The Impact of Climate Change on the Hydro-Climate of Malaysia Based on IPCC Fifth Assesment Report.

THE IMPACT OF CLIMATE CHANGE ON THE HYDRO-CLIMATE OF
MALAYSIA BASED ON IPCC FIFTH ASSESSMENT REPORT

2021

Cetakan Pertama/First Printing 2021

Hak Cipta Institut Penyelidikan Air Kebangsaan Malaysia (NAHRIM), 2021

Copyright National Water Research Institute of Malaysia (NAHRIM), 2021

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THE IMPACT OF CLIMATE CHANGE ON THE HYDRO-CLIMATE OF MALAYSIA BASED ON IPCC
FIFTH ASSESSMENT REPORT
Ir. Mohd Zaki Mat Amin
Ir. Azman Mat Jusoh
Noor Hisham Ab Ghani
Nurul Huda Md Adnan
Zurina Zainol
Thian Siaw Yin
Nur Aiza Mohamad
Mustafa Levent Kavvas
Ali Ercan
Kei Ishida
Mohd Ghazali Omar

ISBN 978-967-0382-44-9

MESSAGE FROM THE MINISTRY OF ENVIRONMENT AND WATER

YB Dato’ Sri Tuan Ibrahim bin Tuan Man YB Senator Dato’ Dr Ahmad Masrizal
Minister of Environment and Water bin Muhammad

Deputy Minister of Environment and Water

Climate change has emerged as one of the greatest challenges of the 21st century. The impact
of climate change is extremely disastrous, with ice caps melting, sea levels rising and oceans
becoming globally warmer. Climate change poses a fundamental risk to human beings, flora
and fauna and in general, the whole ecosystem. The climate and local weather all around the
world are becoming more extreme and erratic, and Malaysia is no exception to experiencing
impacts such as the 2014 great floods in the East Coast States of Peninsular Malaysia as well
as droughts in the year 1998 and 2016. All these disasters result in disruptions to the economy
and daily activities as well as enormous loss of property.

These climatic changes, together being intensified by non-climatic drivers, are expected to
pose further greater threats to the resiliency and sustainability of our resources and socio-
economic developments. It is highly essential to project and predict the future climatic
conditions and the security level especially of Malaysia’s water resources and hydrologic
regimes.

Therefore, this notable achievement by NAHRIM in producing regional projections of future
hydro- meteorological forecast will benefit the government, policymakers as well as local
communities to plan and prepare adequate actions in addressing the impacts of climate
change. This effort is undoubtedly vital in bridging the gaps between scientific knowledge
and practical implementation. I am confident that this report shall be a basis in our support
for United Nations Sustainable Development Goals (SDGs) particularly, in Chapter 13:
Climate Action.

I am hopeful that this report will provide a valuable window of information particularly to
enhance our knowledge and understanding the impacts and vulnerabilities of climate change
on our hydro- meteorological conditions. It is important to acknowledge that this report is a
collaborative achievement, and I would like to express my sincere appreciation to all the
experts who have in one way or another, kindly shared their priceless knowledge, experience
and hard work towards the successful publication of this report.

Congratulations to the management of NAHRIM, and my utmost anticipation that this report
shall be one of the main references for further downstream researches and development
projects in leveraging and mainstreaming climate change both, at the national and local
levels.

PREFACE

Dato’ Seri Ir. Dr. Zaini bin Ujang Dato’ Ir. Dr. Hj. Md. Nasir bin Md. Noh
Secretary General Director General

Ministry of Environment and Water National Water Research Institute of
Malaysia

As observed in the recent decades, hydro-meteorological trends are changing,
extreme events are becoming more frequent with higher magnitudes, producing
catastrophic impacts to the economy, social and environment. At the same time, the
global climate change agenda has become a key issue in water management all over
the world, thus the impacts of climate change need to be reflected at regional or local
scale in order to enhance planning, development and management for a climate-
resilient future.

As one of the main institutions in Malaysia that pioneering R&D in climate change
regional downscaling, and vulnerability and adaptation assessment on water
resources since 2002, NAHRIM has carried out numerous research projects
particularly pertaining to climate change adaptation while continuously supporting
the country’s national and international commitments and agendas.

Thus in 2018, NAHRIM continued to update its Regional Hydroclimate Model
(RegHCM) and investigate the impact of climate change on the hydro-climate of the
country based on the Intergovernmental Panel on Climate Change Fifth Assessment
Report (IPCC AR5) projections under a complete spectrum of future greenhouse gas
emission scenarios based on Representative Concentration Pathways (RCPs),
focusing on six main hydro-climate parameters at 6-km grid resolution over the
geographical region of Peninsular Malaysia, Sabah and Sarawak.

The projected hydrologic and climatic outputs are very essential for the
quantification of the potential climate change vulnerabilities and impacts on water
related sectors. It is our hope that the findings of this study will lead to the progression
of engineering as well as nature-based methodologies and development for climate
change adaptation particularly.

Table of Contents

1. INTRODUCTION ................................................................................................................................... 1

1.1. Background........................................................................................................................................ 1
1.2. Scope ................................................................................................................................................. 2
1.3. Objectives .......................................................................................................................................... 3

2. SELECTION OF CLIMATE CHANGE SCENARIOS....................................................................... 7

3. GLOBAL AND REGIONAL CLIMATE CHANGE BASED ON CLIMATE MODELS IN
CMIP5…………………………………………………………………………………………………… 9

3.1. Representative Concentration Pathways (RCPs) .............................................................................. 14
3.2. Global CMIP5 GCM Simulations to be Dynamically Downscaled in This Study ........................... 16

4. THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL......................................... 24

4.1. WRF Model over Peninsular Malaysia (WRF-PM) and Sabah-Sarawak (WRF-SS) ....................... 25
4.2. Geographic Information System Database for the WRF Model over Peninsular Malaysia.............. 26
4.3. Initial and Boundary Conditions for WRF Model over Peninsular Malaysia and Sabah-Sarawak .. 32

5. HISTORICAL CONTROL SIMULATIONS: MODEL CONFIGURATION, BIAS
CORRECTION AND MODEL VALIDATION ................................................................................. 69

6. ASSESSMENT OF THE IMPACT OF CLIMATE CHANGE ON THE HYDROCLIMATE OF
PENINSULAR MALAYSIA IN THE 21ST CENTURY ................................................................. 120

6.1. Rainfall ........................................................................................................................................... 120
6.2. Air temperature............................................................................................................................... 147
6.3. Evapotranspiration.......................................................................................................................... 178
6.4. Relative humidity ........................................................................................................................... 204
6.5. Downward shortwave radiation...................................................................................................... 230
6.6. Wind speed ..................................................................................................................................... 256

7. SUMMARY AND CONCLUSIONS.................................................................................................... 282

8. REFERENCES...................................................................................................................................... 290

APPENDIX A.................................................................................................................................................. 291

APPENDIX B .................................................................................................................................................. 315

List of Figures

Figure 1.1 - The defined watersheds and coastal regions of Peninsular Malaysia ................................ 4
Figure 1.2 - The defined hydro-climatic sub-regions of Sabah and Sarawak ....................................... 5
Figure 3.1- CMIP5 multi-model simulated time series from 1950 to 2100 for (a) change in global

annual mean surface temperature relative to 1986–2005, (b) Northern Hemisphere September sea
ice extent (5-year running mean), and (c) global mean ocean surface pH. Time series of
projections and a measure of uncertainty (shading) are shown for scenarios RCP2.6 (blue) and
RCP8.5 (red). Black (grey shading) is the modelled historical evolution using historical
reconstructed forcings. The mean and associated uncertainties averaged over 2081−2100 are
given for all RCP scenarios as colored vertical bars. The numbers of CMIP5 models used to
calculate the multi-model mean is indicated. For sea ice extent (b), the projected mean and
uncertainty (minimum-maximum range) of the subset of models that most closely reproduce the
climatological mean state and 1979 to 2012 trend of the Arctic sea ice is given (number of models
given in brackets). For completeness, the CMIP5 multi-model mean is also indicated with dotted
lines. The dashed line represents nearly ice-free conditions (i.e., when sea ice extent is less than
106 km2 for at least five consecutive years). This figure was reproduced from IPCC (2013). .. 11
Figure 3.2 - Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 during
2081–2100 of (a) annual mean surface temperature change, (b) average percent change in annual
mean precipitation, (c) Northern Hemisphere September sea ice extent, and (d) change in ocean
surface pH. Changes in panels (a), (b) and (d) are shown relative to 1986–2005. The number of
CMIP5 models used to calculate the multi-model mean is indicated in the upper right corner of
each panel. For panels (a) and (b), hatching indicates regions where the multi-model mean is small
compared to natural internal variability (i.e., less than one standard deviation of natural internal
variability in 20-year means). Stippling indicates regions where the multi-model mean is large
compared to natural internal variability (i.e., greater than two standard deviations of natural
internal variability in 20-year means) and where at least 90% of models agree on the sign of
change. In panel (c), the lines are the modelled means for 1986−2005; the filled areas are for the
end of the century. The CMIP5 multi-model mean is given in white color, the projected mean sea
ice extent of a subset of models (number of models given in brackets) that most closely reproduces
the climatological mean state and 1979 to 2012 trend of the Arctic sea ice extent is given in light
blue color. This figure was reproduced from IPCC (2013)......................................................... 12
Figure 3.3 - Projections of global mean sea level rise over the 21st century relative to 1986–2005 from
the combination of the CMIP5 ensemble with process-based models, for RCP2.6 and RCP8.5.
The assessed likely range is shown as a shaded band. The assessed likely ranges for the mean
over the period 2081–2100 for all RCP scenarios are given as colored vertical bars, with the
corresponding median value given as a horizontal line. This figure was reproduced from IPCC
(2013). ......................................................................................................................................... 13
Figure 3.4 - Global mean surface temperature increase as a function of cumulative total global CO2
emissions from various lines of evidence. Multi-model results from a hierarchy of climate-carbon
cycle models for each RCP until 2100 are shown with colored lines and decadal means (dots).
Some decadal means are labeled for clarity (e.g., 2050 indicating the decade 2040−2049). Model
results over the historical period (1860 to 2010) are indicated in black. The colored plume
illustrates the multi-model spread over the four RCP scenarios and fades with the decreasing
number of available models in RCP8.5. The multi-model mean and range simulated by CMIP5
models, forced by a CO2 increase of 1% per year (1% yr–1 CO2 simulations), is given by the thin

