regional climate projections indonesianausaid-final report-v7

8/8/2019 Regional Climate Projections IndonesianAusAID-Final Report-V7

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The Centre for Australian Weather and Climate Research

A partnership between CSIRO and the Bureau of Meteorology

REGIONAL CLIMATE CHANGE PROJECTION

DEVELOPMENT AND INTERPRETATION FOR

INDONESIA

Jack Katzfey, John McGregor, Kim Nguyen and Marcus Thatcher

14 March 2010

Final Report for AusAID


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ACKNOWLEDGEMENTS

The authors would like to acknowledge the assistance of Dr. Mezak Ratag of BMKG,

Indonesia, for help in selecting participants from Indonesia for this project. We also would like

to thank the workshop participants for their hard work and enthusiasm, and for sharing theirknowledge and perspectives. Finally, we would like to thank all the lecturers for their effort in

preparing, presenting and discussing their work.

We acknowledge the modelling groups, the Program for Climate Model Diagnosis and

Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM)for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this

dataset is provided by the Office of Science, U.S. Department of Energy.

We would also wish to thank AusAID for funding of this research.


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Enquiries should be addressed to:

Jack Katzfey

Mesoscale Modelling Applications Team Leader

Centre for Australian Weather and Climate Research

CSIRO Marine and Atmospheric ResearchAspendale, VIC Australia 3195

[email protected]

Copyright and Disclaimer 2010 CSIRO To the extent permitted by law, all rights are reserved and no part of thispublication covered by copyright may be reproduced or copied in any form or by any means

except with the written permission of CSIRO.

Important DisclaimerCSIRO advises that the information contained in this publication comprises general statements

based on scientific research. The reader is advised and needs to be aware that such informationmay be incomplete or unable to be used in any specific situation. No reliance or actions must

therefore be made on that information without seeking prior expert professional, scientific and

technical advice. To the extent permitted by law, CSIRO (including its employees and

consultants) excludes all liability to any person for any consequences, including but not limited

to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly

from using this publication (in part or in whole) and any information or material contained in it.

Cover Figure: Conformal-cubic model grid used for 60 km resolution simulations.


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Contents

Executive Summary ................................................................................................... 71. Introduction ....................................................................................................... 82. Methodology ...................................................................................................... 9

2.1 Choice of SRES scenarios .................................................................................... 92.2 Choice of coupled general circulation models........................................................ 92.3 Introduction to CCAM ......................................................................................... 102.4 Downscaling methodology .................................................................................. 10

3. Regional Climate Simulations for Indonesia ................................................. 113.1 Present-day climatology...................................................................................... 123.2 Simulation of models with climate change signal ................................................. 17

3.2.1 Projected rainfall changes from 1971-2000 to 2081-2100............ ......................173.2.2 Seasonal rainfall changes ................................................................................173.2.3 Annual rainfall changes ...................................................................................193.2.4 Seasonal and annual changes in maximum and minimum temperatures ...........203.2.5 Seasonal and annual changes in pan evaporation ............................................24

4. Analysis workshop at Aspendale................................................................... 265. Follow-up activities ......................................................................................... 286. Future Directions ............................................................................................ 287. Conclusions .................................................................................................... 29References ................................................................................................................ 31Appendix A Workshop participants ..................................................................... 33Appendix B Workshop lectures and lecturers .................................................... 34Appendix C - CCAM Documentation ....................................................................... 35Acronyms ................................................................................................................. 37


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List of FiguresFigure 1: Land-sea mask and orography (contours, m) (a) CSIRO Mk3.5 GCM; (b) CCAM

with resolution of about 60 km over Indonesia................................................................. 8Figure 2: Sea surface temperature bias (C) in CSIRO Mk3.5 GCM for January. ................. 10Figure 3: Downscaling using CCAM (a) the quasi-uniform CCAM C48 grid, with a resolution

of about 200 km over the entire globe; (b) the stretched C48 grid, with resolution of about60 km over Indonesia. .................................................................................................. 11

Figure 4: DJF maximum and minimum temperatures (C) over Indonesia, for the period1971-2000 (CCAM simulations in top row, CRU observations in bottom row). ............... 12

Figure 5: JJA maximum and minimum temperatures (C) over Indonesia, for the period 1971-2000 (CCAM simulations in top row, CRU simulations in bottom row). .......................... 13

Figure 6: Present-day rainfall (mm/day) over Indonesia in DJF. GPCP observed (top left);host GCMs (top), CCAM 200 km simulations (middle) and CCAM 60 km downscaled runs(bottom), with names of the host GCMs above the figure. ............................................ 13

Figure 7: Present-day rainfall (mm/day) over Indonesia in JJA. GPCP observed (top left);host GCMs (top), CCAM 200 km simulations (middle) and CCAM 60 km downscaled runs(bottom), with name of host GCM above figure. ............................................................ 14

Figure 8: CCAM ensemble simulations of present-day rainfall over Indonesia (mm/day) forDJF (top row) and MAM (bottom row). Observed rainfall (left column), simulations (rightcolumn). ....................................................................................................................... 15

Figure 9: CCAM ensemble simulations of present-day rainfall over Indonesia (mm/day) forJJA and SON. Observed rainfall (left column), simulations (right column). .................... 16

Figure 10: Annual rainfall changes (mm) between future (2081-2100) and present (1971-2000). Six-member ensemble mean of CCAM 60 km downscaled simulation (left) andhost GCMs simulations (right). ...................................................................................... 17

Figure 11: Seasonal rainfall changes (mm/day) over Indonesia. CCAM 60 km simulationsbased on GFDL2.1 (left column), ECHAM5 (middle column) and HadCM3 (right column)..................................................................................................................................... 19

Figure 12: Annual rainfall changes (mm/day) over Indonesia. CCAM 60 km simulationsbased on GFDL2.1 (left column), ECHAM5 (middle column) and HadCM3 (right column).................................................................................................................................... 20

Figure 13: Seasonal (first four rows) and annual (bottom row) changes in maximumtemperature (C) over Indonesia. CCAM 60 km simulations based on GFDL2.1 (leftcolumn), ECHAM5 (middle column) and HadCM3 (right column). ................................. 22

