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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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Ap

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May

Ju

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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

ry

Feb

ruar

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arch

A

pri

l M

ay

Jun

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July

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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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F

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Ap

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31

July

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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

Jan

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July

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Figure 3.5 Simulated Baseline and Future Mean Monthly Temperature (°K) for Q6