i

black line and grey area. For a specific amount of cumulative CO2 emissions, the 1% per year
CO2 simulations exhibit lower warming than those driven by RCPs, which include additional
non-CO2 forcings. Temperature values are given relative to the 1861−1880 base period,
emissions relative to 1870. Decadal averages are connected by straight lines. This figure was
reproduced from IPCC (2013)..................................................................................................... 14
Figure 3.5 - Sample global climate data for MIROC5 historical control simulation at 1990-01-01 00:00
UTC: Near surface specific humidity in () .................................................................................. 17
Figure 3.6 - Sample global climate data for MIROC5 historical control simulation at 1990-01-01 00:00
UTC: Surface air pressure in (Pa) ............................................................................................... 17
Figure 3.7 - Sample global climate data for MIROC5 historical control simulation at 1990-01-01 00:00
UTC: Sea level pressure in (Pa) .................................................................................................. 18
Figure 3.8 - Sample global climate data for MIROC5 historical control simulation at 1990-01-01 00:00
UTC: Air temperature in (K)....................................................................................................... 18
Figure 3.9 - Sample global climate data for MIROC5 historical control simulation at 1990-01-01 00:00
UTC: Eastward near surface wind in (ms-1)............................................................................... 19
Figure 3.10 - Sample global climate data for MIROC5 historical control simulation at 1990-01-01
00:00 UTC: Northward near surface wind in (ms-1) .................................................................. 19
Figure 3.11 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00 UTC:
Near surface specific humidity in () ............................................................................................ 21
Figure 3.12 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00 UTC:
Surface air pressure in (Pa) ......................................................................................................... 21
Figure 3.13 - Sample global climate data for MIROC5 RCP4.5 scenario at 12026-01-01 00:00 UTC:
Sea level pressure in (Pa) ............................................................................................................ 22
Figure 3.14 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00 UTC:
Air temperature in (K)................................................................................................................. 22
Figure 3.15 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00 UTC:
Eastward near surface wind in (ms-1) ......................................................................................... 23
Figure 3.16 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00 UTC:
Northward near surface wind in (ms-1)....................................................................................... 23
Figure 4.1 - The modeling domains of (a) Peninsular Malaysia and (b) Sabah and Sarawak hydro-
climate models. D01 denotes the large outer domain at grid size 54km, D02 as the intermediate
domain at grid size 18km, and D03 as the inner modeling domain where the modeling studies are
being carried out, is at 6km grid resolution................................................................................. 26
Figure 4.2 - The topographic elevation (m) in (a) Peninsular Malaysia and (b) Sabah and Sarawak . 27
Figure 4.3 - Slope of orography around (a) Peninsular Malaysia and (b) Sabah and Sarawak........... 28
Figure 4.4 – 16 categories of (a) top and (b) bottom layer soil types.................................................. 29
Figure 4.5 - Sample monthly surface albedo for January (%) for (a) Peninsular Malaysia and (b) Sabah
and Sarawak ................................................................................................................................ 30
Figure 4.6 - Sample monthly green fraction for January (unitless) for (a) Peninsular Malaysia and (b)
Sabah and Sarawak...................................................................................................................... 31
Figure 4.7 - Sample initial and boundary conditions for historical simulations over Peninsular Malaysia
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Geopotential
Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d).................................. 33
Figure 4.8 - Sample initial and boundary conditions for historical simulations over Peninsular Malaysia
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Temperature
in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d).............................................. 34

ii

Figure 4.9 - Sample initial and boundary conditions for historical simulations over Peninsular Malaysia
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 35

Figure 4.10 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC):
Eastward velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............. 36

Figure 4.11 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC):
Northward velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........... 37

Figure 4.12 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Sea-
level Pressure in Pa (a), Surface Pressure in Pa (b)..................................................................... 37

Figure 4.13 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) ............................................................................ 38

Figure 4.14 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Geopotential
Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d).................................. 39

Figure 4.15 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Temperature
in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d).............................................. 40

Figure 4.16 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 41

Figure 4.17 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Eastward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 42

Figure 4.18 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM (1990-07-01 00:00 UTC): Northward velocity in m/s at 300 hPA (a),
500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................................................................... 43

Figure 4.19 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Sea-level
Pressure in Pa (a), Surface Pressure in Pa (b) ............................................................................. 43

Figure 4.20 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) ............................................................................ 44

Figure 4.21 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Geopotential
Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d).................................. 45

Figure 4.22 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Temperature
in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d).............................................. 46

Figure 4.23 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 47

iii

Figure 4.24 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Eastward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 48

Figure 4.25 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Northward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 49

Figure 4.26 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Sea-level
Pressure in Pa (a), Surface Pressure in Pa (b) ............................................................................. 49

Figure 4.27 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) ............................................................................ 50

Figure 4.28 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC):
Geopotential Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............ 51

Figure 4.29 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC):
Temperature in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)......................... 52

Figure 4.30 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC):
Relative Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............... 53

Figure 4.31 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC):
Eastward velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............. 54

Figure 4.32 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC):
Northward velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........... 55

Figure 4.33 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC): Sea-
level Pressure in Pa (a), Surface Pressure in Pa (b)..................................................................... 55

Figure 4.34 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for IPSL-CM5A-LR GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) ............................................................................ 56

Figure 4.35 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC):
Geopotential Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............ 57

Figure 4.36 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC):
Temperature in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)......................... 58

Figure 4.37 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 59

Figure 4.38 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC): Eastward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................. 60

iv

Figure 4.39 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC):
Northward velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........... 61

Figure 4.40 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC): Sea-level
Pressure in Pa (a), Surface Pressure in Pa (b) ............................................................................. 61

Figure 4.41 - Sample initial and boundary conditions for historical simulations over Peninsular
Malaysia for MRI-CGCM GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) ............................................................................ 62

Figure 4.42 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Geopotential Height in m at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)................................................................. 63

Figure 4.43 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Temperature in K at 300 hPA (a),
500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................................................................... 64

Figure 4.44 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Relative Humidity in % at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)................................................................. 65

Figure 4.45 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Eastward velocity in m/s at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)................................................................. 66

Figure 4.46 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Northward velocity in m/s at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)................................................................. 67

Figure 4.47 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Sea-level Pressure in Pa (a),
Surface Pressure in Pa (b) ........................................................................................................... 67

Figure 4.48 - Sample initial and boundary conditions for future simulations over Peninsular Malaysia
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Soil Temperature in K of 0-10 cm
ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground layer (c), and of 100-200
cm ground layer (d) ..................................................................................................................... 68

Figure 5.1 - Locations of the (a) 334 rainfall stations in Peninsular Malaysia and (b) 354 rainfall
stations in Sabah and Sarawak .................................................................................................... 70

Figure 5.2 - Locations of the (a) 23 meteorological stations for temperature in Peninsular Malaysia and
(b) 10 meteorological stations for temperature in Sabah and Sarawak ....................................... 71

Figure 5.3 - Number of gauges in Sabah and Sarawak with good rainfall observation records for various
durations ...................................................................................................................................... 73

Figure 5.4 - Simulated 10-year average annual rainfall (mm) over (a) Peninsular Malaysia and (b)
Sabah-Sarawak during 1970 – 1980, by downscaling the control run data from five global climate
models, against observations. ...................................................................................................... 78

Figure 5.5 - Simulated 10-year average annual rainfall (mm) over (a) Peninsular Malaysia and (b)
Sabah-Sarawak during 1980 – 1990, by downscaling the control run data from five global climate
models, against observations. ...................................................................................................... 80

