Introduction Atmospheric greenhouse gas concentrations have increased rapidly in the past century and are almost certain to continue to increase in the future (IPCC 2001a). Global Climate Models (GCMs) are the best available tools for simulating future climates based on various greenhouse gas and aerosol emission scenarios. GCM experiments indicate a global warming of 1.4 to 5.8°C by the year 2100, relative to 1990 (IPCC 2001a). This is likely to be associated with changes to weather patterns, sea-level rise and impacts on ecosystems, water resources, agriculture, forests, fisheries, industries, set- tlements, energy, tourism and health (IPCC 2001b). Changes in climate will not be globally uniform. More warming is likely in polar regions and over Aust. Met. Mag. 56 (2007) 131-152 131 Australian climate change projections derived from simulations performed for the IPCC 4th Assessment Report R. Suppiah, K.J. Hennessy, P.H. Whetton, K. McInnes, I. Macadam, J. Bathols, J. Ricketts and C.M. Page CSIRO Marine and Atmospheric Research, Australia (Manuscript received March 2007; revised August 2007) In this study, we present climate change projections based on the results from 23 climate model simulations performed for the IPCC 4th Assessment Report. Statistical methods are used to test how well each model simulated observed average (1961-1990) patterns of mean sea-level pressure, temperature and rainfall over the Australian region. The 15 models with the highest pattern correlations and smallest rms errors are identified. The 21st century simula- tions are driven by the IPCC ‘SRES’ greenhouse gas and aerosol emission sce- narios. Using the 15 best climate models, annual and seasonal average projec- tions of Australian rainfall and temperature change are derived for various decades. Results are highlighted for 2030 and 2070 for comparison with pro- jections published by CSIRO in 2001. The projections are expressed as ranges, incorporating uncertainty in both global warming and regional differences between climate simulations over Australia. Inland regions show greater warming, compared to coastal regions. There are large decreases in the number of days below 0°C and large increases in the number of days above 35°C or 40°C. Rainfall changes are more complex than temperature changes. Although increases and decreases in rainfall are pro- jected in the future, decreases dominate the overall pattern, especially in the south in winter and spring. CSIRO’s earlier projections, based on nine climate models, appear robust when compared with the updated projections. The patterns and magnitudes of warming are similar, although the updated projections have slightly less warming in coastal regions. The pattern of rainfall change is also similar, par- ticularly the strong decrease in winter and spring over southern Australia, but the updated projections give a more widespread tendency for increases in sum- mer in eastern Australia and a clearer tendency for decreases in autumn in Queensland and the eastern Northern Territory. Corresponding author address: Dr Ramasamy Suppiah, CSIRO Marine and Atmospheric Research, PMB No. 1, Aspendale, Vic. 3195, Australia. Email: [email protected]
Transcript
IntroductionAtmospheric greenhouse gas concentrations haveincreased rapidly in the past century and are almostcertain to continue to increase in the future (IPCC2001a). Global Climate Models (GCMs) are the bestavailable tools for simulating future climates based on
various greenhouse gas and aerosol emission scenarios.GCM experiments indicate a global warming of 1.4 to5.8°C by the year 2100, relative to 1990 (IPCC 2001a).This is likely to be associated with changes to weatherpatterns, sea-level rise and impacts on ecosystems, waterresources, agriculture, forests, fisheries, industries, set-tlements, energy, tourism and health (IPCC 2001b).Changes in climate will not be globally uniform.
More warming is likely in polar regions and over
Aust. Met. Mag. 56 (2007) 131-152
131
Australian climate change projectionsderived from simulations performed for
the IPCC 4th Assessment ReportR. Suppiah, K.J. Hennessy, P.H. Whetton, K. McInnes, I. Macadam, J. Bathols,
J. Ricketts and C.M. PageCSIRO Marine and Atmospheric Research, Australia
(Manuscript received March 2007; revised August 2007)In this study, we present climate change projections based on the results from23 climate model simulations performed for the IPCC 4th Assessment Report.Statistical methods are used to test how well each model simulated observedaverage (1961-1990) patterns of mean sea-level pressure, temperature andrainfall over the Australian region. The 15 models with the highest patterncorrelations and smallest rms errors are identified. The 21st century simula-tions are driven by the IPCC ‘SRES’ greenhouse gas and aerosol emission sce-narios. Using the 15 best climate models, annual and seasonal average projec-tions of Australian rainfall and temperature change are derived for variousdecades. Results are highlighted for 2030 and 2070 for comparison with pro-jections published by CSIRO in 2001. The projections are expressed as ranges,incorporating uncertainty in both global warming and regional differencesbetween climate simulations over Australia.Inland regions show greater warming, compared to coastal regions. There
are large decreases in the number of days below 0°C and large increases in thenumber of days above 35°C or 40°C. Rainfall changes are more complex thantemperature changes. Although increases and decreases in rainfall are pro-jected in the future, decreases dominate the overall pattern, especially in thesouth in winter and spring.CSIRO’s earlier projections, based on nine climate models, appear robust
when compared with the updated projections. The patterns and magnitudes ofwarming are similar, although the updated projections have slightly lesswarming in coastal regions. The pattern of rainfall change is also similar, par-ticularly the strong decrease in winter and spring over southern Australia, butthe updated projections give a more widespread tendency for increases in sum-mer in eastern Australia and a clearer tendency for decreases in autumn inQueensland and the eastern Northern Territory.