Figure 14: Seasonal (first four rows) and annual (bottom row) changes in minimumtemperature (C) over Indonesia. CCAM 60 km simulations based on GFDL2.1 (left

column), ECHAM5 (middle column) and HadCM3 (right column). ................................. 23Figure 15: Seasonal (first four rows) and annual (bottom row) changes in pan evaporation

(mm/day) over Indonesia. CCAM 60 km simulations based on GFDL2.1 (left column),ECHAM5 (middle column) and HadCM3 (right column). ................................................ 25

Figure 16: Participants in the 2009 Analysis Workshop at CMAR-Aspendale, with some of thelecturers. ...................................................................................................................... 26

Figure 17: Photographs of the participants in the 2009 Analysis Workshop taken duringlectures, excursions and workshop dinner..................................................................... 27

Figure 18: Images from the PowerPoint presentation given by Halimurrahman, one of thescientists attending the 2009 Analysis Workshop at CMAR-Aspendale. ........................ 28
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List of TablesTable 1: The six GCMs chosen for use in this project, along with their country of origin and

approximate horizontal resolution. .................................................................................. 9Table 2: Organisations and number of participants at workshop ........................................... 26


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EXECUTIVE SUMMARY

IPCC climate change projections are available for Indonesia and other parts of the Asia-

Pacific region, but there is limited ability to utilise this information on a regional scale as the

information provided is too coarse. Such countries then need the ability to downscale this

information to produce finer resolution projections of future climate for their own regionalpurposes. This project addressed these issues through regional climate modelling over

Indonesia, Vietnam and the Philippines, providing participants with datasets and skills toassess possible impacts of climate change over their areas of interest.

Fine-resolution downscaling is needed for good simulation of rainfall patterns over the

maritime continent of Indonesia because it better represents the topography and other

features, providing more realistic climate simulations than global simulations, which are

normally run on a 200 km grid. This project addresses these issues through regional climate

modelling over Indonesia using Conformal-Cubic Atmospheric Model (CCAM) at 60 km

horizontal resolution. In order to better capture the uncertainty of climate change, six

different IPCC AR4 global coupled models (GCMs) with monthly bias-corrected SSTs were

used to force CCAM. The three time periods simulated were from 1971 to 2000, 2041 to 2060and 2081 to 2100 for the A2 emission IPCC scenario. The dataset produced has undergone

preliminary analysis and will be extended for use in future research in the region.

Capacity building was provided during a two-week workshop on the use of regional climate

models and the interpretation of climate projection data, training 14 scientists from Indonesia,

the Philippines and Vietnam through lectures and tutorials, as well as hands-on data

manipulation. The participants shared their expertise and experiences, developing individualresearch projects and presenting talks at the end of the workshop.

The Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) will

continue to run scenarios using CCAM, providing information for informing policy andadaptation decisions. BMKG staff will use the datasets and skills developed in the workshop

to assess possible impacts of climate change over Indonesia so they are better able toparticipate in policy decisions about adaptation to potential changes. In addition, downscaled

climate changes results generated by CCAM have already been used by Conservation

International and a project in Indonesia funded by the Asian Development Bank. Philippine

and Vietnamese participants are also negotiating to use CCAM to produce further downscaled

climate change simulations over their countries.

As a result of this project Indonesia, Vietnam, Philippines and South Africa are discussingwith CSIRO the possibility of setting up a consortium to further develop and apply CCAM for

weather and climate research.

Many of the AusAID Phase 1 projects involve projection of effects of climate change onfuture water supplies, agriculture, ecosystems and biodiversity, which in turn have effects on

human health and wellbeing. By providing better downscaled regional climate change

information, this project will aid future policy decisions and decrease vulnerability to the

adverse effects of climate change.

Building upon this project and as part of the Pacific Climate Change Science Program (part of

the Australian Governments International Climate Change Adaptation work), CSIRO is

running a global 60 km CCAM climate simulation with multiple global climate models for

the period 1971-2100 for the Asia-Pacific region, and will make this dataset available to

countries in the area, with further support in interpretation as needed.


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1. INTRODUCTION

Climate change has been identified as an urgent threat to the Asia-Pacific region, spurring

AusAID to instigate a series of short, tactical projects to understand the impacts of climate

change and identify ways to adapt to changes to ensure the health and wellbeing of the

inhabitants of the region.

Climate change projections from the Fourth Assessment Report of the IPCC (AR4) are

available, but there is limited ability to utilise this information on a regional scale, as the

information provided is too coarse. In the final report of another Phase 1 AusAID funded

project,Assessing the vulnerability of rural livelihoods in the Pacific to climate change (Parket al., 2009), it was noted that East Timor has a relatively high vulnerability to climate

change, but the use of Indonesian data as a proxy was too coarse to determine this adequately

(p. 41 of report).

To properly simulate the rainfall patterns over Indonesia and other countries in the region,

fine-scale simulations are needed to capture the effects of topography. The many islands

produce local circulation and convection effects that can not be captured by a coarse model.Also, the mountains in the region have significant effects on the weather and climate. This

project addresses these issues through regional climate model simulations over Indonesia and

other countries in the Asia-Pacific region. An example of the land-sea mask and orography as

portrayed by a GCM and the CCAM 60 km grid is shown in Figure 1. Note the much more

realistic representation of Indonesia and the mountains in the 60 km grid versus the GCM.

Downscaled model simulations at 60 km resolution over Indonesia were produced using an

ensemble made up of six host global climate models (GCMs) for the periods 1971-2000,

2041-2060, 2081-2100 for the IPCC A2 emission scenario. These time periods were chosen to

capture the current (1971-2000), near future (2041-2060) and end of the century (2081-2100)climate. Constraints on the project prevented running continuously for the full 130 years. The

simulations were produced using the CSIRO CCAM, driven by the sea surface temperatures

(SSTs) of the six host GCMs. A more thorough discussion of the methodology is given in thenext section.

As well as producing a more detailed and complete climate dataset, scientists from the region

were trained in its analysis in some detail during a two-week workshop held in Melbourne

during May 2009, so that the downscaled results could be tailored to their particular needs.