Figure 5.6 - Simulated 10-year average annual rainfall (mm) over (a) Peninsular Malaysia and (b)
Sabah-Sarawak during 1990 – 2000, by downscaling the control run data from five global climate
models, against observations. ...................................................................................................... 82

v

Figure 5.7 - Simulated 10-year average air temperature (oC) over (a) Peninsular Malaysia and (b)
Sabah-Sarawak during 1970 – 1980 by downscaling the control run data from five global climate
models. ........................................................................................................................................ 84

Figure 5.8 - Simulated 10-year average air temperature (oC) over (a) Peninsular Malaysia and (b)
Sabah-Sarawak during 1980 – 1990 by downscaling the control run data from five global climate
models. ........................................................................................................................................ 86

Figure 5.9 - Simulated 10-year average air temperature (oC) over (a) Peninsular Malaysia and (b)
Sabah-Sarawak during 1990 – 2000 by downscaling the control run data from five global climate
models. ........................................................................................................................................ 88

Figure 5.10 – Monthly mean of observed and simulated rainfall during 1970-2000 period at the
selected gauge locations in Peninsular Malaysia and Sabah-Sarawak. Simulations
correspond to the dynamically-downscaled five GCM control run simulations ................. 114

Figure 5.11 - Monthly mean of observed and simulated surface air temperature during 1970-2000
period at selected gauge locations in Peninsular Malaysia and Sabah-Sarawak. Simulations
correspond to the dynamically-downscaled five GCM control run simulations. ................ 119

Figure 6.1 - Time series plots of the annual accumulated rainfall during the historical (1970-2000) and
the projected future (2010-2100) conditions for 13 watersheds in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 125

Figure 6.2 - Time series plots of the annual accumulated rainfall during the historical (1970-2000) and
the projected future (2010-2100) conditions for 12 coastal regions in Peninsular Malaysia. Future
conditions are in terms of the individual projections and the ensemble average of the projections.
................................................................................................................................................... 129

Figure 6.3 - a) Long-term mean of the annual rainfall (mm) during the historical period (1970-2000),
the early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st century
(2071-2100), and b) the change in the long-term mean of the annual rainfall from the historical
period, to the early, middle, and late 21st century for 13 watersheds and 12 coastal regions in
Peninsular Malaysia. ................................................................................................................. 130

Figure 6.4 - Time series plots of the annual accumulated rainfall during the historical (1970-2000) and
the projected future (2010-2100) conditions for subregions in Sabah. Future conditions are in
terms of individual projections and the ensemble average of the projections........................... 138

Figure 6.5 - Time series plots of the annual accumulated rainfall during the historical (1970-2000) and
the projected future (2010-2100) conditions for subregions in Sarawak. Future conditions are in
terms of the individual projections and the ensemble average of the projections. .................... 143

Figure 6.6 - a) Long-term mean of the annual rainfall (mm) during the historical period (1970-2000),
the early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st century
(2071-2100), and b) the change in the long-term mean of the annual rainfall from the historical
period, to the early, middle, and late 21st century for each of the 39 subregions in Sabah and
Sarawak. .................................................................................................................................... 144

Figure 6.7 - Time series plots of the annual average air temperature during the historical (1970-2000)
and the projected future (2010-2100) conditions for 13 watersheds in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 153

Figure 6.8 - Time series plots of annual average air temperature during the historical (1970-2000) and
the projected future (2010-2100) conditions for 12 coastal regions in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 157

vi

Figure 6.9- a) Long-term mean of the daily minimum, average, and maximum air temperature (oC)
during the historical period (1970-2000), the early 21st century (2011-2040), the middle 21st
century (2041-2070), and the late 21st century (2071-2100), and b) the change in the 30-year
mean of the daily minimum, average, and maximum air temperature from the historical period,
to the early, middle, and late 21st century for 13 watersheds and 12 coastal regions of Peninsular
Malaysia. ................................................................................................................................... 159

Figure 6.10 - Time series plots of the annual average air temperature during the historical (1970-2000)
and the projected future (2010-2100) conditions for subregions in Sabah. Future conditions are in
terms of individual projections and the ensemble average of the projections........................... 169

Figure 6.11- Time series plots of annual average air temperature during the historical (1970-2000) and
the projected future (2010-2100) conditions for subregions in Sarawak . Future conditions are in
terms of individual projections and the ensemble average of the projections.......................... 173

Figure 6.12 - a) Long-term mean of the daily minimum, average, and maximum air temperature (oC)
during the historical period (1970-2000), the early 21st century (2011-2040), the middle 21st
century (2041-2070), and the late 21st century (2071-2100), and b) the change in the 30-year
mean of the daily minimum, average, and maximum air temperature from the historical period,
to the early, middle, and late 21st century for each of the 39 subregions in Sabah and Sarawak.
................................................................................................................................................... 175

Figure 6.13 - Time series plots of annual evapotranspiration during the historical (1970-2000) and the
projected future (2010-2100) conditions for 13 watersheds in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 183

Figure 6.14 - Time series plots of annual evapotranspiration during the historical (1970-2000) and the
projected future (2010-2100) conditions for 12 coastal regions in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 187

Figure 6.15 - a) Long-term mean of the annual evapotranspiration (mm) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual
evapotranspiration from the historical period, to the early, middle, and late 21st century for 13
watersheds and 12 coastal regions of Peninsular Malaysia....................................................... 187

Figure 6.16 - Time series plots of annual evapotranspiration during the historical (1970-2000) and the
projected future (2010-2100) conditions for subregions in Sabah. Future conditions are in terms
of individual projections and the ensemble average of the projections..................................... 196

Figure 6.17 - Time series plots of annual evapotranspiration during the historical (1970-2000) and the
projected future (2010-2100) conditions for subregions in Sarawak. Future conditions are in terms
of individual projections and the ensemble average of the projections.................................... 200

Figure 6.18 - a) Long-term mean of the annual evapotranspiration (mm) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual
evapotranspiration from the historical period, to the early, middle, and late 21st century for each
of the 39 subregions in Sabah and Sarawak. ............................................................................. 201

Figure 6.19- Time series plots of the annual average relative humidity during the historical (1970-
2000) and the projected future (2010-2100) conditions for 13 watersheds in Peninsular Malaysia.
Future conditions are in terms of individual projections and the ensemble average of the
projections. ................................................................................................................................ 209

vii

Figure 6.20- Time series plots of the annual average relative humidity during the historical (1970-
2000) and the projected future (2010-2100) conditions for coastal regions of Peninsular Malaysia.
Future conditions are in terms of individual projections and the ensemble average of the
projections. ................................................................................................................................ 213

Figure 6.21 - a) Long-term mean of the annual average relative humidity (%) during the historical
period (1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070),
and the late 21st century (2071-2100), and b) the change in the long-term mean of the annual
average relative humidity from the historical period, to the early, middle, and late 21st century
for 13 watersheds and 12 coastal regions of Peninsular Malaysia. ........................................... 213

Figure 6.22 - Time series plots of the annual average relative humidity during the historical (1970-
2000) and the projected future (2010-2100) conditions for subregions in Sabah. Future conditions
are in terms of individual projections and the ensemble average of the projections. ................ 221

Figure 6.23 - Timeseries plots of the annual average relative humidity during the historical (1970-
2000) and the projected future (2010-2100) conditions for subregions of Sarawak . Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 226

Figure 6.24 - a) Long-term mean of the annual average relative humidity (%) during the historical
period (1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070),
and the late 21st century (2071-2100), and b) the change in the long-term mean of the annual
average relative humidity from the historical period, to the early, middle, and late 21st century
for each of the 39 subregions in Sabah and Sarawak. ............................................................... 227

Figure 6.25 - Time series plots of the annual average downward shortwave radiation during the
historical (1970-2000) and the projected future (2010-2100) conditions for 13 watersheds in
Peninsular Malaysia. Future conditions are in terms of individual projections and the ensemble
average of the projections.......................................................................................................... 235

Figure 6.26 - Time series plots of the annual average downward shortwave radiation during the
historical (1970-2000) and the projected future (2010-2100) conditions for 12 coastal regions in
Peninsular Malaysia. Future conditions are in terms of individual projections and the ensemble
average of the projections.......................................................................................................... 239

Figure 6.27 - a) Long-term mean of the annual average downward shortwave radiation (W/m2) during
the historical period (1970-2000), the early 21st century (2011-2040), the middle 21st century
(2041-2070), and the late 21st century (2071-2100), and b) the change in the long-term mean of
the annual average downward shortwave radiation from the historical period to the early, middle,
and late 21st century for 13 watersheds and 12 coastal regions of Peninsular Malaysia. ......... 239

Figure 6.28 - Time series plots of the annual average downward shortwave radiation during the
historical (1970-2000) and the projected future (2010-2100) conditions for subregions in Sabah.
Future conditions are in terms of individual projections and the ensemble average of the
projections. ................................................................................................................................ 248