Corresponding author address: Dr Ramasamy Suppiah, CSIROMarine and Atmospheric Research, PMB No. 1, Aspendale, Vic.3195, Australia.Email: [email protected]
land, compared with the tropics and oceans (IPCC2001a). Climate change projections have been usedto assess the impacts of climate change for the trop-ics (Hulme and Viner 1998), South America (Carrilet al. 1997), Europe (Smith and Pitts 1997), WesternUnited States (Leung et al. 2004) and other regions.Most projections are derived from multiple modelresults, using simple averages or weighted valuesbased on statistical measures of model reliability,such as the correlation between observed and simu-lated climate patterns (Tebaldi et al. 2005). Theunderlying assumption is that projections are likelyto be more reliable from models that simulate thepresent climate well. Some studies have attempted toincorporate other factors, such as interannual vari-ability (Giorgi 2005).Over the past decade, CSIRO has produced cli-
mate change projections for Australia (CSIRO 1996,2001) and its various States and Territories (Whettonet al. 2005). Here, we present new Australian climatechange projections based on simulations performedfor the IPCC 4thAssessment Report (AR4), involving23 GCMs. Although the name of the database hasbeen changed recently to the WCRP CMIP3 multi-model database from IPCC 4th Assessment ModelOutput database, we decided to maintain the previousname for consistency in this paper. The projectionsprovide estimates of changes in temperature and rain-fall in Australia from 2020 to 2080. The purpose ofthese projections is to raise awareness, provide inputto risk assessments, and underpin the development ofmitigation and adaptation strategies.
Climate model dataMonthly data from 23 GCMs were obtained from theIPCC Model Output website at http://www-pcmdi.llnl.gov/ipcc/info_for_analysts.php. Some ofthe models have single simulations for the 20th and21st centuries, while some models have multiple sim-ulations. For models with multiple simulations, wehave computed ensemble-mean changes in climate.The simulations of the 20th century climate were dri-ven by observed changes in greenhouse gases andaerosols. Some simulations included direct and indi-rect effects of aerosols, some included ozone deple-tion, and some included volcanic aerosols and solarforcing. Radiative forcing is not directly observed andparticularly uncertain for aerosol. Table 1 gives infor-mation about the various forcings used in each simu-lation and the resolution of each model. The 21st cen-tury simulations were driven by the Special Report onEmissions Scenarios (SRES) A2 and A1B emissionscenarios (SRES 2000).
Assessment of model reliabilityThe simulated globally averaged temperature fromIPCC AR4 models over the 20th century agrees wellwith observations, showing an increase from the1920s to 1940s, a slight decrease from the 1940s to1970s, then a rapid increase (Easterling et al. 2006;IPCC 2007). An assessment of the ability of individualmodels to simulate the observed global warming hasnot been undertaken for this study, and is not consid-ered as important as assessing the ability of models tosimulate observed regional climate patterns. The relia-bility of climate models in the Australian region hasbeen tested by comparing observed and simulated pat-terns of average temperature, precipitation and meansea-level pressure (MSLP). Gridded observed temper-ature and rainfall data were obtained from the NationalClimate Centre of the Australian Bureau ofMeteorology and gridded MSLP data were obtainedfrom the National Centers for EnvironmentalPrediction (NCEP) re-analysis (Kalnay et al. 1996).Climatological averages for the MSLP for a 30-yearperiod (1961-1990) for the four seasons were obtainedfrom Collier (2004). Statistical methods were used totest whether the models adequately reproduce each ofthese fields in the Australian region (110-155ºE, 11-45ºS). A pattern correlation coefficient of 1.0 indicatesa perfect match between the observed and simulatedspatial pattern, and a root mean square (rms) error of0.0 indicates a perfect match between the observedand simulated magnitudes.Since models have different horizontal grids,
observed and model average seasonal MSLP, temper-ature and rainfall data were interpolated on to a com-mon grid (0.5°×0.5°) before calculating pattern corre-lation and rms error statistics. The rms error is basedon all grid-points in the considered domain, and iscalculated in the units hPa, °C and mm for seasonalMSLP, temperature and rainfall, respectively. Rootmean square error for MSLP and temperature arebased on seasonal values and shown as hPa and °C inFigs 2 and 4. However, for rainfall, seasonal total rmserror values were divided by the number of days inthe season and expressed as mm per day in Fig. 6.Realistic simulations of MSLP patterns are impor-
tant as they are implicitly linked to atmospheric cir-culation (i.e. wind, moisture, etc.) patterns. Figure 1shows observed average MSLP from 1961-1990 overthe Australian region for summer (DJF), autumn(MAM), winter (JJA) and spring (SON). Duringspring, a low pressure area develops over northwest-ern Australia centred on the Pilbara region, creating astrong pressure gradient between the tropics and highlatitudes. This spatial pattern is strengthened in sum-mer, before weakening below its spring strength in
132 Australian Meteorological Magazine 56:3 September 2007
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 133
Table 1. The 23 simulations of 20th century climate available from PCMDI http://www-pcmdi.llnl.gov/ipcc/info_for_analysts.php. The 15 models that best reproduced Australian average (1961-1990) patterns of tem-perature, rainfall and MSLP are shown in bold letters.