(a) (b)

Figure 1: Land-sea mask and orography (contours, m) (a) CSIRO Mk3.5 GCM; (b) CCAM with

resolution of about 60 km over Indonesia.


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2. METHODOLOGY

In this section, the method used to select the host models and the process of downscaling are

described in detail. The procedure used was to pick an emission scenario, select which GCMs

to downscale, use CCAM to downscale to 200 km and finally use CCAM to downscale from

200 km to 60 km resolution. The methodology used for each step is described below.

2.1 Choice of SRES scenarios

It has been stated in the IPCC (2007) report that it is highly likely that anthropogenic

pollutants are responsible for recent global warming and the extent of anticipated climatechange is dependent on the amount of future greenhouse gas emissions. Hence, the choice of

an emission scenario to use in the climate simulations is important. The most commonly used

and accepted set of greenhouse gas emission scenarios, known as the SRES scenarios, comes

from the IPCC (Nakicenovic et al., 1992). In this project, we chose to downscale GCM

model data from the A2 climate scenario because current emission levels are at or above those

specified for this scenario and therefore appear to be realistic.[www.fas.org/sgp/crs/misc/RS22970.pdf]

2.2 Choice of coupled general circulation models

Global general circulation models simulate the Earths atmosphere, oceans and ice throughcoupling the various components. The computational effort to accomplish this, and to run

long climate simulations, restricts one to relatively coarse horizontal resolution. Although the

IPCC used data from 23 GCMs when compiling its Fourth Assessment Report (AR4), in thisproject only six of these GCMs were used to produce the fine-scale climate projections over

Indonesia. Because each model varies slightly in its internal structure and physical

parameterizations, the use of more than one model in an ensemble prediction is an accepted

technique for obtaining more realistic results. The six models were chosen for this study

based on the work of Smith and Chandler (2009), who assessed the ability of the models to

simulate present-day means and variability and found that regional projections of rainfall,especially, can be improved when data from the poorly performing models are removed from

the ensemble. The six GCMS utilised in this project also tended to have better than average

El Nios and Australia-wide verification statistics, according to Smith and Chandler (2009).

Corresponding analyses have not been completed over Indonesia.

Another consideration when choosing the six GCMs was to ensure that each of the selected

models had been run by IPCC for the chosen emissions scenarios, since the downscaling

technique requires input of data from the GCMs. The final list of six GCMS that were chosen

for this project is given in Table 1. All are well-known models that have been used in avariety of applications for climate change study.

GCM Country of origin Approximate horizontal resolution (km)

CSIRO Mk3.5 Australia 200

GFDLCM2.0 USA 300GFDLCM2.1 USA 300

ECHAM5/MPI Germany 200

MIROC3.2 (med res) Japan 300

HadCM3 United Kingdom 300

Table 1: The six GCMs chosen for use in this project, along with their country of origin and approximate

horizontal resolution.


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By concentrating our efforts on these six models in an ensemble projection we aim to assess

some of uncertainty associated with downscaling all 23 IPCC runs, while also maximising the

value of our model results.

2.3 Introduction to CCAM

CSIRO Marine and Atmospheric Research has been undertaking regional climate modelling

for well over two decades. For much of this time the CCAM has been the mainstay of CSIRO

dynamical downscaling (McGregor 2005; McGregor and Dix 2008). CCAM is a full

atmospheric global climate model based on a conformal-cubic grid (see front cover of this

report and Figure 3). For the downscaling experiments of this project, CCAM was configured

to use a stretched grid, which allowed a higher resolution of 60 km in the areas of interest

over Indonesia. [See Appendix C for more information on CCAM.] CCAM has been used for

several over projects over the tropical region, such as McGregor and Nguyen (2008),

McGregor and Nguyen (2009), McGregor et al. (2008a), McGregor et al. (2008b), McGregor

et al. (2009), Nguyen and McGregor (2009).

2.4 Downscaling methodology

Downscaling involves several steps. The first step is to remove the Sea Surface Temperature

(SST) biases from the host GCM simulations. This is because all GCMs have SST biases due

to the coarse resolution of the GCMs and many unresolved physical and dynamical processesin the models. The SST bias of the CSIRO Mk3.5 GCM for January is shown in figure 2. The

SST biases produce air-sea fluxes that affect the atmospheric downscaling model and cause

deficiencies in the simulated climate. To correct the biases, the global monthly values for the

SSTs simulated by the GCMs for the current climate period (1971-2000) are compared with

the monthly values of the National Oceanic and Atmospheric Administration (NOAA)

Optimal Interpolation SST analysis dataset Reynolds (1988) for the same period. Theseglobal monthly biases are subtracted from the individual monthly GCM SST fields before it isused in the downscaled simulation (since the model used is global, global SSTs are required).

Because the same bias correction is used throughout the climate projections the technique

preserves the inter- and intra-annual variability of the host GCM and also preserves the

climate change signal.

Figure 2: Sea surface temperature bias (C) in CSIRO Mk3.5 GCM for January.


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Then a quasi-uniform 200 km CCAM (Figure 3a) atmospheric climate simulation driven only

by the bias-corrected, interpolated SSTs and sea ice concentrations from the GCMs isperformed. Note that no atmospheric forcing was applied to the downscaled 200 km CCAM

simulations. It was decided that with the bias corrected sea surface, there might be an

inconsistency with the atmospheric fields coming from the GCM, so they were not used.

These 200 km runs were then further downscaled to 60 km (Figure 3b) by running CCAM

with a stretched grid. The resolution of 60 km was chosen to balance the computational

demand with the resolution required to capture the main islands and topography of the region.For these downscaled simulations, digital filter forcing (Thatcher and McGregor, 2009) of

surface pressure, wind, temperature and moisture above 850 hPa was used every 6 h to

preserve the large-scale patterns generated by the 200 km simulations while allowing fine-

scale detail to develop.

The runs were completed for the following time periods: 1971-2000, 2041-2060, and 2081-

2100. These periods (present, mid century and end of century) were chosen to capture the

current climate and the future climate change signal. Ideally, a continuous run (1971-2100)

would be preferred, but due to time and resource constraints, only the three time periods werecompleted. All the CCAM runs used the same distributions as the GCMs for CO2, ozone and

aerosols. As with most of the GCMs, only the direct effect of aerosols was included in the

simulations.

