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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
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.
December-January-February
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.
September-October-November
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
GFDL2.1 ECHAM5 HadCM3
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.
December-January-February
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.
September-October-November
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
GFDL2.1 ECHAM5 HadCM3
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.pdf8/8/2019 Regional Climate Projections IndonesianAusAID-Final Report-V7
32/38
32
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',
International Journal of Climatology, vol. 29, 1013-1033.
Kowalczyk, E.A., Garratt, J.R. and Krummel, P.B., 1994: Implementation of a soil-canopyscheme into the CSIRO GCM -regional aspects of the model response, CSIRO Div.
Atmospheric Research Tech. Paper No. 32, 59 pp.
Lacis, A and Hansen, J., 1974: 'A parameterisation of the absorption of solar radiation in the
Earth's atmosphere',Journal of Atmospheric Science, vol. 31, 118-133.
Lal, M., J. L. McGregor, and K. C. Nguyen, 2008: Very high-resolution climate simulation
over Fiji using a global variable-resolution model. Climate Dynamics, 30, 293-305.
McGregor, J., 2003: A new convection scheme using a simple closure. In "Current issues in
the parameterization of convection", BMRC Research Report 93, 33-36.
McGregor, J.L., Gordon, HB, Watterson, IG, Dix, MR and Rotstayn, L..D., 1993: The CSIRO
9- level atmospheric general circulation model, CSIRO Div. Atmospheric Research Tech.
Paper No. 26, 89 pp.
Rotstayn, L.D., 1997: 'A physically based scheme for the treatment of stratiform clouds and
precipitation in large-scale models', Quarterly Journal of the Royal Meteorological Society,
vol. 123, 1227-1282.
Schmidt, F., 1977: 'Variable fine mesh in spectral global model',Beitr. Phys. Atmos., vol. 50,
211-217.
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Schwarzkopf, M.D. and Fels, S.B., 1991: 'The simplified exchange method revisited: An
accurate, rapid method for computation of infrared cooling rates and fluxes', Journal of
Geophysical Research, vol. 96, 9075-9096.
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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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