Post on 01-May-2020
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Nile Forecast Center (NFC) Planning Sector Ministry of Water Resources and irrigation (MWRI)
Development of Climate Change Scenarios for the Arab Region using a Regional Climate Model
Final Consultancy Report
Mohamed Elshamy
November 2011
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1. Introduction
1.1 Background
Water is a finite and vulnerable resource that is essential to all forms of life on
earth. Worldwide water is becoming an increasingly scarce resource. In past
times, at least in non-desert areas, water availability was not questioned. The
Arab Region is generally characterized as a water-scarce region. With the
increasing population on one hand and environmental degradation on the other,
pressure has been intensified on the available water resources in the region and
has lead to over-abstraction of the valuable resource in many of its
countries.(Hough and Jones, 1997)
Climate change, yet, adds another dimension to the water scarcity problem of the
region. One on hand, several studies indicate rainfall reductions around the
Mediterranean basin affecting water availability for many of Arab countries. On
the other hand, water resources in Egypt, Sudan, and Somalia will be affected by
changes in rainfall regimes over the Horn of Africa as manifested in river
discharges (The Nile, Jubba and Shebelli all originate in Ethiopia). Other climate
change impacts on water resources in the region can be expected in terms of
increased frequencies of droughts and floods resulting from intensified rainfall
storms in short periods; a generally-expected consequence of the accelerated
hydrological cycle in a warmer world (IPCC, 2007).
However, a complete assessment of the impact of climate change on the water
resources in the region is lacking. IPCC assessments separate the region into
their constituent parts in Africa and Asia. Therefore, generating detailed climatic
scenarios for the region is necessary for the assessment of climate change
impacts.
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1.2 Objectives
The main objective of this consultancy is to prepare digital climate change maps
of simple indicators of renewable water resource potentials based on detailed
climatic scenarios for the whole of the Arab region as obtained from the results of
a regional climate model. The objectives of this report is to document the process
of generating these scenarios, presenting an initial analysis of the results, and
putting forward recommendations for the future use of those scenarios.
1.3 Report Layout
This report is divided into four chapters. After this introduction, Chapter 2
provides the details of the climate change scenario generation process. Chapter
3 presents the results and provides an initial analysis of the projected climate
change impacts. Finally, Chapter 4 provides conclusions and recommendations
focusing on future use of the generated scenarios.
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2. Climate Change Scenario Generation
The generation of detailed climate change scenarios is lengthy and time
consuming process (Figure 2.1). It starts by the development of global socio-
economic scenarios to project the future use of energy from the different sources
as well as the global population and development projections. These are then
used to force global climate models (GCMs) to project the climate on the global
scale. To obtain the regional detail, the results of GCMs need to be downscaled
as the resolution of GCMs is too coarse to be used for impact models.
Downscaling is either done using statistical methods or dynamical methods (i.e.
Regional Climate Models – RCMs). The last step is to use the downscaled output
to obtain the impacts on the selected sector. For example, in terms of water
resources, the results are used to force hydrological models to assess the
impacts. This study focuses on the fourth step, i.e. providing regional detail using
a regional climate model to assist the impact modeling community. The following
sections provide some details about these steps with more focus on the fourth
step as implemented in this study.
Figure 2.1 Construction of Climate Change Scenarios Source: Hadley Centre (2001)
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2.1 Emissions and Concentrations Scenarios
Based on assumptions on future global socio-economic developments, different
emissions of greenhouse gasses and aerosols can be expected. Different
emissions of greenhouse gasses lead to different future concentrations of these
gases in the atmosphere. The IPCC published a special report on emissions
scenarios in 2000 (IPCC, 2000). In this report the Standard Reference Emission
Scenarios (SRES) were presented. The SRES have been constructed to explore
future developments in the global environment with special reference to the
production of greenhouse gases and aerosol precursor emissions. The scenarios
are based on story-lines of how the world may develop in the future. Four
families of scenarios adopted along two axes. On one axis the level of
globalization of the solutions varies (between global and regional), while on the
other axis the solutions may come from increase of material wealth or from
sustainability. Figure 2.2 illustrates the approach.
