Israel and western Jordan ranged from less than 100 mm to ∼250 mm
compared to the observed precipitation, which exceeds
400 mm near the Mediterranean coast (Lionello and
Giorgi, 2007). Several simulations of RegCM3 driven
with different boundary conditions exhibited similar
biases, with the annual total rainfall along the Mediterranean coast being ∼200–300 mm compared to more
than the observed 500 mm (Krichak et al., 2007).
Even a state-of-the-art global model with 20 km resolution underestimated the precipitation in eastern Jordan, although the amount of coastal rainfall was closer
to that observed (Kitoh et al., 2008).
In a study of precipitation processes using RegCM2
at 25 km resolution, the model’s underestimation of
rainfall at the peak of the rainy season was attributed
to its failure to resolve the coastal mountains (Evans
et al., 2004). The RCM used in this study had a lower
resolution (∼44 km) and was thus even less able to
resolve the coastal mountains.
Figure 2 compares the seasonal cycles of various
rainfall statistics for the baseline scenario with data
from nine stations along the River Jordan in Israel.
Although the underestimation of rainfall evident in
Figure 1 is clear, the figure of the seasonal cycle
in total rainfall shows that the model replicates the
general pattern of a rainy winter and dry summer.
Most rainfall in Jordan and Israel results from the
Mediterranean cyclones as they move eastward across
the Mediterranean and over the Middle East. This
means that during the rainy season, there tends to be
several days of rain as a cyclone passes, followed by a
dry period. The frequency and duration of these rainy
events are reflected respectively by the probabilities
of rain given no rain the day before and rain given
rain the day before. It should be noted that, like most
climate models, the RCM generates small amounts on
almost every day. The probability of rain given rain the
day before and rain given no rain the day before, and
hence the number of rainy days, are well simulated
by the model for the winter season. However, the
mean rain per rainy day and maximum rainfall are
far lower in the model baseline scenario than in
observations. This suggests that the underestimation
in rainfall by the RCM reflects an underestimation of
rainfall intensity.
It can be seen that there are rainy days during the
summer and hence the rainfall probabilities do not fall
to zero, which means that there are still some days
where there is more than 0.1 mm of rain during the
summer. Rainfall on these days is, however, very low,
and summer rainfall does not contribute significantly
to the annual total. Moreover, there is little difference
between the probabilities of rain given rain the day
before and rain given no rain the day before, which
Copyright  2009 Royal Meteorological Society
E. Black
suggests that the summer rainfall that occurs in the
model results from small scale isolated events rather
than from large-scale, long duration rainfall events
seen in the winter. It is not possible to make a formal
comparison between summer rainfall in the model and
observations because the observed summer rainfall
was not recorded. However, the summer season is
known to be extremely dry in the Middle East, and
rainy days are rare.
Overall, the regional model simulations presented in
this study are of similar quality to other recent regional
model simulations of the Middle East’s present-day
climate. Temperature and some aspects of the observed
spatial variability and seasonal cycle in rainfall are
well simulated. In particular, the frequency and duration of rainy events are well simulated, increasing
confidence in predictions of changes in these statistics.
In contrast, because rainfall intensity is poorly simulated, predictions of changes in this variable should be
regarded with caution. The disparity in the quality of
the model’s simulation of different rainfall statistics is
a strong argument for considering individual statistics
separately – the approach taken in this study.
3. Predicted rainfall changes for the Middle
East
3.1. Changes in the monthly mean
Figure 3 shows the change in the monthly mean
rainfall over the Mediterranean and southern Europe.
In October, the predicted changes in Middle East
and eastern Mediterranean rainfall are small and
insignificant. In November, a significant decrease in
precipitation is predicted in Jordan and Israel. The
largest changes are predicted at the peak of the
rainy season, December and January, when significant
reductions in rainfall (of the order of 40%) are
predicted over much of the eastern Mediterranean
region. By February and March, the rainfall changes
predicted for the Middle East are small both in
magnitude and percentage terms.
On a larger scale, in October and November, the
eastern Mediterranean is predicted to get wetter and
the predicted decrease in precipitation alluded to above
is localised to the Middle East. In December and January, rainfall is predicted to significantly decrease over
the whole Mediterranean. The pattern is more complicated in February and March, with some parts of the
Mediterranean predicted to get wetter, and other parts
to get drier.
These results are broadly consistent with wider
surveys of global models included in the IPCC fourth
assessment, which predict a decrease in annual total
rainfall in the Middle East by the end of the 21st
century under an A2 scenario (Evans, 2008; Mariotti
et al., 2008). Several regional and high resolution
global model studies also predict a significant drying
in the eastern Mediterranean by the end of the 21st
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The impact of climate change on daily precipitation statistics
197
Figure 3. Predicted changes in monthly mean rainfall for October–March. Top set: the absolute change in rainfall over the whole
Mediterranean, Middle East and southern Europe. Bottom set: percentage change in rainfall for the eastern Mediterranean only.
The black dots indicate statistical significance at the 95% level as determined by a Student’s t-test.
century (Lionello and Giorgi, 2007; Kitoh et al., 2008;
Evans, 2009).