In this report, sample output presented is mainly from three CCAM simulations: GFDL2.1,

ECHAM5 and HadCM3 to give some idea of the spread between the various runs.

3. REGIONAL CLIMATE SIMULATIONS FOR INDONESIA

In this section, a selection of current and future climate results are presented. Due to the large

amount of data generated in these runs, the following discussion represents a summary of the

work, rather than a complete analysis. Up to 140 different variables are available from the

model runs. Most data is at 6 hour intervals and in netcdf format, though this can beconverted to other formats upon request.

Figure 3: Downscaling using CCAM (a) the quasi-uniform CCAM C48 grid, with a resolution of about

200 km over the entire globe; (b) the stretched C48 grid, with resolution of about 60 km over Indonesia.

(a) (b)


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3.1 Present-day climatology

Comparison of seasonal DJF (December-January-February) maximum and minimum

temperatures from the 60 km simulations with the Climate Research Unit (CRU, based in the

University of East Anglia, UK, New et al., 1999) 50 km climatology is shown in Figure 4.

The CRU climatology is a gridded observational dataset, only over land. The agreementbetween the CCAM results and the CRU is very good; however, a slight warm bias exists

over Australia. It should be noted that the station data used for the CRU analyses is rather

sparse, especially in regions of high orography such as, Papua New Guinea, where there are

not many mountain observing stations. The maximum and minimum temperatures for JJA

(June-July-August) (Figure 5) show similar good agreement between the CCAM downscaledresults and the observed CRU climatology.

Figure 4: DJF maximum and minimum temperatures (C) over Indonesia, for the period 1971-2000

(CCAM simulations in top row, CRU observations in bottom row).

DJF maximum and minimum temperatures over Indonesia


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Figure 5: JJA maximum and minimum temperatures (C) over Indonesia, for the period 1971-2000

(CCAM simulations in top row, CRU simulations in bottom row).

Figure 6: Present-day rainfall (mm/day) over Indonesia in DJF. GPCP observed (top left); host GCMs

(top), CCAM 200 km simulations (middle) and CCAM 60 km downscaled runs (bottom), with names of

the host GCMs above the figure.

ECHAM5GFDL2.1

GCM

CCAM2

00km

CCAM6

0km

JJA maximum and minimum temperatures over Indonesia

Simulations of present-day DJF rainfall over Indonesia


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The observed DJF rainfall climatology from Global Precipitation Climatology Project (GPCP,

Adler et al., 2003) data, at 1 degree latitude-longitude grid GPCP, is compared with the GCMrainfall of GFDL2.1 and ECHAM5, the rainfall in the CCAM 200 km simulations and the

CCAM 60 km simulations as shown in figure 6. Here, the GPCP data was used instead of the

CRU dataset, since the latter is only over land and we want to validate the model over the

whole region for precipitation. A key feature of the DJF observed rainfall is the band ofrainfall amount greater than 8 mm/day over the Indonesian archipelago, decreasing to 4 to 8

mm/day between Indonesia and the Philippines, and less than 0.5 mm/day over Southeast

Asia). Although the global models capture the dry season over Southeast Asia for this DJFperiod, they produce too much rainfall (more than 8 mm/day) between Indonesia and the

Philippines. The downscaled CCAM runs help to correct this problem and have a more

realistic rainfall pattern, though inaccuracies still exist.

Similarly to the DJF rainfall, the JJA rainfall is shown in Figure 7. A north to south gradient

of rainfall is evident, with the Southeast Asia monsoon is clearly evident (with rainfall

amounts of over 8 mm/day) decreasing to the dry season over Australia (with rainfall amounts

less than 0.5 mm/day). Although the GCMs capture the overall gradient of rainfall, less in the

south - more in the north, the pattern of rainfall over Indonesia in the GCMs is incorrect, with

too much rainfall along the equator. The CCAM simulations again help to correct this

problem and have a more realistic distribution of rainfall.

Figure 7: Present-day rainfall (mm/day) over Indonesia in JJA. GPCP observed (top left); host GCMs

(top), CCAM 200 km simulations (middle) and CCAM 60 km downscaled runs (bottom), with name of

host GCM above figure.

GFDL2.1 ECHAM5

GCM

CCAM2

00km

CCAM6

0km

Simulations of present-day JJA rainfall over Indonesia


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A more detailed seasonal comparison of the rainfall in the observed GPCP climatology withthe six member CCAM 60 km simulation ensemble mean is shown in Figure 8. For DJF,

CCAM captures the band of higher rainfall over Indonesia, the minimum to the north of

Papua New Guinea, and the second maximum extending towards the Philippines. A dual

Inter Tropical Convergence Zone (ITCZ) structure is evident over the Pacific in both the

observations and the ensemble mean, though more emphasized in the downscaled results. In

MAM (March-April-May), a transition season, the observed maximum amounts decrease, a

feature captured by the ensemble mean.

Observed

DJF

MAM

6 member ensemble mean

Figure 8: CCAM ensemble simulations of present-day rainfall over Indonesia (mm/day) for DJF (top

row) and MAM (bottom row). Observed rainfall (left column), simulations (right column).

CCAM ensemble simulations of present-day rainfall over Indonesiafor DJF and MAM


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During June July August (JJA) (as seen in Figure 9), the band of maximum rain has shiftednorthward to Southeast Asia, extending through the Philippines and into the Pacific. The

large rainfall amounts are in regions where there are either orographic effects or perhapstropical cyclones [or Southwest monsoon]. The model captures these effects, although

possibly shifting too far north the maximum rainfall east of the Philippines. A second

maximum of rainfall occurs west of the Indonesian archipelago, which is also simulated well.

The minimum of rainfall along the equator near Malaysia is possibly too dry in CCAM. BySeptember-October-November (SON), the peak rainfall amounts have decreased and have

started shifting southward, which is captured by the model.

Observed

JJA

SON

6-member ensemble mean

Figure 9: CCAM ensemble simulations of present-day rainfall over Indonesia (mm/day) for JJA and

SON. Observed rainfall (left column), simulations (right column).