Figure 2.2 SRES Scenario Storylines (IPCC, 2001)
SRES A1: a future world of very rapid economic growth, low population growth
and rapid introduction of new and more efficient technology. Major underlying
themes are economic and cultural convergence and capacity building, with a
substantial reduction in regional differences in per capita income. In this world,
people pursue personal wealth rather than environmental quality. The A1
scenario family develops into three groups that describe alternative directions of
technological change in the energy system. The three A1 groups are
Emphasis on sustainability and equity
Emphasis on material wealth
Globalisation
Regionalisation
A1 BalancedA1 FossilA1 Technology
B1
B2A2 Regional solutions
Economic Golden Age Sustainable development
Cultural diversity
Emphasis on sustainability and equity
Emphasis on material wealth
Globalisation
Regionalisation
A1 BalancedA1 FossilA1 Technology
B1
B2A2 Regional solutions
Economic Golden Age Sustainable development
Cultural diversity
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distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil
energy sources (A1T), or a balance across all sources (A1B).
SRES A2: a very heterogeneous world. The underlying theme is that of
strengthening regional cultural identities, with an emphasis on family values and
local traditions, high population growth, and less concern for rapid economic
development.
SRES B1: a convergent world with rapid change in economic structures,
"dematerialization" and introduction of clean technologies. The emphasis is on
global solutions to environmental and social sustainability, including concerted
efforts for rapid technology development, dematerialization of the economy, and
improving equity.
SRES B2: a world in which the emphasis is on local solutions to economic,
social, and environmental sustainability. It is again a heterogeneous world with
less rapid, and more diverse technological change but a strong emphasis on
community initiative and social innovation to find local, rather than global
solutions.
0
5
10
15
20
25
30
1990 2020 2050 2080
Glo
ba
l em
iss
ion
s (
GtC
)
A1B
A1F
A1T
A2
B1
B2
Figure 2.3 CO2 emissions and global atmospheric concentrations for different SRES scenarios (IPCC, 2000)
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Associated to all these scenarios are emissions of greenhouse gases and
concentrations of greenhouse gases in the atmosphere (Figure 2.3). More
extensive descriptions on the assumptions of these scenarios can be found in the
publications of the IPCC (e.g., IPCC 2000).
After the release of the IPCC fourth assessment report in 2007, the IPCC
requested the development a new set of scenarios to be used for the fifth
assessment report planned to be released in 2013 (Moss et al., 2008). These
scenarios are referred to as RCPs (Representative Concentration Pathways) and
are being developed using a different approach from that used for the SRES
report. Global climate centers are currently running global simulations using
these scenarios. According to IPCC, the results of GCMs will be available for the
modeling community by the end of this year (2011). For this study, the SRES
A1B scenario is used.
2.2 Global Climate Modelling
The next step is to convert those GHG and aerosol concentrations to climate
over the globe. First the radiative forcing (the radiation imbalance caused by a
GHG/aerosol) of those gases are computed using either simple models or more
complex radiative transfer calculations, usually embedded within Global
Circulation Models (GCMs). GCMs are the most sophisticated tools to assess
changes in climate. These are numerical models are referred to as AOGCM
(Atmospheric Ocean General Circulation Models, or simply GCMs). Such models
describe the earth's climate and the oceans' circulation in 3-dimensions. The
models are based on physical laws of conservation of mass, energy and
momentum. Figure 2.4 shows the general layout of such a model.
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These models are able to
provide various weather
variables such as air
pressure, rainfall,
temperature, wind speed,
humidity, etc. Currently an
increasing number of climate
models exist. Although all
these models are based on
physical laws, the results of
the models differ. This
occurs particularly for rainfall
simulations. This is partly
caused by the coarse spatial
scale of the models that
does not allow for an
accurate representation of the earth's surface. Hence, for impact assessment the
IPCC recommends the use of at least three climate models. Figure 2.5 shows the
average global temperature rise for different SERS scenarios and the range
produced by different climate models.
Figure 2.4 Layout of a Global Climate model
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Figure 2.5 Range of global temperature rise for different SERS scenarios according to different climate models.