Figure 6.29 - Time series plots of the annual average downward shortwave radiation during the
historical (1970-2000) and the projected future (2010-2100) conditions for subregions of
Sarawak . Future conditions are in terms of individual projections and the ensemble average of
the projections. .......................................................................................................................... 252

Figure 6.30 - a) Long-term mean of the annual average downward shortwave radiation (W/m2) during
the historical period (1970-2000), the early 21st century (2011-2040), the middle 21st century
(2041-2070), and the late 21st century (2071-2100), and b) the change in the long-term mean of
the annual average downward shortwave radiation from the historical period to the early, middle,
and late 21st century for each of the 39 subregions in Sabah and Sarawak. ............................. 253

viii

Figure 6.31 - Time series plots of the annual average wind speed during the historical (1970-2000) and
the projected future (2010-2100) conditions for 13 watersheds in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 261

Figure 6.32 - Time series plots of annual average wind speed during the historical (1970-2000) and the
projected future (2010-2100) conditions for 12 coastal regions in Peninsular Malaysia. Future
conditions are in terms of individual projections and the ensemble average of the projections.
................................................................................................................................................... 265

Figure 6.33 - a) Long-term mean of the annual average wind speed (m/s) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
wind speed from the historical period to the early, middle, and late 21st century for 13 watersheds
and 12 coastal regions of Peninsular Malaysia.......................................................................... 265

Figure 8.1 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Geopotential
Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)................................ 291

Figure 8.2 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Temperature
in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................ 292

Figure 8.3 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 293

Figure 8.4 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Eastward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 294

Figure 8.5 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Northward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 295

Figure 8.6 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Sea-level
Pressure in Pa (a), Surface Pressure in Pa (b) ........................................................................... 295

Figure 8.7 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for GFDL-ESM2M GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) .......................................................................... 296

Figure 8.8 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Geopotential Height
in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................ 297

Figure 8.9 - Sample initial and boundary conditions for historical simulations over Sabah and Sarawak
for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Temperature in K at
300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)........................................................ 298

Figure 8.10 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 299

Figure 8.11 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Eastward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 300

ix

Figure 8.12 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for CCSM4 GCM (1990-07-01 00:00 UTC): Northward velocity in m/s at 300 hPA (a),
500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................................................................. 301

Figure 8.13 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Sea-level
Pressure in Pa (a), Surface Pressure in Pa (b) ........................................................................... 301

Figure 8.14 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for CCSM4 GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) .......................................................................... 302

Figure 8.15 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Geopotential
Height in m at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)................................ 303

Figure 8.16 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Temperature
in K at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................ 304

Figure 8.17 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Relative
Humidity in % at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 305

Figure 8.18 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Eastward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 306

Figure 8.19 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Northward
velocity in m/s at 300 hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d) ........................... 307

Figure 8.20 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Sea-level
Pressure in Pa (a), Surface Pressure in Pa (b) ........................................................................... 307

Figure 8.21 - Sample initial and boundary conditions for historical simulations over Sabah and
Sarawak for MIROC GCM historical control simulation (1990-07-01 00:00 UTC): Soil
Temperature in K of 0-10 cm ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground
layer (c), and of 100-200 cm ground layer (d) .......................................................................... 308

Figure 8.22 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Geopotential Height in m at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................................... 309

Figure 8.23 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Temperature in K at 300 hPA (a),
500 hPA (b), 700 hPA (c), and 900 hPA (d) ............................................................................. 310

Figure 8.24 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Relative Humidity in % at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................................... 311

Figure 8.25 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Eastward velocity in m/s at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................................... 312

Figure 8.26 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Northward velocity in m/s at 300
hPA (a), 500 hPA (b), 700 hPA (c), and 900 hPA (d)............................................................... 313

x

Figure 8.27 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Sea-level Pressure in Pa (a),
Surface Pressure in Pa (b) ......................................................................................................... 313

Figure 8.28 - Sample initial and boundary conditions for future simulations over Sabah and Sarawak
for MIROC GCM RCP8.5 scenario (2050-07-01 00:00 UTC): Soil Temperature in K of 0-10 cm
ground layer (a), of 10-40 cm ground layer (b), of 40-100 cm ground layer (c), and of 100-200
cm ground layer (d) ................................................................................................................... 314

xi

List of Tables

Table 1.1 - Defined watersheds of Peninsular Malaysia ....................................................................... 5
Table 1.2 - Computed areas of the hydro-climatic subregions of Sabah............................................... 6
Table 1.3 - Computed areas of the hydro-climatic subregions of Sarawak........................................... 6
Table 2.1 - The list of historical control simulations for the specified five GCMs............................... 7
Table 2.2 - The list of future projections, based on four Representative Concentration Pathways

greenhouse gas emission scenarios, for the specified 5 GCMs..................................................... 8
Table 4.1 – 16 categories of soil types ................................................................................................ 29
Table 6.1 - Long-term mean of the annual rainfall (mm) during the historical period (1970-2000), the

early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st century
(2071-2100), and b) the change in the long-term mean of the annual rainfall from the historical
period, to the early, middle, and late 21st century for 13 watersheds in Peninsular Malaysia.. 131
Table 6.2 - Long-term mean of the annual rainfall (mm) during the historical period (1970-2000), the
early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st century
(2071-2100), and b) the change in the long-term mean of the annual rainfall from the historical
period, to the early, middle, and late 21st century for 12 coastal regions in Peninsular Malaysia.
................................................................................................................................................... 132
Table 6.3 - Long-term mean of the annual rainfall (mm) during the historical period (1970-2000), the
early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st century
(2071-2100), and b) the change in the long-term mean of the annual rainfall from the historical
period, to the early, middle, and late 21st century for subregions in Sabah.............................. 145
Table 6.4- Long-term mean of the annual rainfall (mm) during the historical period (1970-2000), the
early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st century
(2071-2100), and b) the change in the long-term mean of the annual rainfall from the historical
period, to the early, middle, and late 21st century for subregions in Sarawak.......................... 146
Table 6.5- Long-term mean of the daily average air temperature (oC) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the daily average air
temperature from the historical period, to the early, middle, and late 21st century for 13
watersheds of Peninsular Malaysia. .......................................................................................... 160
Table 6.6- Long-term mean of the daily average air temperature (oC) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the daily average air
temperature from the historical period, to the early, middle, and late 21st century for 12 coastal
regions in Peninsular Malaysia.................................................................................................. 161
Table 6.7- Long-term mean of the daily average air temperature (oC) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the daily average air
temperature from the historical period, to the early, middle, and late 21st century for subregions
in Sabah. .................................................................................................................................... 176
Table 6.8- Long-term mean of the daily average air temperature (oC) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the daily average air
temperature from the historical period, to the early, middle, and late 21st century for subregions
in Sarawak. ................................................................................................................................ 177

xii

Table 6.9 - Long-term mean of the annual evapotranspiration (mm) during the historical period (1970-
2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st
century (2071-2100), and b) the change in the long-term mean of the annual evapotranspiration
from the historical period, to the early, middle, and late 21st century for 13 watersheds in
Peninsular Malaysia. ................................................................................................................. 188

Table 6.10 - Long-term mean of the annual evapotranspiration (mm) during the historical period (1970-
2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st
century (2071-2100), and b) the change in the long-term mean of the annual evapotranspiration
from the historical period, to the early, middle, and late 21st century for 12 coastal regions in
Peninsular Malaysia. ................................................................................................................. 189

Table 6.11 - Long-term mean of the annual evapotranspiration (mm) during the historical period (1970-
2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the late 21st
century (2071-2100), and b) the change in the long-term mean of the annual evapotranspiration
from the historical period, to the early, middle, and late 21st century for subregions in Sabah.
................................................................................................................................................... 202

Table 6.12 - Long-term mean of the annual evapotranspiration (mm) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual
evapotranspiration from the historical period, to the early, middle, and late 21st century for
subregions in Sarawak............................................................................................................... 203

Table 6.13 - Long-term mean of the annual average relative humidity (%) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
relative humidity from the historical period, to the early, middle, and late 21st century for 13
watersheds in Peninsular Malaysia............................................................................................ 214

Table 6.14 - Long-term mean of the annual average relative humidity (%) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
relative humidity from the historical period, to the early, middle, and late 21st century for 12
coastal regions in Peninsular Malaysia...................................................................................... 215

Table 6.15 - Long-term mean of the annual average relative humidity (%) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
relative humidity from the historical period, to the early, middle, and late 21st century for
subregions in Sabah................................................................................................................... 228

Table 6.16 - Long-term mean of the annual average relative humidity (%) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
relative humidity from the historical period, to the early, middle, and late 21st century for
subregions in Sarawak............................................................................................................... 229

Table 6.17 - Long-term mean of the annual average downward shortwave radiation (W/m2) during the
historical period (1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-
2070), and the late 21st century (2071-2100), and b) the change in the long-term mean of the
annual average downward shortwave radiation from the historical period to the early, middle, and
late 21st century for 13 watersheds in Peninsular Malaysia...................................................... 240