3.2. Changes in the daily statistics
Figure 4 shows how the probability of rain has
changed for January, the month when the largest
change in rainfall is predicted. It can be seen that in
Jordan there is a small but significant reduction in the
probability of rain, both given rain the day before, and
given no rain the day before. In Israel, there is a significant reduction in the probability of rain given rain
the day before, and a slight increase in probability of
rain given no rain the day before. On a wider scale,
Copyright  2009 Royal Meteorological Society
over much of the Mediterranean, there is a significant
reduction in the probabilities of rain both given rain
and given no rain the day before, which means that
both the duration and frequency of rainy events are
predicted to decrease.
The seasonal cycles in several daily precipitation
statistics for 2070–2100 and 1960–1990 near the
Jordan River are compared in Figure 5. It can be seen
that the shape of the seasonal cycle in total rainfall
is predicted to change markedly, with more rain in
April and May, than in November, December, January
and February. These changes are associated with a
reduction in rainy days and rainfall probabilities during
the boreal winter. This strong decrease of precipitation
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E. Black
Figure 4. The changes in daily rainfall probabilities for January only. Left column: the probability of rain given no rain the day
before. Right column: the probability of rain given rain the day before. Top row: absolute changes for the whole Mediterranean,
Middle East and southern Europe. Bottom row: percentage changes for the eastern Mediterranean only. Statistical significance at
the 95% level is indicated by black dots.
in winter accompanied by a smaller or insignificant
change in the spring is also seen in a study using MM5
(Evans, 2009).
Comparison between the mean rain per rainy day
and maximum rain per rainy day for the control
and A2 scenario suggests that under an A2 scenario,
although the maximum daily rainfall is similar to the
control scenario, the rainfall intensity is, on average,
a little lower. The changes in rain/rainy days are,
however, not significant and should be regarded with
caution because the regional model does a poor job of
simulating rainfall intensity (see Section 2.2).
4. Mechanisms for changes in Middle East
precipitation
There have been several studies of the causes of
precipitation decreases in the Middle East. A study
using the ECHAM5 model predicted that, in a future
climate, the Northern Hemisphere storm tracks will
move polewards, causing a reduction in the strength
of the Mediterranean storm track (Bengtsson et al.,
2006). This would be expected to cause a reduction
in large-scale rainfall in the Middle East. These
results are consistent with a regional model study,
which suggested that a predicted decrease in rainfall
in the southern Mediterranean, including the Middle
East, was caused by a reduction in the number of
cyclones crossing the Mediterranean (Lionello and
Giorgi, 2007). Furthermore, a study of precipitation
processes in the Middle East using a regional model
suggested that the weakening of the Mediterranean
storm track is reflected by a change to the dominant
mechanism of rainfall in the Middle East from a direct
dependence on storm tracks to increased importance of
Copyright  2009 Royal Meteorological Society
precipitation triggering by upslope moist air masses
(Evans, 2009).
The changes in the statistics of the weather reported
here are consistent with the previously published work
on climate change of the Mediterranean (see for
example Bengsston et al., 2006; Lionello and Giorgi,
2007). Both the frequency (P (rain/no rain the day
before)) and duration (P (rain/rain the day before)) are
predicted to reduce. This is indicative of a reduction
in the proportion of rainfall delivered by large-scale
Mediterranean cyclones, which would be consistent
with the reduction in the strength of the Mediterranean
storm track.
In general, the predicted reduction in the duration
of rainy events (P (rain/rain the day before)) is greater
in absolute terms than the predicted reduction in the
frequency of rainy events (P (rain/no rain the day
before)). This raises the possibility that the stronger
heating associated with surface temperature increases
will cause an increase in the amount of convective
precipitation relative to large-scale precipitation.
5. Conclusions
Under an A2 emissions scenario, a significant reduction in boreal winter precipitation in Jordan and Israel
is predicted by the end of the 21st century. This
reflects reductions in the frequency and duration of
rainy events and hence the number of rainy days. The
model does a reasonably good job of simulating these
variables, which lends credence to these predictions.
A reduction in the amount of rainfall on rainy days is
also predicted, but this result should be treated with
caution because of the model’s poor simulation of rainfall intensity. Although a study of rainfall mechanisms
Atmos. Sci. Let. 10: 192–200 (2009)
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The impact of climate change on daily precipitation statistics
199
Figure 5. Seasonal cycle of rainfall statistics for the region shown in Figure 2. From top to bottom: total rainfall, rainy days, mean
rain per rainy day, maximum rain per rainy day, probability of rain given no rain the day before and probability of rain given rain
the day before. Red lines are for the baseline scenario and the black lines are for the future scenario. The plots on the right show
the difference between the baseline and future scenarios. Filled bars indicate significant differences at the 95% level as determined
by a Student’s t-Test.
is beyond the scope of this article, it should be noted
these results are consistent with published work on
the weakening of the Mediterranean storm track under
future climate scenarios.
Acknowledgements
Some of the data used in this study were kindly provided
by the Israel Meteorological Service. The lateral boundary conditions for the regional climate integrations were
provided by the Hadley Centre regional modelling group.
Copyright  2009 Royal Meteorological Society
All members of this group, particularly David Hassell,
Richard Jones and David Hein, have always been ready
to help and advise with the regional modelling. Charles
Williams carried out much of the preliminary work on setting up the regional model. Discussing these results with
the other members of the WLC meteorology project – David
Brayshaw, Brian Hoskins and Julia Slingo – has been invaluable. This paper greatly benefitted from the comments of two
anonymous reviewers. Emily Black is funded by the Leverhulme Trust Water, Life and Civilisation project and NCASclimate.
Atmos. Sci. Let. 10: 192–200 (2009)
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