2.3 Regional Climate Modelling
As mentioned above, GCMs are the main tool to generate future climatic
scenarios in response to emission scenarios. However, their spatial resolution is
still coarse for impact studies. There are several methods to generate detailed
climatic scenarios from GCM results. These methods a generally categorized as
statistical and dynamical downscaling. Statistical downscaling is based on
establishing statistical relationships between the required fine scale variables
(e.g. temperature and precipitation) and coarse scale GCM variables but it only
provides downscaled data for the selected variables. Dynamic downscaling, on
the contrary, uses a physically based model to provide the details for all
variables. With respect to a GCM, an RCM analysis can help in identifying the
modification of local climate induced by the interaction between changes in the
general circulation pattern of the ocean and the atmosphere (depicted by the
GCM results) and the regional features (orography, land-use, vegetation, etc.).
The UK Met Office Hadley Centre has developed a regionalized version of its
GCM called PRECIS which is used to perform the regional downscaling in this
study. The following sub-sections give the details of the process.
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2.3.1. Domain Selection
The first step in the regional modeling exercise is to define the domain to
downscale. Particular care needs to be taken with the design of the model
domain. If the domain is too small it may prevent the proper internal development
of reliable high resolution detail. If the domain is too large it will increase
computational expense without adding further information. Over large scales, the
RCM solution may also diverge from that of the GCM, complicating the
interpretation of the climate change projections (Jones et al., 1997). In addition,
the domain edge should avoid steep topography as the noise generated by
interpolation can propagate inside the domain.
Figure 2.6 Extents of the PRECIS Arab Domain
For this study, the selected domain (Figure 2.6) covers the whole of the Arab
region and extends eastwards and westwards to include parts of the Indian and
Atlantic Oceans, the main sources of moisture into the region. However, this
makes it a relatively large region (220 x 150 pixels at 50 km resolution) which
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required relatively high computational resources for running the climatic
scenarios. The dark shaded rim around the region is used in the calculations but
is excluded from the analysis as it is used to relax the boundary conditions used
to force the RCM at the regions of the boundary.
Editing of the RCM domain was necessary. Most of the inland water bodies –
such as Lake Victoria - would normally be considered to be at sea level by the
PRECIS system with negative consequence in terms of circulation. All the inland
water bodies were edited to correct their altitude above sea level. Similarly the
land sea mask was addressed to reflect the local shape of the coastline.
2.3.2. Selection of Scenarios
This application considers uncertainties in the regional climate response to global
climate change through the construction of an ensemble of 5 RCM runs, but not
those arising from different emissions scenarios nor those arising from different
downscaling methods (e.g. different RCMs). Results from the GCM were all
derived for one emission scenario (SRES A1B) as previous studies (e.g.
Elshamy et al., 2009) indicated that the uncertainty across climate models is
much larger than that across emission scenarios, at least till 2050. The sudy
followed the UK Met Office (UKMO) procedure to select a subset of 5 scenarios
out of the 17 QUMP ensemble members (UKCP09 - Murphy et al., 2009) for
which boundary date are available from the UKMO. The following sections
discuss how these ensemble members have been selected following the UKMO
guidelines.
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As the selected domain is relatively large (220 x 150 pixels at 50 km resolution),
it is inhomogeneous for analyzing patterns of precipitation and temperature (the
most important variables in terms of water resources assessments). Therefore,
three relatively homogeneous sub-regions have been selected as shown in
Figure 2.7. Region 1 covers the Horn of Africa where most of the flow of the Nile
and the Jubba-Shebelli river systems is generated. It also covers the Gulf of
Aden, Yemen, and parts of Oman, Saudi Arabia and UAE. This region is
characterized by summer monsoon precipitation. Region 2 covers a large part of
the Mediterranean coasts in Arab countries and is characterized by winter rainfall
and moderate temperatures. Region 3 covers Northwest Africa characterized by
summer monsoon with relatively higher temperature.
The analysis focused on precipitation and temperature as two of the most
important variables in terms of water resources. First the results of the 17 QUMP
members for the baseline period 1961-1990 are compared to (quasi-) observed
datasets to see how the GCM is performing in reproducing the current climate of
Region 1
Region 2Region 3
Figure 2.7 Validation Sub-Regions within the Arab Domain
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the region. The spatial averages of the selected variables over the three sub-
regions are used in the comparison. Then, the range of future predictions (2021-
2050) is inter-compared to select the ensemble members that cover the
uncertainty range as widely as possible. This is based on the climatic sensitivity
(temperature change) and precipitation extremes of the QUMP members.