Table 6.18 - Long-term mean of the annual average downward shortwave radiation (W/m2) during the
historical period (1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-

xiii

2070), and the late 21st century (2071-2100), and b) the change in the long-term mean of the
annual average downward shortwave radiation from the historical period to the early, middle, and
late 21st century for 12 coastal regions in Peninsular Malaysia................................................ 241
Table 6.19 - Long-term mean of the annual average downward shortwave radiation (W/m2) during the
historical period (1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-
2070), and the late 21st century (2071-2100), and b) the change in the long-term mean of the
annual average downward shortwave radiation from the historical period to the early, middle, and
late 21st century for subregions in Sabah.................................................................................. 254
Table 6.20 - Long-term mean of the annual average downward shortwave radiation (W/m2) during the
historical period (1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-
2070), and the late 21st century (2071-2100), and b) the change in the long-term mean of the
annual average downward shortwave radiation from the historical period to the early, middle, and
late 21st century for subregions in Sarawak.............................................................................. 255
Table 6.21 - Long-term mean of the annual average wind speed (m/s) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
wind speed from the historical period to the early, middle, and late 21st century for 13 watersheds
in Peninsular Malaysia. ............................................................................................................. 266
Table 6.22 - Long-term mean of the annual average wind speed (m/s) during the historical period
(1970-2000), the early 21st century (2011-2040), the middle 21st century (2041-2070), and the
late 21st century (2071-2100), and b) the change in the long-term mean of the annual average
wind speed from the historical period to the early, middle, and late 21st century for 12 coastal
regions in Peninsular Malaysia.................................................................................................. 267
Table 8.1 - Summary information for rainfall observation stations in Peninsular Malaysia............. 315
Table 8.2 - Summary information for rainfall observation stations in Sabah and Sarawak .............. 322
Table 8.3 - List of meteorological stations in Peninsular Malaysia .................................................. 331
Table 8.4 - List of meteorological stations in Sabah and Sarawak.................................................... 331

xiv

Glossary of Acronyms and Abbreviations

AR4 Intergovernmental Panel on Climate Change’s Fourth Assessment
AR5 Report
AOGCM
CCSM4 Intergovernmental Panel on Climate Change’s Fifth Assessment Report

CMIP5 Atmosphere-ocean general circulation model
ESM
GCM Fourth version of the Community Climate System Model (CCSM), a
GFDL-ESM2M coupled global climate model (GCM) developed by the University
Corporation for Atmospheric Research (UCAR), USA with an
GIS atmospheric horizontal grid of 3.75° × 3.75°
HPC
IPCC Coupled Model Intercomparison Project Phase 5
IPSL-CM5A-LR
Earth System Model
MIROC5
General Circulation Model or Global Climate Model
MRI-CGCM3
Global coupled carbon–climate ESM which incorporated
NAHRIM biogeochemical components, developed at the National Oceanic and
PM Atmospheric Administration (NOAA) Geophysical Fluid Dynamics
RCPs Laboratory (GFDL), USA with spatial resolution of 2.5° x 2°

Geographic information system

High-performance computer

Intergovernmental Panel on Climate Change

Fifth version of IPSL model, an ESM developed by The Institut Pierre
Simon Laplace (IPSL), France, with a low atmospheric resolution of 1.9°
x 3.75°

Fifth version of Model for Interdisciplinary Research on Climate
(MIROC), an atmosphere-ocean general circulation model (AOGCM),
with a 1.4º x 1.4º spatial resolution for the atmospheric parcel, developed
jointly at the Center for Climate System Research (CCSR), University of
Tokyo, National Institute for Environmental Studies (NIES), and Japan
Agency for Marine-Earth Science and Technology

Meteorological Research Institute CGCM Version 3, developed by the
Meteorological Research Institute (MRI), Japan with atmospheric spatial
resolution of 1.125° x 2.25°

National Water Research Institute of Malaysia

Peninsular Malaysia

Representative Concentration Pathways are four greenhouse
gas concentration trajectories adopted by the IPCC for its greenhouse gas
emission scenarios for the Fifth Assessment Report (AR5) in 2014. It

xv

SRES supersedes Special Report on Emissions Scenarios (SRES) projections
SS that were published in 2000
WRF
Special Report on Emissions Scenarios

Sabah and Sarawak

Weather Research Forecasting model; this numerical computer model
simulates atmospheric processes comprehensively over a geographical
region.

xvi

1. INTRODUCTION

1.1. Background
Weather and climate are among the foremost factors that determine how a society develops in
a geographical region. Daily fluctuations of temperature are called weather, while climate is
typically the average state of the atmosphere observed over a finite time period for a number
of years. The social, economic and physical infrastructure of a geographical region such as
Malaysia has evolved from the adaptation of that region’s society to the prevailing climate
and to the hydrological conditions brought about by that climate over decades or, as in the
case of Malaysia, over centuries. The frequency of occurrence and magnitudes of such
phenomena as typhoons, floods and droughts have a profound influence on the habitability of
a region and on the social and economic activities of that region’s population which tries to
moderate the stresses brought about by those climatic/hydrologic extreme events.

Recently, the Intergovernmental Panel on Climate Change (IPCC) adopted Representative
Concentration Pathways (RCPs) for its greenhouse gas emission scenarios for its Fifth
Assessment Report (AR5) in 2014 which superseded the Special Report on Emissions
Scenarios (SRES) projections published in 2000. Furthermore, AR5 utilized the simulation
results of most recent versions of the climate models, which improved their representation of
earth climate compared to those utilized in the Fourth Assessment Report (AR4). Flato et al.
(2013) evaluated the global climate models that were used in the preparation of AR5 in
comparison to those models used in AR4 and concluded that:

i. The ability of climate models to simulate surface temperature has improved in many
important aspects relative to the generation of models assessed in the AR4;

ii. There is very high confidence that models reproduce the general features of the global-
scale annual mean surface temperature increase over the historical period, including
the more rapid warming in the second half of the 20th century, and the cooling
immediately following large volcanic eruptions;

iii. The simulation of large-scale patterns of precipitation has improved somewhat since
the AR4, although models continue to perform less well for precipitation than for
surface temperature;

1

iv. Models are able to capture the general characteristics of storm tracks and extratropical
cyclones, and there is some evidence of improvement since the AR4;

v. Many models are able to reproduce the observed changes in upper ocean heat content
from 1961 to 2005 with the multi-model mean time series falling within the range of
the available observational estimates for most of the period;

vi. The simulation of the tropical Pacific Ocean mean state has improved since the AR4,
with a 30% reduction in the spurious westward extension of the cold tongue near the
equator, a pervasive bias of coupled models;

vii. Many important modes of climate variability and intra-seasonal to seasonal
phenomena are reproduced by models, with some improvements evident since the
AR4;

viii. Climate and Earth System models are based on physical principles, and they reproduce
many important aspects of the observed climate.

Climate is fundamentally an evolving, transient process. As such, the best way to make
statistical inferences about climate change is to obtain as many as possible simulated future
climate projection realizations from various GCMs for a range of future scenarios so that one
can form a sufficiently large ensemble of member climate projection realizations, covering
the complete range of greenhouse gas emission scenarios based on Representative
Concentration Pathways (RCPs), at any given specified time for a specified hydrologic (e.g.
watershed runoff) or climatic (e.g. rainfall) variable. Then at that instant of time, one can make
inferences based upon the ensemble average value of the variable of interest, with the sample
probability distribution providing a measure of the uncertainty. Ensemble averages, along with
the probability distributions, of the same variable, estimated at different time points would
then provide a measure of the change in the variable of interest. With this ensemble approach,
one can then filter out the natural variability of the climatic variable in order to discern the
real signal that should describe the impact of the climate change on the climatic variable.

1.2. Scope
Within the above framework, the scope of this project is to investigate the impact of climate
change on the climate of Peninsular Malaysia, Sabah and Sarawak based on the most recent
versions of the Global Climate Models (GCMs) utilized in the preparation of AR5, and cover
the complete spectrum of future greenhouse gas emission scenarios based on Representative

2

Concentration Pathways (RCPs), from the best possible future scenario RCP2.6 to the worst
case scenario RCP8.5, while also including the scenarios RCP4.5 and RCP6.0 in order to
account for the uncertainty in the RCPs. Meanwhile, this study utilizes 5 different global
coupled atmospheric-oceanic climate models (i.e. CCSM4, MIROC5, MRI-CGCM3, GFDL-
ESM2M, and IPSL-CM5A-LR) in order to account for the model uncertainty in climate
change simulations. Finally, in order to account for the effect of the uncertain initial conditions
on the future climate projection simulations of the earth as a nonlinear system (the so-called
“internal variability” effect), 16 different climate projection realizations from 5 GCMs under
4 different emission scenarios are utilized.