Originating group(s), country Model Horizontal Simulated data Forcings used inresolution (in degrees) used in slope model simulations
and the number of analysisvertical levels (L))
Beijing Climate Center, China BCC 1.9×1.9 L16 1871-2100 G, SDBjerknes Centre for Climate
Research, Norway BCCR 1.9×1.9 L31 1850-2099 G, SDCanadian Climate Centre, Canada CCCMAT47 3.8×3.8 L31 1850-2100 G, SDCanadian Climate Centre, Canada CCCMAT63 2.8×2.8 L31 1850-2100 NOTAVAILABLEMeteo-France, France CNRM 2.8×2.8 L45 1860-2099 G, O, SD, BCCSIRO, Australia CSIRO-MARK3 1.9×1.9 L18 1871-2100 G, O, SDGeophysical Fluid Dynamics Lab,
USA GFDL 2.0 2.5×2.0 L24 1861-2100 G, O, SD, BC, OC,LU, SO, V
Studies, USA GISS-AOM 4.0×3.0 L12 1850-2100 G, SD, SSNASA/Goddard Institute for Space
Studies, USA GISS-E-H 4.0×5.0 L15 1880-2099 G, O, SD, SI, BC, OC,MD, SS, LU, SO, V
NASA/Goddard Institute for SpaceStudies, USA GISS-E-R 4.0×5.0 L15 1880-2100 G, O, SD, SI, BC, OC,
MD, SS, LU, SO, VLASG/Institute of Atmospheric
Physics, China IAP 2.8×2.8 L26 1850-2100 G, SDInstitute of Numerical Mathematics,
Russia INMCM 5.0×4.0 L21 1871-2100 G, SD, SOInstitut Pierre Simon Laplace, France IPSL 2.5×3.75 L19 1860-2100 G, SD, SICentre for Climate Research, Japan MIROC-H 1.12×1.12 L56 1900-2100 G, O, SD, BC, OC, MD,
SS, LU, SO, VCentre for Climate Research, Japan MIROC-M 2.8×2.8 L20 1850-2100 G, O, SD, BC, OC, MD,
SS, LU, SO, VMeteorological Institute of the
University of Bonn,Meteorological Research Instituteof KMA, Germany/Korea MIUB 3.8×3.8 L19 1860-2100 G, SD, SI
Max Planck Institute forMeteorology DKRZ, Germany MPI-ECHAM5 1.9×1.9 L31 1860-2100 G, O, SD, SI
Meteorological Research Institute,Japan MRI 2.8×2.8 L30 1851-2100 G, SD, SO
National Center for AtmosphericResearch, USA NCAR-CCSM 1.4×1.4 L26 1870-2099 G, O, SD, BC, OC, SO, U
National Center for AtmosphericResearch, USA NCAR-PCM1 2.8×2.8 L26 1890-2099 G, O, SD, SO, V
Hadley Centre, UK HADCM3 3.75×2.5 L19 1860-2099 G, O, SD, SIHadley Centre, UK HADGEM1 1.9×1.25 L38 1860-2098 G, O, SD, SI, BC,
autumn. In winter, the subtropical circulation domi-nates over the continent with a region of high pres-sure over southern Australia. Figure 2 shows the pat-tern correlations and rms errors between the observedand simulated MSLP for each season. The better-per-forming models lie closer to the top left corner ofeach panel, with strong pattern correlations and smallrms errors. Most of the models capture large-scalecirculation features since their pattern correlationsare strong (greater than 0.8) during DJF, MAM andJJA. However, model performance is more variableduring spring. Models such as IPSL, BCC, MIROC-H and GISS-E-H perform poorly over Australia inmost seasons.Figure 3 shows observed spatial patterns of sea-
sonal average temperature from 1961-1990 over theAustralian region. The north-south temperature gradi-ent is clearly evident, along with lower temperaturesin the southeast associated with high elevation.Figure 4 shows that most of the models simulate theobserved spatial pattern of temperature over Australiavery well, yielding a pattern correlation of at least 0.9in all seasons. However, the rms error exceeds 2°C forsome models.Observed patterns of average 1961-1990 seasonal
rainfall are shown in Fig. 5. Summer shows a strongnorth-south gradient, dominated by the Australianmonsoon, with high rainfall along the east coast.Slightly weaker gradients are evident in autumn, andweaker still in spring. The winter pattern is almostthe opposite of that in summer, with most of centraland northern Australia being relatively dry, and sig-nificant rainfall in the south due to cold fronts andlow pressure systems. Models that capture thesestrong spatial gradients have high pattern correla-tions. Figure 6 depicts the pattern correlations andrms errors for observed and simulated rainfall.Pattern correlations are strong (greater than 0.8) dur-ing summer, except for four models. A cluster ofmodels shows strong correlations during autumn,although the size of this cluster is smaller than insummer. Correlations are moderate (0.5-0.8) for mostmodels in winter and spring.Deciding on what is acceptable performance is