Figure 2.8 shows the mean monthly precipitation climatology over the baseline
period resulting from the 17 QUMP ensembles versus quasi-observed
precipitation from CMAP dataset. For region 1, most ensemble members mimic
the observed bi-modal distribution of rainfall distribution over the region. Although
the monsoon over the Horn of Africa has a summer peak, other parts of the
region have a spring peak. Some members of the ensemble show a third peak
but in general the ensemble members are spread around the observed. This is
generally the case for the two other regions although all members overestimate
the dry season rainfall over region 2 and spring rainfall over region 3. In general,
the GCM simulations are considered satisfactory in terms of rainfall over the
three sub-regions.
The ensemble members perform better for temperature as they encompass the
observed set (from ERA40) for the three sub-regions as shown in Figure 2.9. The
uncertainty range for temperature is thus small compared to precipitation
especially for region 2. This is a common observation amongst previous studies
as GCMs tend to predict temperature better than precipitation.
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Figure 2.8 Baseline Mean Monthly Rainfall Distributions over the three Sub-Regions
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Figure 2.9 Baseline Mean Monthly Temperature Distributions over the three Sub-Regions
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All ensemble members predict
temperature increases for all
three regions. Precipitation
changes are more variable
but all ensemble members
predict rainfall increases for
region 1 while most members
predict reductions in region 3.
The signal is mixed for region
2. Figure 2.10 shows the
ranges of temperature and
precipitation changes as
annual averages for all
ensemble members for the
three regions.
In order to select a subset of
the ensemble that captures
the greatest range, members
producing the maximum and
minimum changes for each
variable were extracted for
temperature and precipitation
changes as annual averages.
For precipitation, this was
repeated using the mean
change during the wet months
as well (Figure 2.11). The
results are summarized in
Table 2.1. Figure 2.10 Predicted Mean Annual Precipitation Changes (%) versus Mean Annual Temperature
Changes (°C) for the three Sub-Regions
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For this analysis, Q0 has
been excluded as it is
included in the selected
subset in all cases to
represent the unperturbed
physics ensemble member.
Figure 2.11 Predicted Mean Wet Season Precipitation Changes (%) versus Mean Wet Season
Temperature Changes (°C) for the three Sub-Regions
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Table 2.1 Ensemble Members producing Extreme Changes
Region max ∆P Annual
min ∆P Annual
max ∆P Wet Season
min ∆P Wet Season
max ∆T Annual
min ∆T Annual
1 Q3 Q15 Q3 Q11 Q13 Q1 2 Q11 Q16 Q11 Q16 Q12&Q16 Q3 3 Q6 Q16 Q6 Q10 Q16 Q3
Based on the above analysis, the following ensemble members are selected:
Q0: Unperturbed Physics member
Q3: Low sensitivity member for most regions in addition to producing
maximum rainfall increases for region 1
Q16: High Sensitivity member for most regions in addition to producing the
highest rainfall reduction for regions 2 and 3.
Q6: Member producing highest rainfall increases for region 3
Q11: Member producing highest increases for region 2 while producing
lowest wet season changes for region 1.
After discussions with the UKMO, they suggested excluding Q3 because it did
not satisfactorily reproduce the precipitation cycle for region 1. They advised that
it should be replaced by two other QUMP members: Q2 and Q8 to capture both
the low sensitivity/temperature and the high precipitation parts of the response
range. Therefore, the final selection contains 6 ensemble members: Q0, Q2, Q6,
Q8, Q11, and Q16. However, given the limited resources allocated to this study
(including time) versus the high computational cost involved, only three scenarios
were completed (Q0, Q2, and Q6) in compliance with the contract.
2.3.3. Data Acquisition
Data was initially obtained from the UKMO for Q0 as the unperturbed physics
ensemble member that should be included in the ensemble in all cases. After the
set of scenarios have been selected in consultation with the UKMO as explained,
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the necessary data were obtained from the UKMO for the 5 additional scenarios
(Q2, Q6, Q8, Q11, Q16). These data (for all 6 scenarios) consist of:
- Initial conditions (from GCM runs)
- Time varying boundary conditions and GHG/aerosol concentrations (from
GCM runs)
- Ancillary data (e.g. sea surface temperature, sea ice fraction, etc.)