CCAM ensemble simulations of present-day rainfall over Indonesia

for JJA and SON


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3.2 Simulation of models with climate change signal

It has been verified in the last section that CCAM results for the current climate agree very

well with observations. CCAM is then simulated with climate change signal and the results

from the model are presented here. Again, it should be noted here that this is only a smallsample of the total dataset available. The changes presented here are for the 20 year future

climate mean minus the 30 year current climate mean.

3.2.1 Projected rainfall changes from 1971-2000 to 2081-2100

The first results presented show the six model ensemble mean change in annual rainfall.

Results for the 60 km CCAM downscaled runs as well as from the corresponding GCMs are

shown in Figure 10. Projections show that in 2081-2100, there is a tendency for Java to

become drier, a tendency for northern Sumatra to become wetter, with mixed results over

Borneo. The large-scale pattern of changes is somewhat similar between the CCAM runs and

the GCMs, although there are significant differences, especially over Irian Jaya and PapuaNew Guinea, where the GCMs show rainfall increases while CCAM shows rainfall decreases.

3.2.2 Seasonal rainfall changes

A comparison of the current and simulated seasonal rainfall over Indonesia produced by three

of the CCAM runs chosen for this study (GFDL2.1, ECHAM5 and HadCM3) for the period

1971-2000 to 2081-2100 is given in Figure 11. Note that changes given are in mm/day.

Although an increase of 1 mm/day appears to be quite small, this equates to about 90 mm for

the season.

December-January-February

The three models agree on increased rainfall over southern Sumatra (by about 0.5 mm/day),

Borneo (by 0.5 to 1.5 mm/day) and Sulawesi (by 0.5 to 1.5 mm/day). Over northern Sumatra

there may be declines of 0.5 mm/day. Over Java and islands to the east, CCAM/GFDL 2.1

and CCAM/ECHAM5 indicate small increases, whilst CCAM/HadCM3 shows decreases of

0.5 to 1 mm/day.

Host GCMs

Figure 10: Annual rainfall changes (mm) between future (2081-2100) and present (1971-2000). Six-

member ensemble mean of CCAM 60 km downscaled simulation (left) and host GCMs simulations

CCAM 60 km runs


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March-April-May

The three model runs agree on increased rainfall over Sumatra, Borneo and Sulawesi by up to

0.5 mm/day. There may be some increases over Sumatra of up to 1 mm/day. Over Java there

should be little change. The models agree on decreased rainfall on the islands east of Java of

0.5 to 1 mm/day.

June-July-August

All three models produce mixed increases and decreases of rainfall over Sumatra, Borneo andSulawesi of up to 0.5 mm/day. Over Java and islands to the east, the models generally agree,

with declines in rainfall of 0.5 to 1.5 mm/day.

September-October-November

The models show mixed increases and decreases of rainfall over Sumatra up to 0.5 mm/day.The first two models show little change over Borneo, Sulawesi, Java and islands to the east,

whereas CCAM/HadCM3 shows a decline in rainfall over those islands of about 0.5 mm/day.


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3.2.3 Annual rainfall changes

The three CCAM simulations agree on increasing annual rainfall over Sumatra, Borneo and

Sulawesi by around 0.5 mm/day (see Figure 12). Over Java and islands to the east there is

less agreement, with small increases in annual rainfall from CCAM/GFDL 2.1 and

CCAM/ECHAM5, but decreases of around 0.5 mm/day from CCAM/HadCM3. The spread

in the changes of rainfall is associated with many factors. Primarily, since all CCAM

simulations were with the same model, the differences between simulated changes are a resultof differences in SSTs coming from the host GCMs. In addition, there is different

characteristic inter- and intra-annual variability in the runs, due to different model physics,

which could lead to differences in the rainfall changes. The spread between the six

simulations is one indication of the uncertainty of climate change; it is more useful to

describe a range of possible changes that are consistent with future global warming scenarios

DJF

JJA

MAM

SON

Figure 11: Seasonal rainfall changes (mm/day) over Indonesia. CCAM 60 km simulations based on

GFDL2.1 (left column), ECHAM5 (middle column) and HadCM3 (right column).

GFDL2.1 ECHAM5 HadCM3


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rather than make a single best guess which may not be representative of the risks andimpacts of climate change on the region.

The annual rainfall changes produced by each of these three models can be compared with the

ensemble mean changes shown in Figure 10, indicating that there are regions where the

models agree with the mean, whereas in other areas some models have larger changes ofsimilar sign which dominate the ensemble mean. For example, the ensemble mean decrease

in rainfall to the north of Java is dominated by decreases in the CCAM/HadCM3 run, while

the other two runs have only small changes. When assessing climate impacts, it is sometimesuseful to know the range of the possible climate change, as well as the mean. The most

extreme case would be where three of the models in a 6-member ensemble run show positive

changes, while the other three show negative changes of about equal magnitude, producing a

mean of zero, with the possibility that the climate variable of interest might actually show

larger variation.

3.2.4 Seasonal and annual changes in maximum and minimumtemperatures

A comparison of seasonal and annual simulations of changes in maximum temperature over

Indonesia produced by the three CCAM simulations, GFDL2.1, ECHAM5 and HadCM3 for

the period 1971-2000 to 2081-2100 are presented in Figure 13. Similar figures for minimum

temperature change are shown in Figure 14.


All three models show increases in maximum temperatures in DJF ranging from 0.5C to2C. The CCAM/GFDL2.1 simulation shows large increases, while the CCAM/ECHAM5

and CCAM/HADCM3 runs show small increases. All show strong increases in the south of

Java than in other regions. The CCAM/GFDL2.1 simulation also shows large increases (1.5

to 2C) south of the Philippines, while the other two models show small increases there (0.5

to 1C). The changes in minimum temperatures exhibit a similar pattern to changes inmaximum temperatures, however, the increase is more over the land compared to that over

the water.

March-April-May

In MAM, the increase over land in the CCAM/GFDL2.1 run are in the 2 to 2.5C range, with

the other two models showing slightly smaller increases. Minimum temperature changes for

ANN

HadCM3ECHAM5GFDL2.1

Figure 12: Annual rainfall changes (mm/day) over Indonesia. CCAM 60 km simulations based on

GFDL2.1 (left column), ECHAM5 (middle column) and HadCM3 (right column)


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MAM are similar to maximum temperature changes over the water, but are slightly larger

over Indonesian land masses.