not straightforward. A good performance at simulat-ing current climate does not guarantee that theenhanced greenhouse simulation is accurate. Nor doerrors in the current climate performance mean thatthe enhanced greenhouse simulated changes in cli-mate are unreliable. This means that focusing on theresults of the very best performing models maytherefore inadequately represent the underlyinguncertainty in projecting regional climate change.Thus, our approach to validation has been to view amodel as acceptable unless the current climate errors
134 Australian Meteorological Magazine 56:3 September 2007
Fig. 1 Observed average seasonal mean sea-levelpressure from 1961-1990 over the Australianregion. Source: NCEP Re-analysis.
Fig. 2 Pattern correlation and rms error betweenobserved and simulated MSLP. Details formodels are given in Table 1. Horizontal andvertical lines indicate threshold values for thedemerit point system for pattern correlationand rms error.
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 135
Fig. 3 Observed average seasonal temperature from1961-1990 over the Australian region. Source:Australian Bureau of Meteorology.
Fig. 4 Pattern correlation and rms error betweenobserved and simulated temperature. Detailsfor models are given in Table 1. Horizontal andvertical lines indicate threshold values for thedemerit point system for pattern correlationand rms error.
Fig. 5 Average seasonal mean rainfall over theAustralian region. Source: Australian Bureauof Meteorology.
Fig. 6 Pattern correlation and rms error betweenobserved and simulated rainfall. Details formodels are given in Table 1. Horizontal andvertical lines indicate threshold values for thedemerit point system for pattern correlationand rms error.
are of a nature which, in our judgment, significantlyreduce the likelihood that the enhanced greenhousesimulation is reliable. Absence of key climate fea-tures (e.g. pressure belts) in the region of interestwould be an example of an unacceptable failure. Therepresentation of model processes is important forjudging the reliability of enhanced greenhousechanges, and for this reason we have placed empha-sis on multivariable assessment, and on spatial pat-terns. Visual examination of model output maps isused in conjunction with statistical tests to judgethresholds of unacceptability on the statistical tests.This approach is used here to form an assessment ofthe acceptability of the 23 simulations under consid-eration. The exercise of model validation shouldtherefore be seen as necessary but not sufficient toensure the reliability of projected changes in climatesimulated by the various models.To select the GCMs that perform ‘best’, we have
used a subjective demerit point system (Whetton et al.
2005): one point for an rms error greater than two or apattern correlation less than 0.8 in any given season.Anextra demerit point was allocated to models with anrms error greater than four or a pattern correlationbelow 0.6. Models were considered acceptable if theyhad less than eight demerit points across all seasons,for MSLP, temperature and rainfall. Consequently, fif-teen models were judged to have satisfactory perfor-mance over the Australian region. It is clear from Figs2, 4 and 6 and Table 2 that the demerit point systemeffectively gives more weight to rms error of MSLP asthe threshold of 2 hPa is a challenging one for modelsto meet. However, the demerit point contribution ofrms error of MSLP does not affect the overall rankingand the selection of the models. This is because thosemodels selected on the basis of the demerit point sys-tem perform well in simulating the pressure patternsand perform well in simulating temperature and rainfallpatterns over the Australian region. These models areshown by bold letters in Tables 1 and 2.