1.3. Objectives
Accordingly, the specific objectives of this study are:
a. to configure the WRF model of Peninsular Malaysia (WRF-PM) and WRF model of Sabah

and Sarawak (WRF-SS) at 6-km grid resolution to be utilized to dynamically downscale
1970-2000 historical climate control runs from 5 different GCMs i.e. CCSM4, MIROC5,
MRI-CGCM3, GFDL-ESM2M, and IPSL-CM5A-LR;
b. to dynamically downscale 16 coarse-resolution climate change projections for the duration
2006-2100 from the above-mentioned 5 GCMs corresponding to a full range of
greenhouse gas emission scenarios based on Representative Concentration Pathways
(RCPs, adopted by the Fifth Assessment Report in 2014) to represent different future
conditions from the best possible future scenario RCP2.6 to the worst case scenario
RCP8.5, while also including the scenarios RCP4.5 and RCP6.0 in order to account for
the uncertainty in the RCPs to Peninsular Malaysia as well as Sabah and Sarawak regions.
This study resulted in 16 x 94-year future realizations of the climate at fine resolution over
the regions, that are compared to 5 x 30-year downscaled historical realizations from the
control runs of the 5 GCMs;
c. to evaluate the potential impacts of climate change on the climate, in terms of rainfall, air
temperature, evapotranspiration, solar radiation, wind speed, and relative humidity at 6-
km grid resolution over the geographical regions of Peninsular Malaysia and Sabah—
Sarawak by means of WRF-PM and WRF-SS respectively. The evaluations are performed
over 13 watersheds (i.e. Batu Pahat, Johor, Muda, Kelang, Kelantan, Linggi, Muar,
Pahang, Perak, Selangor, Dungun, Kemaman, and Kuantan) and 12 coastal regions which

3

are the remaining parts of Peninsular Malaysia as shown in Figure 1.1, as well as a total
of 39 sub-regions of Sabah and Sarawak as shown in Figure 1.2. The defined areas are
tabulated in Table 1.1 – 1.3.

Coastal Region 1

Coastal Region 12

Muda

Coastal Region 11 Kelantan Coastal Region 2
Dungun
Perak Coastal Region 3
Kemaman
Pahang Kuantan

Coastal Region 10 Coastal Region 4

Selangor Johor

Coastal Region 9 Muar
Kelang

Coastal Region 8
Lingi

Coastal Region 7

Coastal Region 6

Batu Pahat
Coastal Region 5

Figure 1.1 - The defined watersheds and coastal regions of Peninsular Malaysia

4

Figure 1.2 - The defined hydro-climatic sub-regions of Sabah and Sarawak

Table 1.1 - Defined watersheds of Peninsular Malaysia

Watersheds State Catchment Area (km2)

Pahang Pahang 29,684
Kelantan Kelantan 12,672
Kelang Selangor 1,278
Johor Johor 2,247
Selangor Selangor 2,082
Dungun Terengganu 1,712
Perak Perak 14,827
Linggi Negeri Sembilan 1,328
Muar Johor 6,084
Batu Pahat Johor 2,228
Muda Kedah 4,130
Kemaman Terengganu 2,125
Kuantan Pahang 1,601

5

Table 1.2 - Computed areas of the hydro-climatic subregions of Sabah

Name State Catchment Area (km2)

Beaufort Sabah 1,841
Beluran Sabah 1,116
Kota Kinabalu Sabah 741
Kudat Sabah 2,625
Lahad Datu Sabah 5,235
Pitas Sabah 2,340
Sandakan Sabah 2,384
Sg Abai Sabah 862
Sg Kalabakan Sabah 1,337
Sg Kalumpang Sabah 1,112
Sg Labuk Sabah 5,668
Sg Padas Sabah 8,822
Sg Papar Sabah 788
Sg Segama Sabah 5,341
Sg Sinsilog Sabah 927
Sg Sugut Sabah 3,067
Sg Tuaran Sabah 1,147
Sipitang Sabah 1,290
Southern Sabah Sabah 7,212
Tawau Sabah 2,626
Trusan Kinabatangan Sabah 16,233

Table 1.3 - Computed areas of the hydro-climatic subregions of Sarawak

Name State Catchment Area (km2)

Batang Balingian Sarawak 3,126
Batang Baram Sarawak 22,109
Batang Kemena Sarawak 6,028
Batang Kerian Sarawak 1,504
Batang Lawas Sarawak 1,239
Batang Lupar Sarawak 6,511
Batang Mukah Sarawak 2,174
Batang Oya Sarawak 2,093
Batang Rajang Sarawak 51,133
Batang Sadong Sarawak 3,527
Batang Samarahan Sarawak 1,124
Batang Saribas Sarawak 2,118
Batang Tatau Sarawak 4,848
Batang Terusan Sarawak 2,473
Northern Sarawak Sarawak 5,866
Sg Limbang Sarawak 3,916
Sg Sarawak Sarawak 1,727
Southern Sarawak Sarawak 2,437

6

2. SELECTION OF CLIMATE CHANGE SCENARIOS

Realization of the study requires procurement of the most reliable future climate change
simulation global data that are produced by global coupled atmospheric-oceanic climate
models (also called “General Circulation Models”, and referred to as “GCMs”), at coarse grid
resolution (~200km-400km). Once the coarse resolution climate change data of the GCMs are
obtained for the region covering Peninsular Malaysia and Sabah-Sarawak, then these data are
dynamically downscaled to a much finer spatial grid resolution (6km) by the WRF (Weather
Research Forecasting) model over Peninsular Malaysia (WRF-PM) together with Sabah and
Sarawak (WRF-SS) regions in order to account for the impact of local topography and local
land surface and landuse conditions on the local climates of the region. WRF-PM and WRF-
SS are configured under the new GCM historical climate control run simulation data based on
CMIP5. It is then used for the simulations of the climate conditions over both regions.

30 years (1970-2000) of climate data from historical control simulations of 5 GCMs i.e.
CCSM4, MIROC5, MRI-CGCM3, GFDL-ESM2M, and IPSL-CM5A-LR and 94 years (2006-
2100) of GCM simulations for 16 future projections are collected. Table 2.1 and Table 2.2 list
the selected historical control simulations and future projections of the GCMs.

Dynamical downscaling by the regional numerical atmospheric model WRF-PM and WRF-
SS for the historical period are implemented, and configuration of these models are conducted
by finding the best model options by comparing their results with historical climate
observations (precipitation and air temperature). Model biases in precipitation and
temperature are obtained based on historical climate simulations for 5 GCMs in comparison
to historical climate observations to estimate the bias correction for the future climate
simulations.

Table 2.1 - The list of historical control simulations for the specified five GCMs

GCM Scenario Period

CCSM4 Historical control run 1970-2000
MIROC5 Historical control run 1970-2000
MRI-CGCM3 Historical control run 1970-2000
GFDL-ESM2M Historical control run 1970-2000
IPSL-CM5A-LR Historical control run 1970-2000

7

Table 2.2 - The list of future projections, based on four Representative Concentration
Pathways greenhouse gas emission scenarios, for the specified 5 GCMs

GCM Scenario Period
CCSM4
MIROC5 RCP4.5 2006-2100
MRI-CGCM3 RCP8.5 2006-2100
GFDL-ESM2M RCP2.6 2006-2100
RCP4.5 2006-2100
IPSL-CM5A-LR RCP6.0 2006-2100
RCP8.5 2006-2100
RCP4.5 2006-2100
RCP8.5 2006-2100
RCP2.6 2006-2100
RCP4.5 2006-2100
RCP6.0 2006-2100
RCP8.5 2006-2100
RCP2.6 2006-2100
RCP4.5 2006-2100
RCP6.0 2006-2100
RCP8.5 2006-2100

Dynamical downscaling by means of WRF-PM for 16 future climate projections by 5 GCMs
(i.e. CCSM4, MIROC5, MRI-CGCM3, GFDL-ESM2M, and IPSL-CM5A-LR) and 4 RCPs
at 6 km spatial resolution for the duration of 2006-2100 over the regions of Peninsular
Malaysia and Sabah-Sarawak are then separately carried out. Bias corrections are then
performed for the dynamically downscaled 16 future climate projections for both regions.

In this study, the potential impacts of climate change are evaluated in terms of rainfall, air
temperature, evapotranspiration, solar radiation, wind speed, and relative humidity at 6-km
grid resolution over the region of Peninsular Malaysia and Sabah-Sarawak by means of WRF-
PM and WRF-SS respectively.

8

3. GLOBAL AND REGIONAL CLIMATE CHANGE BASED ON CLIMATE
MODELS IN CMIP5

Projections of changes in the climate system are made using a hierarchy of climate models
ranging from simple climate models to comprehensive climate models, and Earth System
Models. These models simulate changes based on a set of scenarios of anthropogenic forcing.
A new set of scenarios, the Representative Concentration Pathways (RCPs), was used for the
new climate model simulations carried out under the framework of the Coupled Model
Intercomparison Project Phase 5 (CMIP5) of the World Climate Research Programme. In all
RCPs, atmospheric CO2 concentrations are higher in 2100 relative to present day as a result
of a further increase of cumulative emissions of CO2 to the atmosphere during the 21st
century. See the next section for more information on RCPs. To place climate projections in
historical context, it is necessary to consider observed changes between different periods.
Based on the longest global surface temperature dataset available, the observed change
between the average of the period 1850–1900 and of the AR5 reference period is 0.61 [0.55
to 0.67] °C. However, warming has occurred beyond the average of the AR5 reference period.
The readers can refer to IPCC (2013) for more information.