2.3.4. Setting up and Running RCM Simulations
One of the common problems in assessing the impacts of climate change is the
definition of the baseline of past averages against which to compare future
projections. It is commonly assumed (Jones et al., 1997) that a 30-year period is
the minimum needed to capture important aspects of the low frequency variability
of the climate. Therefore, simulations were set-up for two 31-year periods for
each scenario: a baseline period spanning 1/12/1959-1/1/1991 and a future
period spanning 1/12/2019-1/1/2051. The first 13 months of each simulation
(baseline and future) were considered as spin-up periods to eliminate the effect
of initial conditions.
2.3.5. Processing Outputs
The most important variables for water resources analysis are precipitation,
temperature, and evapotranspiration. Hydrological models typically require
potential evapotranspiration which is not directly produced by climate models
(they produce actual evapotranspiration). Thus, potential evapotranspiration
needs to be calculated from other variables depending on the calculation
method. For this analysis, the Penman-Monteith method (Allen et al., 1998) was
used to calculate PET based on long-mean temperature, radiation, humidity, and
wind speed outputs from the RCM simulations.
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Actual evapotranspiration and runoff are direct outputs from the RCM which were
also processed as indicative variables of water resources potential for the region
(in addition to precipitation and potential evapotranspiration). However, it should
be noted that PRECIS does not include a runoff routing component, and thus its
runoff output should be handled with care. Estimated runoff from PRECIS can be
used afterwards for comparison with results of hydrological models either at the
basin-scale (for some of the region main basins such as the Nile, Euphrates,
etc.) or at the region scale if a distributed model of the region (such as VIC) is to
be constructed for the region. In either case, precipitation, temperature, and
potential evapotranspiration will be the basic inputs. Rainfall is also an important
input in assessments of groundwater recharge, an essential resource in the
region.
Actual evapotranspiration is the sum of four components: evaporation from soil,
evaporation from the vegetation canopy, transpiration from the vegetation, and
sublimation from ice covering the soil or vegetation surfaces. The last component
is not important for the Arab region. Runoff is also the sum of two components:
surface and sub-surface runoffs. For both variables, the respective components
are summed and the analysis is done for the total.
For each of the above mentioned variables, and outputs necessary to calculate
them, the long-term mean monthly fields were calculated for the two 30-year
periods: 1961-1990 and 2021-2050. Then, monthly delta change factors are
calculated for each of the variables. The methodology for calculating these DCFs
is detailed in the next section.
2.3.6. Calculating Delta Change Factors
As mentioned earlier, regional and global climate change models often have
systematic biases between the observed present climate and that simulated by
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the climate model. The delta change method is often used to correct such biases.
Briefly the climate is simulated for a control period (typically a 30-year period,
e.g. 1961-1990). Monthly delta change factors (DCFs) will be calculated for
rainfall (ratios – Equation 1), temperature (differences – Equation 2),
evapotranspiration (ratios), and runoff (ratios) from the baseline period (1961-
1990) and the future period (2021-2050).
jP
jP
baseline
futurejP
[1]
Where j is the month, where the ^ (tilde) sign represents the average, Pfuture is the
future rainfall and Pbaseline is the precipitation in the reference or baseline case.
The delta (∆) factors are calculated as the average over the 30 years for each
month. The same equation is applied to PET, runoff, and actual
evapotranspiration.
Similarly for the temperature
jTjT baselinefuturejT
[2]
The main difference between temperature and precipitation (and other variables)
is that the delta factors for precipitation are relative whereas the delta factors for
temperature are absolute.
For rainfall and runoff, there are some regions, especially in the Arab region,
where the baseline is nearly zero and this poses a problem when calculating
relative DCF (the problem of division of zero). To overcome this problem, a
threshold of 1mm is applied to exclude those areas from calculating the DCFs
and assigning no data to those areas. Those areas vary in extents from a month
to another and from a scenario to another.
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2.3.7. Developing Delta Change Maps
The last step in the preparation of the scenarios is their presentation in an easy
form that can be utilized by both the climate community and impact assessment
community. The climate modeling community is used to netcdf format and
therefore the long-term monthly averages for the baseline and future periods as
well as the DCFs are provided in this format. For the impact assessment
community, e.g. hydrologists, and for easy presentation of the results, DCFs are
converted into GIS raster format.