136 Australian Meteorological Magazine 56:3 September 2007
Table 2. Demerit points based on comparison of observed and simulated MSLP, temperature and rainfall: one point foran rms error greater than 2 or a pattern correlation less than 0.8 in any given season. An extra demerit pointwas allocated to models with an rms error greater than 4 or a pattern correlation below 0.6. The 15 models withless than eight demerit points are shown by bold letters.
Originating group(s) Model Mean sea-level Temperature Rainfall Totalpressure including
extrademeritpoints
Beijing Climate Center BCC 9 3 5 17Bjerknes Centre for Climate Research BCCR 0 0 5 5Canadian Climate Centre CCCMAT47 6 1 1 8Canadian Climate Centre CCCMAT63 5 4 1 10Meteo-France CNRM 2 1 1 4CSIRO CSIRO-MARK3 3 2 2 7Geophysical Fluid Dynamics Lab GFDL 2.0 4 0 0 4Geophysical Fluid Dynamics Lab GFDL 2.1 0 1 1 2NASA/Goddard Institute for Space Studies GISS-AOM 3 4 1 8NASA/Goddard Institute for Space Studies GISS-E-H 5 4 5 14NASA/Goddard Institute for Space Studies GISS-E-R 4 3 1 8LASG/Institute of Atmospheric Physics IAP 0 0 2 2Institute of Numerical Mathematics INMCM 1 3 3 7Institut Pierre Simon Laplace IPSL 9 1 4 14Centre for Climate Research MIROC-H 5 0 2 7Centre for Climate Research MIROC-M 6 0 1 7Meteorological Institute of the Universityof Bonn, Meteorological ResearchInstitute of KMA MIUB 1 2 1 4Max Planck Institute for MeteorologyDKRZ MPI-ECHAM5 1 0 0 1Meteorological Research Institute MRI 1 1 1 3National Center for Atmospheric Research NCAR-CCSM 1 0 1 2National Center for Atmospheric Research NCAR-PCM1 3 3 5 11Hadley Centre HADCM3 4 0 2 6Hadley Centre HADGEM1 0 2 0 2
Construction of regional climatechange projectionsIn this paper the term ‘emission scenarios’ refers toIPCC SRES emission scenarios and the term ‘globalwarming scenarios’ refers to increases in temperatureas a result of various SRES emission scenarios.Climate change projections are often based on timeslices within a given climate simulation, e.g. a thirty-year period centred on 2030, 2050 and so on. A dis-advantage with this approach is that large portions ofthe simulation are not used and the projections arelimited to the subset of SRES emission scenarios usedin the simulations, e.g. B1, A1B and A2. However,analysis of temperature and rainfall simulations hasshown that the patterns of change tend to scale linear-ly with global warming, for a range of emission sce-narios (Whetton et al. 2005). This property has beenexploited in a way that avoids disadvantages associat-ed with time slices. The alternative method linearlyregresses simulated 20th and 21st century grid-pointseasonal mean temperature (or rainfall) time seriesagainst simulated global mean annual temperaturetime series. For example, output of the CSIRO-MARK3 model is available for the 230 year 1871-2100 period (see Table 1). For this model, for eachgrid-point, 230 values of seasonal mean temperature(or rainfall) are regressed against 230 values of glob-al mean annual temperature. The gradient of the lineof best fit for each grid-point constitutes the modelresponse, per degree of global warming. The regres-sion analysis is of absolute values of temperature andrainfall totals and absolute values of global mean tem-perature, rather than of changes in these quantities rel-ative to the current climate, and the intercept of thebest fit line is allowed to vary from zero. Linearregression as a means of pattern extraction is advan-tageous in that it decouples the model’s response fromthe particular emissions scenario used in the simula-tion. It allows the patterns to be rescaled by globalwarming values for a broader range of emission sce-narios. Figure 7 shows model-to-model variations inpatterns of simulated rainfall change, per degree ofglobal warming. Models such as CCCMA T47,CCCMA T63 and MIUB show strong increases inrainfall, while GISS AOM, IAP and GFDL 2.1 showstrong decreases.
Combination of the regional information with theglobal warming projectionsThe patterns of regional change per degree of globalwarming can be rescaled by a given amount of globalwarming to produce a pattern of change that wouldapply for a given future date and global warming sce-nario. Changes in Australia's climate derived from
scaled patterns of change and those derived from timeslices of model simulations are compared in Fig. 8.Changes, relative to averages for a current climate(1975-2004) time slice, in annual average temperatureand annual total precipitation are shown for 2010-2039 and 2070-2099 time slices. For each climatevariable and future time slice, pattern-scaled changesand time-slice average changes for each of the fifteenmodels shown by bold letters in Tables 1 and 2 wereinterpolated to a common, 4°x4° grid. This griddeddata yielded two data-sets of 930 changes, each com-prising a change for each of the fifteen models foreach of 62 4°x4° grid boxes covering Australia. Thedata-set of pattern-scaled changes was linearlyregressed against the data-set of time-slice averagechanges, as illustrated in Fig. 8.Figure 8 also shows linear relationships between pat-
tern-scaled and time-slice average changes, supportingthe hypothesis that patterns of change in average tem-
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 137
Fig. 7 Annual rainfall change (%) per degree ofglobal warming (PDGW) for 22 of the 23 AR4climate simulations. The 15 models used toconstruct climate change projections areshown by tick marks. Temperature data forBCC are incomplete and therefore not shownin this figure.