June-July-August

The changes in maximum temperature in JJA are more similar between the models than for

the other seasons, generally showing increases of 1 to 1.5C over the oceans and of varying

amounts over land. Again, the CCAM/ECHAM5 run shows less of an increase than the

others, with increases in the CCAM/HadCM3 run being similar in magnitude to those in the

CCAM/GFDL2.1 run. Similarly to the other seasons, minimum temperature increases are

greater over land and similar over water.


By SON, the CCAM/GFDL2.1 run shows slightly greater increases than in JJA (greater than

1.5C), while the CCAM/ECHAM5 run shows smaller increases (0.5 to 1.5C) and theCCAM/HADCM3 run again showed increases of 1 to 1.5C, similar to JJA. Minimum

temperature increases for all three models are slightly smaller than maximum temperature

changes over water, but similar over land.

Annual changes

The annual mean temperature changes confirm that the CCAM/GFDL2.1 run show the largest

warming (1 to 2C over Indonesia) and the CCAM/ECHAM3 run shows the least warming

(0.5 to 1.5C). In general, the pattern of warming is similar in all the models. Similar results

are evident for minimum temperature increases, with slightly smaller increases over water and

slightly larger increases over land compared with maximum temperatures changes.


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DJF

JJA

MAM

SON

ANN

Figure 13: Seasonal (first four rows) and annual (bottom row) changes in maximum temperature (C)

over Indonesia. CCAM 60 km simulations based on GFDL2.1 (left column), ECHAM5 (middle column)

and HadCM3 (right column).

GFDL2.1 HadCM3ECHAM5


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DJF

JJA

MAM

SON

ANN


Figure 14: Seasonal (first four rows) and annual (bottom row) changes in minimum temperature (C)

over Indonesia. CCAM 60 km simulations based on GFDL2.1 (left column), ECHAM5 (middle column)

and HadCM3 (right column).


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3.2.5 Seasonal and annual changes in pan evaporation

A comparison of seasonal and annual simulations of changes in pan evaporation overIndonesia between the periods 1971-2000 and 2081-2100 produced by three of the CCAM 60

km simulations chosen for this study is given in Figure 15. Pan evaporation gives an

indication of the net effect of evaporation from a water mass, such as, a dam or a reservoir,

due to temperature, humidity and wind changes. The changes are independent of the soil

properties and soil moisture. These results can be used to capture the first order surface

evaporation effects.


The three models generally agree, with small decreases over the equatorial waters, someincreases over land, and larger increases over Southeast Asia (around 2 mm/day) and

Australia (1 mm/day). Sumatra shows increases in all models, while Kalimantan and Irian

Jaya show differing changes in the different models.

March-April-May

In MAM, the CCAM/GFDL2.1 run changes sign from slight decrease to increases. Othermodels also show a tendency for increases in evaporation. The pan evaporation over

Australia increased from 1 to 1.5 mm/day.

June-July-August

In this season, all three runs show increased pan evaporation over most of Indonesia of 1 to1.5 mm/day. The increases over Southeast Asia and Australia are now only about 1 mm/day.


The models continue to show increased pan evaporation over Indonesian land, while over theoceans, changes have gone slightly negative in the CCAM/GFDL2.1 run, while the

CCAM/ECHAM5 and CCAM/HADCM3 runs show increases, including increases of greater

than 0.5 mm/day in the CCAM/HADCM3 run north of Kalimantan.

Annual changes

The annual changes in all three models tend to be smaller than the seasonal changes becausesome seasons show increases while others shows decreases, but all models show larger annual

increases over land than ocean, with the largest increases over Southeast Asia and Australia.


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DJF

JJA

MAM

SON

ANN


Figure 15: Seasonal (first four rows) and annual (bottom row) changes in pan evaporation (mm/day) over

Indonesia. CCAM 60 km simulations based on GFDL2.1 (left column), ECHAM5 (middle column) and

HadCM3 (right column).


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4. ANALYSIS WORKSHOP AT ASPENDALE

The two-week workshop on the use of regional climate models and the interpretation of

climate projection data was conducted in the lecture theatre at CMAR Aspendale from 18 to29 May 2009. Fourteen participants, as recommended by Prof. Mezak Ratag of BMKG

attended from Indonesia, the Philippines and Vietnam:

BMKG, Jakarta, Indonesia 4

Institute of Technology, Bandung, Indonesia (ITB) 3

LAPAN (Space Research Agency in Bandung, Indonesia) 3

University of Hanoi, Vietnam 1

PAGASA (Meteorological Service of Philippines) 3

Table 2: Organisations and number of participants at workshop

The workshop was mainly focussed at training the scientists to interpret climate projectiondata for their particular region. It has helped in capacity building for the scientists and their

organisations and increasing the effectiveness of their forecasting/projection techniques. Theworkshop has also given them a chance to share information on the research conducted at

their respective organisations on the specific problems inherent to their region. It has also

developed working relationships with the scientists at CMAR and in other parts of the Asia-

Pacific region.

Figure 16: Participants in the 2009 Analysis Workshop at CMAR-Aspendale, with some of the lecturers.

The lecture theatre was set up with several desktop computers for shared use. Many

participants were also able to use their laptops connected via the Divisions wireless network.

Lectures (see the schedule in Appendix B) and tutorials were conducted everyday on the

CCAM regional climate modelling system which included analysis of the simulations. The

attendees were grouped by their institutes, mostly in groups of 2 or 3, to work on their ownselected projects, analysing the behaviour of the CCAM simulations for their own country.

They were assisted by CMAR staff in this activity, mainly by Drs Marcus Thatcher, Kim

Nguyen, Jack Katzfey and John McGregor. Near the end of the workshop the participants

gave PowerPoint presentations (available on request) on their projects, titled as follows:

Model Assessment for the Philippine Region by Hilario, Cinco and Uson.


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Using CCAM Global Prediction as Initial and Boundary Conditions for Regional Modelsby Junnaedhi.