Key findings in IPCC (2013) can be summarized as:
1. Continued emissions of greenhouse gases will cause further warming and changes in
all components of the climate system. Limiting climate change will require
substantial and sustained reductions of greenhouse gas emissions.
2. Projections for the next few decades show spatial patterns of climate change similar
to those projected for the later 21st century but with smaller magnitude. Natural
internal variability will continue to be a major influence on climate, particularly in
the near-term and at the regional scale. By the mid-21st century the magnitudes of
the projected changes are substantially affected by the choice of emissions scenario.
3. Projected climate change based on RCPs is similar to AR4 in both patterns and
magnitude, after accounting for scenario differences. The overall spread of
projections for the high RCPs is narrower than for comparable scenarios used in AR4
because in contrast to the SRES emission scenarios used in AR4, the RCPs used in
AR5 are defined as concentration pathways, and thus carbon cycle uncertainties

9

affecting atmospheric CO2 concentrations are not considered in the concentration-
driven CMIP5 simulations.
4. Global surface temperature changes for the end of the 21st century is likely to exceed
1.5°C relative to 1850 to 1900 for all RCP scenarios except RCP2.6. It is likely to
exceed 2°C for RCP6.0 and RCP8.5, and more likely than not to exceed 2°C for
RCP4.5. Warming will continue beyond 2100 under all RCP scenarios except
RCP2.6. Warming will continue to exhibit inter-annual-to-decadal variability and
will not be regionally uniform (see Figures 3.1 and 3.2).
5. Changes in the global water cycle in response to the warming over the 21st century
will not be uniform. The contrast in precipitation between wet and dry regions and
between wet and dry seasons will increase, although there may be regional exceptions
(see Figure 3.2).
6. The global ocean will continue to warm during the 21st century. Heat will penetrate
from the surface to the deep ocean and affect ocean circulation.
7. It is very likely that the Arctic sea ice cover will continue to shrink and that Northern
Hemisphere spring snow cover will decrease during the 21st century as global mean
surface temperature rises. Global glacier volume will further decrease.
8. Global mean sea level will continue to rise during the 21st century (see Figure 3.3).
Under all RCP scenarios, the rate of sea level rise will very likely exceed that
observed during 1971 to 2010 due to increased ocean warming and increased loss of
mass from glaciers and ice sheets. Projections of sea level rise are larger than in the
AR4, primarily because of improved modelling of land-ice contributions.
9. Cumulative emissions of CO2 largely determine global mean surface warming by the
late 21st century and beyond (see Figure 3.4). Most aspects of climate change will
persist for many centuries even if emissions of CO2 are stopped. This represents a
substantial multi-century climate change commitment created by past, present and
future emissions of CO2.

10

Figure 3.1- CMIP5 multi-model simulated time series from 1950 to 2100 for (a) change
in global annual mean surface temperature relative to 1986–2005, (b) Northern
Hemisphere September sea ice extent (5-year running mean), and (c) global mean ocean
surface pH. Time series of projections and a measure of uncertainty (shading) are shown
for scenarios RCP2.6 (blue) and RCP8.5 (red). Black (grey shading) is the modelled
historical evolution using historical reconstructed forcings. The mean and associated
uncertainties averaged over 2081−2100 are given for all RCP scenarios as colored vertical
bars. The numbers of CMIP5 models used to calculate the multi-model mean is indicated.
For sea ice extent (b), the projected mean and uncertainty (minimum-maximum range) of
the subset of models that most closely reproduce the climatological mean state and 1979
to 2012 trend of the Arctic sea ice is given (number of models given in brackets). For
completeness, the CMIP5 multi-model mean is also indicated with dotted lines. The
dashed line represents nearly ice-free conditions (i.e., when sea ice extent is less than 106
km2 for at least five consecutive years). This figure was reproduced from IPCC (2013).

11

Figure 3.2 - Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and
RCP8.5 during 2081–2100 of (a) annual mean surface temperature change, (b) average
percent change in annual mean precipitation, (c) Northern Hemisphere September sea ice
extent, and (d) change in ocean surface pH. Changes in panels (a), (b) and (d) are shown
relative to 1986–2005. The number of CMIP5 models used to calculate the multi-model
mean is indicated in the upper right corner of each panel. For panels (a) and (b), hatching
indicates regions where the multi-model mean is small compared to natural internal
variability (i.e., less than one standard deviation of natural internal variability in 20-year
means). Stippling indicates regions where the multi-model mean is large compared to
natural internal variability (i.e., greater than two standard deviations of natural internal
variability in 20-year means) and where at least 90% of models agree on the sign of
change. In panel (c), the lines are the modelled means for 1986−2005; the filled areas are
for the end of the century. The CMIP5 multi-model mean is given in white color, the
projected mean sea ice extent of a subset of models (number of models given in brackets)
that most closely reproduces the climatological mean state and 1979 to 2012 trend of the
Arctic sea ice extent is given in light blue color. This figure was reproduced from IPCC
(2013).

12

Figure 3.3 - Projections of global mean sea level rise over the 21st century relative to
1986–2005 from the combination of the CMIP5 ensemble with process-based models, for
RCP2.6 and RCP8.5. The assessed likely range is shown as a shaded band. The assessed
likely ranges for the mean over the period 2081–2100 for all RCP scenarios are given as
colored vertical bars, with the corresponding median value given as a horizontal line. This
figure was reproduced from IPCC (2013).

13

Figure 3.4 - Global mean surface temperature increase as a function of cumulative total
global CO2 emissions from various lines of evidence. Multi-model results from a
hierarchy of climate-carbon cycle models for each RCP until 2100 are shown with colored
lines and decadal means (dots). Some decadal means are labeled for clarity (e.g., 2050
indicating the decade 2040−2049). Model results over the historical period (1860 to 2010)
are indicated in black. The colored plume illustrates the multi-model spread over the four
RCP scenarios and fades with the decreasing number of available models in RCP8.5. The
multi-model mean and range simulated by CMIP5 models, forced by a CO2 increase of
1% per year (1% yr–1 CO2 simulations), is given by the thin black line and grey area. For
a specific amount of cumulative CO2 emissions, the 1% per year CO2 simulations exhibit
lower warming than those driven by RCPs, which include additional non-CO2 forcings.
Temperature values are given relative to the 1861−1880 base period, emissions relative to
1870. Decadal averages are connected by straight lines. This figure was reproduced from
IPCC (2013).

3.1. Representative Concentration Pathways (RCPs)

For the Fifth Assessment Report of IPCC, the scientific community has defined a set of four

new scenarios, denoted Representative Concentration Pathways (RCPs). They are identified

by their approximate total radiative forcing in year 2100 relative to 1750: 2.6 W m-2 for

14

RCP2.6, 4.5 W m-2 for RCP4.5, 6.0 W m-2 for RCP6.0, and 8.5 W m-2 for RCP8.5. For the
Coupled Model Intercomparison Project Phase 5 (CMIP5) results, these values should be
understood as indicative only, as the climate forcing resulting from all drivers varies between
models due to specific model characteristics and treatment of short-lived climate forcers.
These four RCPs include one mitigation scenario leading to a very low forcing level (RCP2.6),
two stabilization scenarios (RCP4.5 and RCP6), and one scenario with very high greenhouse
gas emissions (RCP8.5). The RCPs can thus represent a range of 21st century climate policies,
as compared with the no-climate policy of the Special Report on Emissions Scenarios (SRES)
used in the Third Assessment Report and the Fourth Assessment Report. For RCP6.0 and
RCP8.5, radiative forcing does not peak by year 2100; for RCP2.6 it peaks and declines; and
for RCP4.5 it stabilizes by 2100. Each RCP provides annual greenhouse gas concentrations
and anthropogenic emissions up to 2100. RCPs are based on a combination of integrated
assessment models, simple climate models, atmospheric chemistry and global carbon cycle
models. While the RCPs span a wide range of total forcing values, they do not cover the full
range of emissions in the literature, particularly for aerosols.

Most of the CMIP5 and Earth System Model simulations were performed with prescribed
CO2 concentrations reaching 421 ppm (RCP2.6), 538 ppm (RCP4.5), 670 ppm (RCP6.0), and
936 ppm (RCP 8.5) by the year 2100. Including also the prescribed concentrations of CH4
and N2O, the combined CO2-equivalent concentrations are 475 ppm (RCP2.6), 630 ppm
(RCP4.5), 800 ppm (RCP6.0), and 1313 ppm (RCP8.5). For RCP8.5, additional CMIP5 Earth
System Model simulations are performed with prescribed CO2 emissions as provided by the
integrated assessment models. For all RCPs, additional calculations were made with updated
atmospheric chemistry data and models (including the Atmospheric Chemistry and Climate
component of CMIP5) using the RCP prescribed emissions of the chemically reactive gases
(CH4, N2O, HFCs, NOx, CO, NMVOC). These simulations enable investigation of
uncertainties related to carbon cycle feedbacks and atmospheric chemistry. The readers can
refer to IPCC (2013) for more information.