perature and precipitation tend to scale linearly withglobal warming. There is close agreement between pat-tern-scaled and time-slice average changes in annualaverage temperature for both the 2010-2039 and 2070-2099 time slices (see Figs 8(a) and 8(b)). The agreementis closer for the 2070-2099 time slice than for the 2010-2039 time slice. This is likely to be due to a greaterinfluence of climate variability relative to longer termglobal climate change on the simulated climate ofAustralia in the early part of the 21st century than thelater part. Time-slice average changes are likely to bemore sensitive to climate variability than pattern-scaledchanges. Time-slice average changes in precipitationexhibit more sensitivity than time-slice average changesin temperature, resulting in a poorer agreement betweenpattern-scaled and time-slice average changes in precip-itation, especially for the 2010-2039 time slice (see Figs8(c) and 8(d)). Similarly, time-slice average changes inseasonal average temperatures and seasonal precipita-
tion totals are more sensitive to climate variability thantime-slice average changes in annual average tempera-ture and annual total precipitation respectively. Hence,for seasonal average temperatures and seasonal precip-itation totals the linear relationships between pattern-scaled and time-slice average changes (not shown) areweaker than for annual average temperature and annualtotal precipitation respectively.Hence, projections can be evaluated for emissions
scenarios that have not been directly simulated inGCM experiments (Whetton et al. 2005). This patternscaling method has been used by CSIRO since 2001(Whetton et al. 2001) and is considered robust(Mitchell 2003). Pattern scaling has been used in cli-mate projections for many regions (CSIRO 1996,2001; Rotmans et al. 1994; Hulme et al. 1996; Kennyet al. 1995; Hulme, 1999).To derive regional projections for the years 2030 and
2070, the ranges of change per degree of global warm-ing are combined with the IPCC (2001a) global warm-ing projections for 2030 and 2070. For example, thehigh scenario for the regional warming range in 2030would be the upper limit of the relevant range of region-al warming per degree of global warming multiplied bythe upper limit of the global warming range for 2030.Correspondingly, the low scenario for the regionalwarming range is based on the combination of the lowerend of the regional warming per degree of global warm-ing multiplied by the lower end of the global warmingrange. (The approach is the same for precipitationchange; however, where the lower limit of the range ofper cent rainfall change is negative, this is combinedwith the upper limit of the projected global warming.)The global warming projections are shown in Fig.
9, for both SRES and CO2 stabilisation scenarios. Forthe SRES scenarios, the range of global warming for1990-2100 allows for two sources of uncertainty: (a)the full range of IPCC greenhouse gas and sulfateaerosol emission scenarios derived from the SpecialReport on Emission Scenarios (SRES 2000); and (b)the full range of ‘climate sensitivity’, i.e. the globalmean warming for a doubling of CO2 from 280 partsper million (ppm) to 560 ppm. The combination ofthese uncertainties is given in the IPCC (2001a) glob-al warming values, namely 0.54 to 1.24°C by 2030and 1.17 to 3.77°C by 2070, relative to 1990. Updatedglobal warming values for 2030 and 2070 were notavailable from IPCC (2007).Two CO2 stabilisation scenarios are considered:
stabilisation at 450 ppm by the year 2100 and stabili-sation at 550 ppm by the year 2150. For the 450 ppmscenario, the global warming is 0.52 to 0.89°C by2030 and 1.11 to 1.95°C by 2070. For the 550 ppmscenario, the global warming is 0.57 to 0.96°C by2030 and 1.36 to 2.36°C by 2070.
138 Australian Meteorological Magazine 56:3 September 2007
Fig. 8 Comparisons of changes in Australia's climatederived from scaled patterns of change andthose derived from time slices of model simu-lations. Changes, relative to averages for acurrent climate (1975-2004) time slice, areshown for: (a) annual average temperature for2010-2039; (b) annual average temperaturefor 2070-2099; (c) annual total precipitationfor 2010-2039; and (d) annual total precipita-tion for 2070-2099. Each point representschanges for a particular climate model for aparticular 4°x4° grid box over Australia.Linear regression best fit lines of changes frompattern-scaled changes versus time-slice aver-age changes are shown.