Comparison of Seasonal Winds between Reanalysis Data and CCAM 1971 -2000 by

Halimurrahman.

Climate Change Studies in Indonesia by Siswanto and Juaeni.

Recent CCAM Activity in Indonesia by Linarka, Hanggoro and Fitria.

Climate Change in Vietnam: Output from CCAM by Tan.

Fire Danger Rating System; and Wave Height Simulation by Harsa.

Several participants also gave lectures on meteorological and climate research at their

institutions.

Other activities undertaken during the workshop included a small workshop dinner, and agroup excursion. Good rapport was developed between CAWCR scientists and the attendees.

Figure 17: Photographs of the participants in the 2009 Analysis Workshop taken during lectures,

excursions and workshop dinner.


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Figure 18: Images from the PowerPoint presentation given by Halimurrahman, one of the scientists

attending the 2009 Analysis Workshop at CMAR-Aspendale.

In the presentation by Halimurrahman, comparisons of the 1000 hPa wind field in CCAM

simulations were verified against the NCEP and ERA analyses. A deficiency was noted inthe SON winds south of India.

5. FOLLOW-UP ACTIVITIES

There have been several follow-up activities since the workshop. In July John McGregor

(CMAR) and John McBride (BOM) were invited to attend an International MonsoonSymposium in Bali. In September, the head of research at BMKG, Dr I Putu Pudja was

accompanied by Dr Dodo Gunawan and Mr Wido Hanggoro in visiting CMAR and BOM for

several days. In late November John McGregor visited PAGASA, BMKG, ITB and LAPAN

for several days on his way to a conference in South Africa. Modelling support continues to

be provided to BMKG via email.

6. FUTURE DIRECTIONS

Based upon the work completed in this project and the associated workshop, several future

directions of work have been identified, including:

BMKG (Indonesia) is using CCAM for regional climate modelling (also for seasonal

forecasting and weather prediction).

BMKG is now able to perform its own climate downscaling simulations, to better

inform policy and adaptation decisions.

LAPAN and ITB in Bandung are keen to collaborate on using CCAM to downscale.

Halims presentation

ERA40NCEP/NCAR

WINDS 1000mb SON 1971-2000


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This project has resulted in capacity building for the countries involved, giving their scientiststhe ability to downscale for their own regional purposes. The information generated can thus

be used to support discussions within the country to assist in policy decisions about the best

way to manage resources. Although climate change is only one driver that may affect

development in the region, management of the risk of climate change, including extremeevents, is important to ensure sustainability of the regional economies. By anticipating future

climate risks and necessary adaptations, it will be possible to reduce vulnerability to the

adverse effects of climate change.

The downscaling project transferred knowledge and skills to the scientists involved,

increasing their self sufficiency and their ability to plan. In the future, it is hoped that there

will be continuing participatory research between scientists from CSIRO and the countries in

the region so that the techniques of regional climate modelling can be further developed and

the information generated can be used for decisions about evidence-based aid targeted to

specific needs.


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REFERENCES

Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U.

Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, and P. Arkin, 2003: The Version 2

Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present).J. Hydrometeor., 4,1147-1167.

IPCC, 2007: Climate change 2007: The Physical Science Basis. Contribution of Working

Group I to the Fourth Assessment Report of the Intergovernmental Panel on

Climate Change, Cambridge, United Kingdom.

McGregor, J. L., 2005: C-CAM: Geometric aspects and dynamical formulation. Technical

Report 70, CSIRO Atmospheric Research, 43 pp.

McGregor, J. L., and M. R. Dix, 2008: An updated description of the Conformal-Cubic

Atmospheric Model. In High Resolution Simulation of the Atmosphere and Ocean, eds. K.

Hamilton and W. Ohfuchi, Springer, 51-76.

McGregor, J. L. and K. C. Nguyen, 2008: Dynamical downscaling of coupled model

historical runs. Final report for project 1.5.4, SEACI, 68-82.

http://www.mdbc.gov.au/subs/seaci/docs/reports/SEACIFinalProjectReportsDec07.pdf

McGregor, J. L., and K. C. Nguyen, 2009: Dynamical downscaling from climate change

experiments. Final Report of Project 2.1.5b for the South East Australian Climate Initiative,

21 pp.

McGregor, J. L., K. C. Nguyen, and J. J. Katzfey, 2008a: A variety of tropical simulations

using CCAM. In "High resolution modelling the second CAWCR modelling workshop.The Centre for Australian Weather and Climate Research Tech. Rep. 6, 29-32.

McGregor, J., K. Nguyen, J. Katzfey, and M. Thatcher, 2009: Regional climate modelling

over island countries. Extended abstracts, International Symposium on Equatorial Monsoon

System, Kuta Paradiso Hotel, Bali, 16-18 July 2009, 8 pp.

McGregor, J. L., K. C. Nguyen, and M. Thatcher, 2008b: Regional climate simulation at 20

km using CCAM with a scale-selective digital filter. Research Activities in Atmospheric and

Oceanic Modelling Report No. 38 (ed. J. Cote), WMO/TD, 7-17.

http://collaboration.cmc.ec.gc.ca/science/wgne/index.html

Nakicenovic, N, J. Alcamo, G. D. Bert de Vries, J. Fenhann, S.Gaffin, K. Gregory, A.Grbler, T. Y. Jung, T. Kram, E. L. La Rovere, L. Michaelis, S. Mori, T. Morita, W. Pepper,

H. Pitcher, L. Price, K. Riahi, A. Roehrl, H.-H. Rogner, A. Sankovski, M. Schlesinger, P.

Shukla, S. Smith, R. Swart, S. van Rooijen, N. Victor, Z. Dadi, 1992:Emissions Scenarios for

the IPCC: an Update, Climate Change 1992: The Supplementary Report to The IPCC

Scientific Assessment, Cambridge, United Kingdom.

New, M., M. Hulme and P. Jones, 1999: Representing twentieth-century space-time climate

variability. Part I: Development of a 1961-90 mean monthly terrestrial climatology. J.Climate, 12, 829-856.