2.4 Impact Assessment
Impact assessment is the last step in the analysis and depends on the studied
impact. The detailed climate change scenarios were developed with hydrological
impacts in mind and this is reflected in the selected set of variables processed.
The outputs of the RCM can thus be used to prepare inputs to hydrological
models either at the basin-scale (for some of the region main basins such as the
Nile, Euphrates, etc.) or at the region scale if a distributed model of the region
(such as VIC) is to be constructed for the region. The variables presented can
help also in assessing impacts on agriculture, on water demands, and on several
other sectors, especially those related to water resources. However, impact
assessment is out of the scope of this study.
2.5 A Note on Uncertainty
This analysis considered uncertainties in the regional climate response to global
climate change through the construction of an ensemble of 6 RCM runs, but not
those arising from different emissions scenarios nor those arising from different
downscaling methods (e.g. different RCMs). As indicated earlier, results from the
GCM were all derived for one emission scenario (SRES A1B) as previous studies
(e.g. Elshamy et al., 2009) indicated that the uncertainty across climate models is
much larger than that across emission scenarios, at least till the 2050s. It is worth
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noting that in assessing the uncertainties in predicted climate impacts, that the
uncertainty in climate projections represents only a part, albeit significant, of the
total uncertainty, (Buontempo et al., 2010). The extent of hydrological impacts
due to climate change will depend on the dominant hydrological processes and
also on the feedbacks between the hydrological system and the atmosphere. The
impact uncertainty must also consider the uncertainties in hydrological models
used for impact projections, and in the observed data used to calibrate them.
Integration of these results with results from other regional climate models based
on the same or other GCMs and emission scenario combinations will allow better
characterization of uncertainty cascade. Thus, this study would complement
rather than replicate other studies using other downscaling methods including
other RCMs.
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3. Results and Analysis
3.1 Validation of Baseline Results
The first step in the analysis is to verify that the RCM is reproducing the baseline
climate. For this purpose, CRU (Climate Research Unit of the University of East
Anglia) data is used as the ground truth. The latest version of the CRU dataset
(version 3) was obtained from the British Atmospheric Data Centre (BADC). This
dataset is based on station observations and comprises the following variables at
a monthly time step covering the period 1901-2006:
- Temperature (mean, maximum, minimum)
- Diurnal Temperature Range
- Precipitation
- Wet day frequency
- Frost day frequency
- Vapour Pressure
- Potential Evapotranspiration
- Vapour Pressure
The CRU dataset has a resolution of 0.5° in both latitude and longitude directions
and covers land areas only. Figure 3.1 and Figure 3.2 compare the mean
monthly temperature and precipitation (respectively) from the unperturbed
ensemble member (Q0) to that of the CRU over an extended baseline period of
50-years (1951-2000). Both figures show that the spatial and temporal patterns
of both variables are broadly similar. However, there are still differences. For
example, Q0 temperature over the Arabian Peninsula is overestimated in
summer months (July, August). For precipitation, the areas of maximum
precipitation (e.g. equatorial regions) are all similar. Therefore, the RCM can be
trusted to project the climate.
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CRU (1951-2000) Q0 Baseline (1951-2000)
Jan
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July
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Figure 3.1 Simulated Baseline Temperature for Q0 vs. CRU Data (°K)
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CRU (1951-2000) Q0 Baseline (1951-2000) Ja
nua
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Feb
ruar
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A
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Jun
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Figure 3.2 Simulated Baseline Precipitation for Q0 vs. CRU Data (mm/mon)
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3.2 Projected Changes
3.2.1. Temperature
Figure 3.3, Figure 3.4, and Figure 3.5 compare the temperature between the
baseline and future periods for Q0, Q2, and Q6 respectively. As can be seen,
there is a consensus among the three scenarios on temperature increase over
the whole domain and especially over the Arabian Peninsula in the summer
months. The differences between the three scenarios are generally small.
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Baseline Future
Jan
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Figure 3.3 Simulated Baseline and Future Mean Temperature for (°K) Q0
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Baseline Future
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July
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Figure 3.4 Simulated Baseline and Future Mean Monthly Temperature for (°K) Q2
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Baseline Future
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Figure 3.5 Simulated Baseline and Future Mean Monthly Temperature (°K) for Q6