Regional climate change projectionsfor AustraliaRegional projections for Australia are described in thenext two sections. The first section is based on resultsaveraged over the fifteen climate models to give asimple representation of patterns of change. The nextsection is more comprehensive because it includesuncertainty due to differences between the fifteen cli-
mate simulations. Spatial changes are presented ascontour maps for 2030 and 2070 for low and highglobal warming scenarios, relative to 1990. Theserepresent changes in climatic conditions averagedover several decades centred on 2030 and 2070. Theconditions of any individual year will be stronglyaffected by interannual and interdecadal natural cli-matic variability and cannot be predicted.
Projections based on average model responsesProjections presented in this section are based onaverage responses to global warming across the bestfifteen climate models, for the SRES scenarios.Uncertainty due to different levels of global warmingis quantified, but uncertainty due to differencesbetween models is excluded in the averaging process.Projections that include uncertainty due to differencesbetween models, and the effect of CO2 stabilisationscenarios, are given in the next section.
Temperature. Average regional warming patternsfor low, mid and high global warming scenarios for2030 are shown in Fig. 10. For low global warming(0.54°C by 2030 and 1.17°C by 2070), there areincreases of up to 0.5°C in southern and easterncoastal areas and between 0.5 and 1.0°C in inlandregions. For mid global warming (0.89°C by 2030and 2.47°C by 2070) there are increases of 0.5 to1.0°C in coastal areas and 1.0 to 1.5°C over inlandregions. For high global warming (1.24°C by 2030and 3.77°C by 2070) there are increases of 0.5 to1.0°C in Tasmania, 1.0 to 1.5°C within 200-1000 km
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 139
Fig. 9 Range (low to high) of global-average warm-ing relative to 1990 based on the SRES emis-sion scenarios (IPCC, 2001a), and 450 ppmand 550 ppm CO2 stabilisation scenarios(Wigley et al. 1996).
Fig. 10 Average temperature changes for 2030 relative to 1990, from 15 models using (a) low, (b) mid and (c) high glob-al warming scenarios.
(a) (b) (c)
of the mainland coast, and 1.5 to 2.0°C over centralparts of Western Australia, Northern Territory andQueensland. Slightly more extensive warming issimulated in spring.Average regional warming patterns for low, mid
and high global warming scenarios for 2070 are shownin Fig. 11. Although the patterns of warming are simi-lar to those in Fig. 10, greater increases are simulatedfor 2070. For the low scenario, areas of 1.0°C to 1.5°Cincreases are widespread, with 1.5 to 2.0°C in areasnorth of latitude 30°S and more than 200 km inland,and 0.5 to 1.0°C over Tasmania. For the mid scenario,there are increases of 2.0 to 3.0°C over most regions,rising to 3.0 to 4.0°C in areas north of latitude 30°Sand more than 200 km inland. For the high scenario,there are increases of 3.0 to 4.0°C within 200 km ofthe coast, and 4.0 to 5.0°C over most of the rest of thecontinent, exceeding 5.0°C in the northwest.
Rainfall. Corresponding average percentage changesin rainfall for low, mid and high scenarios by 2030 areshown in Fig. 12. For the low scenario, annual-aver-age rainfall decreases by 0 to 5% over most of thecountry, except for two small regions in easternTasmania and near Darwin which show increases of 0to 5%. In summer, increases of 0 to 5% are simulatedover the eastern half of Australia, with decreases of 0to 5% elsewhere. In autumn increases of 0 to 5%extend in a band from the northwest to New SouthWales, but most coastal regions have decreases of 0 to5%. In winter and spring, decreases of 5 to 10% aresimulated over the southwest with decreases of 0 to5% elsewhere. For the high scenario, annual-average
decreases of 0 to 5% are widespread, with decreasesof 5 to 10% along the south and west coasts, and adecrease of 10 to 20% in the southwest corner nearPerth. In summer and autumn, the ranges of changeare very similar to those for the low case, but thedecreases are slightly larger in the southwest andTasmania, and the increases are slightly larger in NewSouth Wales. In winter, decreases of 10 to 20% occurin most of Western Australia and inland parts of theNorthern Territory and Queensland, with decreases of5 to 10% elsewhere, except for a 0 to 5% decrease innorthern Northern Territory and Queensland and anincrease of 0 to 5% in Tasmania. In spring, decreasesof 5 to 10% are widespread, with decreases of 0 to 5%across the north and along the New South Wales coastand Tasmania, and decreases of 10 to 20% withinabout 500 km of the west coast. The mid scenario isthe average of the low and high scenarios.Changes in rainfall for high, mid and low scenar-
ios for 2070 are shown in Fig. 13. The low 2070 sce-nario is very similar to the high 2030 scenario.However, for the high 2070 scenario, annual-averagedecreases of 0 to 5% occur in a band from the northcoast to New South Wales, with decreases of 5 to10% along the Queensland coast and in northernVictoria, western South Australia, the south of theNorthern Territory and southeastern WesternAustralia. Decreases of 10 to 20% occur along thesouth coast, with decreases of 30 to 40% in thesouthwest corner. In summer, increases of 0 to 5%cover much of the Northern Territory, Queenslandand South Australia, with increases of 10 to 20% inNew South Wales, decreases of 0 to 5% along the
140 Australian Meteorological Magazine 56:3 September 2007
Fig. 11 Average temperature changes for 2070 relative to 1990, from 15 models using (a) low, (b) mid and (c) high glob-al warming scenarios.