Nguyen, K. C., and J. L. McGregor, 2009: Analyses of climate change for South East

Queensland. CSIRO Technical Report, 978-1-921605-11-6 PDF version, 43 pp.
http://www.mdbc.gov.au/subs/seaci/docs/reports/SEACIFinalProjectReportsDec07.pdfhttp://www.mdbc.gov.au/subs/seaci/docs/reports/SEACIFinalProjectReportsDec07.pdfhttp://collaboration.cmc.ec.gc.ca/science/wgne/index.htmlhttp://collaboration.cmc.ec.gc.ca/science/wgne/index.htmlhttp://collaboration.cmc.ec.gc.ca/science/wgne/index.htmlhttp://www.mdbc.gov.au/subs/seaci/docs/reports/SEACIFinalProjectReportsDec07.pdf


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Park, S., M. Howden, T. Booth, C. Stokes, T. Webster, S. Crimp, L. Pearson, S. Attard, T.

Jovanovic, 2009: Assessing the vulnerability of rural livelihoods in the Pacific to climatechange. Prepared for the Australian Government Overseas Aid Program (AusAID). CSIRO

Sustainable Ecosystems, Canberra.

Reynolds, R. W., 1988: A real-time global sea surface temperature analysis.J. Climate, 1, 75-86.

Smith, I., and E. Chandler, 2009: Refining rainfall projections for the Murray Darling Basinof south-east Australia-the effect of sampling model results based on performance, Climatic

Change, in press.

Thatcher, M., and J. L. McGregor, 2009: Using a scale-selective filter for dynamical

downscaling with the conformal cubic atmospheric model.Mon. Wea. Rev., 137, 1742-1752


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APPENDIX A WORKSHOP PARTICIPANTS

The workshop was conducted in the lecture theatre at CMAR Aspendale from 18 to 29 May

2009. The following 14 participants attended:

BMKG (Jakarta):Mr Utoyo Ajie LinarkaMr Wido Hanggoro

Mr Hastuadi Harsa

Ms Welly Fitria

Institute of Technology Bandung (ITB):Prof Tri Wahyu Hadi

Mr I Dewa Junnaedh

Mr Gilang Permana

LAPAN (Space Research Agency in Bandung):Mr Bambang Siswanto

Dr Ina Juaeni

Mr Halimurrahman

University of Hanoi:Prof. Phan Van Tan

PAGASA (Meteorological Service of Philippines):Dr. Flaviana Hilario

Ms Thelma CincoMs Maria Christina Uson


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APPENDIX B WORKSHOP LECTURES AND LECTURERS

Lectures were given during May 2009at 11 am and 2 pm each day, according to the lecturer

schedule below.

Monday, 18 May

John McGregor Regional modelling with CCAMKim Nguyen Preliminary results from the simulations over Indonesia

Tuesday, 19 MayJohn McBride (BOM) Seasonal predictability of monsoon rainfallJack Katzfey CCAM Downscaling for climate and weather

Wednesday, 20 MayDebbie Abbs Dynamical downscaling of tropical cyclones for the North West Prof. Tan (Univ. Hanoi) Overview on weather forecast and climate research in Vietnam

Thursday, 21 May

John McBride (BOM) a) Case studies of heavy rain events in the monsoon tropicsb) Vietnam an interesting monsoon regime

Ian Smith Current issues with climate change projections

Friday, 22 MayDewi Kirono Generating climate projections and impact assessmentsKevin Tory (BOM) Turning winds with height rainfall diagnosticTony Hirst Coupled climate modelling at CSIRO

Monday, 25 MayEva Kowalczyk Modelling land surface in a climate model

Marcus Thatcher An urban canopy model for Australian regional climate and airquality modelling

Tuesday, 26 May

Flaviana Hilario Climate trends in the Philippines(PAGASA, Manila)

Martin Cope Air quality modelling

Wednesday, 27 May

Tri Wahyu Hadi (ITB) From sea-breeze to climate change: Seeking advances inmeteorology in Indonesia

Ian Watterson Probability density functions for temperature and precipitation

change under global warming

Suppiah The Australian monsoon

Thursday, 28 May Preparation of presentations

Friday, 29 MayPeter Hurley TAPM: past, present and future

Talks by participants


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Appendix C - CCAM Documentation

CCAM is a full atmospheric global climate model, based on using a conformal-cubic grid.

The conformal-cubic grid used for the 60 km simulations used here is shown on the frontcover. To allow for downscaling experiments CCAM can be configured to use a stretched

grid by utilising the Schmidt (1977) transformation of the coordinates and dynamical

equations. A stretched grid allows for higher resolution in areas of interest (as in the 60 kmsimulation). CCAM uses a semi-Lagrangian advection scheme and semi-implicit time step

with an extensive set of physical parameterisations: the GFDL parameterisation for long-wave

and short-wave radiation (Lacis and Hansen, 1974; Schwarzkopf and Fels, 1991) are used,

with interactive cloud distributions determined by the liquid and ice-water scheme of

Rotstayn (1997); the model uses a stability-dependent boundary layer scheme based on

Monin-Obukhov similarity theory (McGregor et al., 1993); the canopy scheme described by

Kowalczyk (Kowalczyk, Garratt and Krummel, 1994) is employed with six layers for soil

temperature, six for soil moisture and three layers for snow; and the cumulus convection

scheme with both downdrafts and detrainment, as well mass-flux closure, as described by

McGregor (2003). Simulations using CCAM have also been successfully undertaken over

South Africa (Engelbrecht, McGregor and Engelbrecht, 2009), Fiji (Lal, McGregor and

Nguyen, 2008) and Indonesia.

CCAM References

Engelbrecht, F.A., McGregor, J.L. and Engelbrecht, C.J., 2009: 'Dynamics of the Conformal-

Cubic Atmospheric Model projected climate-change signal over southern Africa',

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ACRONYMS

Conformal Cubic Atmospheric Model .CCAM

Fourth Assessment Report AR4Global Climate Model ..GCMIntergovernmental Panel on Climate Change ...IPCCWorld Climate Research Programme ...WCRP

Coupled Model Intercomparison Project phase 3 .CMIP3National Centre for Environmental Prediction .NCEP

Special Report on Emission Scenarios .SRESSea Surface Temperature ..SST


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