15

3.2. Global CMIP5 GCM Simulations to be Dynamically Downscaled in This Study

Implementation of dynamical downscaling by the regional numerical atmospheric model
WRF (Weather Research Forecasting model) over Peninsular Malaysia (WRF-PM) and
Sabah-Sarawak (WRF-SS) are performed for 5 GCMs for the historical control runs from
1970 to 2000 and will be performed for 16 future projections from 2006 to 2100. The list of
these GCMs and their resolutions is given below.

Model Atmospheric Grid Ocean Grid

Latitude Longitude Latitude Longitude

CCSM4 0.9424 1.25 lat(i,j) lon(i,j)

MIROC5 1.4008 1.40625 0.5, 0.5 1.40625

MRI-CGCM3 1.12148 1.125 0.5, 0.5 1

GFDL-ESM2M 2.0225 2.5 0.3344, 1 1

IPSL-CM5A-LR 1.8947 3.75 lat(i,j) lon(i,j)

Source: https://portal.enes.org/data/enes-model-data/cmip5/resolution

In the above table the grid resolutions, i.e. the distance between adjacent grid points in degrees,
are tabulated. In the case of the atmospheric grid and its latitude, the tabulated resolution is
only valid for the equator region. For higher latitudes deviations may occur. Ocean models
have their own, finer grid. If two values are given for the latitude resolution of the ocean grid,
the resolution is not constant. The first value is that for the equator, the second for the poles
(maximum for the two poles if different). lat(i,j) and lon(i,j) denote latitudes and longitudes
defined with two indices i and j. In such cases the resolution cannot simply be read out.

Sample of global climate data (i.e. near surface specific humidity, moisture in upper 0.1 meters
of soil column, surface air pressure, sea level pressure, air temperature, eastward near surface
wind, and northward near surface wind) for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC are depicted in Figures 3.5-3.10.

16

Figure 3.5 - Sample global climate data for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC: Near surface specific humidity in ()

Figure 3.6 - Sample global climate data for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC: Surface air pressure in (Pa)

17

Figure 3.7 - Sample global climate data for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC: Sea level pressure in (Pa)

Figure 3.8 - Sample global climate data for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC: Air temperature in (K)

18

Figure 3.9 - Sample global climate data for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC: Eastward near surface wind in (ms-1)

Figure 3.10 - Sample global climate data for MIROC5 historical control simulation at 1990-
01-01 00:00 UTC: Northward near surface wind in (ms-1)

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Implementation of dynamical downscaling by the regional numerical atmospheric model
WRF-PM and WRF-SS are performed for the same 5 GCMs for the 16 future projections from
2006 to 2100, as listed below:

GCM Scenario Period
CCSM4
MIROC5 RCP4.5 2006-2100
MRI-CGCM3 RCP8.5 2006-2100
GFDL-ESM2M RCP2.6 2006-2100
RCP4.5 2006-2100
IPSL-CM5A-LR RCP6.0 2006-2100
RCP8.5 2006-2100
RCP4.5 2006-2100
RCP8.5 2006-2100
RCP2.6 2006-2100
RCP4.5 2006-2100
RCP6.0 2006-2100
RCP8.5 2006-2100
RCP2.6 2006-2100
RCP4.5 2006-2100
RCP6.0 2006-2100
RCP8.5 2006-2100

Sample of global climate data (i.e. near surface specific humidity, surface air pressure, sea
level pressure, air temperature, eastward near surface wind, and northward near surface wind)
for MIROC5 future simulation at 2026-01-01 00:00 UTC are depicted in Figures 3.11-3.16.

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Figure 3.11 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00
UTC: Near surface specific humidity in ()

Figure 3.12 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00
UTC: Surface air pressure in (Pa)

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Figure 3.13 - Sample global climate data for MIROC5 RCP4.5 scenario at 12026-01-01
00:00 UTC: Sea level pressure in (Pa)

Figure 3.14 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00
UTC: Air temperature in (K)

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Figure 3.15 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00
UTC: Eastward near surface wind in (ms-1)

Figure 3.16 - Sample global climate data for MIROC5 RCP4.5 scenario at 2026-01-01 00:00
UTC: Northward near surface wind in (ms-1)

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4. THE WEATHER RESEARCH AND FORECASTING (WRF) MODEL

The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale
numerical weather prediction system designed for both atmospheric research and operational
forecasting applications. It features two dynamical cores, a data assimilation system, and a
software architecture supporting parallel computation and system extensibility. The model
serves a wide range of meteorological applications across scales from tens of meters to
thousands of kilometers. The effort to develop WRF began in the latter part of the 1990's and
was a collaborative partnership of the National Center for Atmospheric Research (NCAR),
the National Oceanic and Atmospheric Administration (represented by the National Centers
for Environmental Prediction (NCEP) and the (then) Forecast Systems Laboratory (FSL)), the
(then) Air Force Weather Agency (AFWA), the Naval Research Laboratory, the University of
Oklahoma, and the Federal Aviation Administration (FAA).

For researchers, WRF can produce simulations based on actual atmospheric conditions (i.e.
from observations and analyses) or idealized conditions. WRF offers operational forecasting
a flexible and computationally-efficient platform, while reflecting recent advances in physics,
numerics, and data assimilation contributed by developers from the expansive research
community. WRF is currently in operational use at NCEP and other national meteorological
centers as well as in real-time forecasting configurations at laboratories, universities, and
private companies.

The WRF system contains two dynamical solvers, referred to as the ARW (Advanced
Research WRF) core and the NMM (Nonhydrostatic Mesoscale Model) core. The ARW has
been developed in large part and is maintained by NCAR's Mesoscale and Microscale
Meteorology Laboratory. The NMM core was developed by the National Centers for
Environmental Prediction and is currently used in their HWRF (Hurricane WRF) system.

This study utilized the WRF model in order to dynamically downscale coarse resolution
climate data of the GCMs to a much finer spatial grid resolution (6km) over Peninsular
Malaysia (WRF-PM) and Sabah-Sarawak (WRF-SS) regions in order to account for the
impact of local topography, local land surface and landuse conditions on the local climates of
the regions.

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4.1. WRF Model over Peninsular Malaysia (WRF-PM) and Sabah-Sarawak (WRF-SS)
In this study, the implementation of the dynamical downscaling over the regions of Peninsular
Malaysia and Sabah-Sarawak for the historical and future periods are performed by the
regional numerical atmospheric model WRF (Weather Research Forecasting model)
separately. Location of the study region with its various modeling domains nested over
Peninsular Malaysia is shown in Figure 4.1a while for Sabah-Sarawak in Figure 4.1b. In these
figures, D01 denotes the large outer domain (grid size: 54km), D02 is the intermediate domain
(grid size: 18km), and the domain D03 is the inner modeling domain at 6km grid resolution.

a)
D01
D02
D03

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b)

D01
D02
D03

Figure 4.1 - The modeling domains of (a) Peninsular Malaysia and (b) Sabah and Sarawak
hydro-climate models. D01 denotes the large outer domain at grid size 54km, D02 as the
intermediate domain at grid size 18km, and D03 as the inner modeling domain where the
modeling studies are being carried out, is at 6km grid resolution
4.2. Geographic Information System Database for the WRF Model over Peninsular

Malaysia
The Weather Research and Forecasting Model (WRF) requires geographical data within
simulation domains. The website of WRF contains all the geographical datasets required for
WRF simulation.
The topographic elevations (m) in Peninsular Malaysia and Sabah-Sarawak are shown in
Figure 4.2. The topographic elevation is based on the 30-second Digital Elevation Model data
(DEM) by the U.S. Geological Survey (USGS). Figure 4.3 depicts the slope of orography
around Peninsular Malaysia and Sabah and Sarawak. The slope is unitless.

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a)

b)

Figure 4.2 - The topographic elevation (m) in (a) Peninsular Malaysia and (b) Sabah and
Sarawak

Figures 4.4a and 4.4b show the 16-category top-layer and bottom-layer soil types,
respectively. The colour bars on the right show soil categories from 1 to 16, description of
which are tabulated in Table 4.1. Figure 4.5 demonstrates a sample figure for monthly surface
albedo (in %, for January), and Figure 4.6 demonstrates a sample figure for monthly green
fraction (for January). Surface albedo is the measure of the diffuse reflection of solar radiation
out of the total solar radiation. A surface with high albedo reflects a lot of solar radiation from

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the sun back into the atmosphere, but a surface with low albedo reflects little solar radiation,
absorbing it instead. Surface albedo varies locally because of different geological and
environmental features. Green fraction corresponds to the fraction of ground covered by green
vegetation and quantifies the spatial extent of the vegetation.

a)

b)

Figure 4.3 - Slope of orography around (a) Peninsular Malaysia and (b) Sabah and Sarawak

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