(a) (b) (c)
Queensland coast and in eastern Western Australia,and decreases of 10 to 20% in southern Victoria,Tasmania and the west coast. In autumn increases of0 to 5% extend in a band from the northwest to NewSouth Wales, but most coastal regions have decreas-es of 5 to 10%. In winter, decreases of 30 to 40%occur in most of Western Australia and inland partsof the Northern Territory and Queensland, withdecreases of 20 to 30% elsewhere, except for a 0 to5% decrease in northern Northern Territory and
coastal Queensland and an increase of 5 to 10% inTasmania and Cape York Peninsula. In spring,decreases of 10 to 20% are widespread, except fordecreases of 5 to 10% in the northwest andTasmania, decreases of 20 to 30% in Victoria, south-ern South Australia and the southern Queenslandcoast, and decreases of 30 to 40% within about 500km of the west coast. The mid scenario is the aver-age of the low and high scenarios, which is close tothe A2 emission scenario.
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 141
Fig. 12 Average percentage rainfall changes for 2030, relative to 1990, from 15 models using (a) low, (b) mid and (c)high global warming scenarios.
Fig. 13 Average percentage rainfall changes for 2070, relative to 1990, from 15 models using (a) low, (b) mid and (c)high global warming scenarios.
(a) (b) (c)
(a) (b) (c)
Projections based on ranges of model responsesThe multi-model average changes presented in Figs10 to 13 do not provide information about uncertain-ty due to differences between the fifteen climate sim-ulations (see Fig. 7). Hence, the results and discussionabove gave information about the central tendency ofregional climate change, but did not consider the fullrange of change based on some of the more extremeclimate change simulations. In this section, we quan-tify the effect of differences between climate modelsand produce a single map representing the full rangeof possible change for a given variable. This analysisalso gives an indication of how representative thecentral tendency given in the previous section is of thefull range of model results.A common grid (4.0 degree) is chosen and models
are interpolated to this grid. The grid spacing is typi-cally about 400 km. Rather than averaging simulatedchanges from all fifteen models at each grid box, werank the changes from the highest (1) to lowest (15) ateach grid box. To reduce the influence of outlierresults, a range is formed from the second-highest andsecond-lowest change per degree of global warmingat each grid-point. The range obviously variesbetween grid-points, so we start with 36 pre-selectedranges that can be plotted as coloured boxes on a map,
e.g., 0-5%, 0-10%, 0-15%, 5-10%, 5-15%, etc. Inmost cases, only 5 to 10 of the pre-selected ranges areneeded for a given map. More detail is given inWhetton et al. (2005). These ranges are then scaled bythe low, mid and high global warming valuesdescribed above (which include uncertainty due todifferent emission scenarios and climate sensitivity).
Average annual and seasonal temperature. Figure14 shows the projected range of annual and seasonalwarming for 2030 and 2070, relative to 1990. Table 3shows changes in temperature for each decadebetween 2020 and 2080, corresponding to Fig. 14.Projections for 2030 and 2070 are discussed in detailto facilitate comparison with the CSIRO (2001) pro-jections, which were based on nine models, namelyCSIRO Mark2, DARLAM 125, CCM1, ECHAM4,ECHAM5, GFDL, NCAR PCM, HADCM2 andHADCM3. The comparison with CSIRO’s (2001)projections is described in the Discussion section.In 2030, for the SRES scenarios, an annual-aver-
age warming of 0.2 to 1.5°C occurs within 200 km ofthe coast, 0.5 to 1.9°C more than 200 km from thecoast, and 0.5 to 2.2°C along inland parts of theTropic of Capricorn. Although the patterns of season-al warming are broadly similar to that of the annual
142 Australian Meteorological Magazine 56:3 September 2007
Fig. 14 The 15-model range of projected annual and seasonal warming for 2030 and 2070, relative to 1990, for (a)SRES emission scenarios, (b) a scenario that stabilises the CO2 concentration at 550 ppm by the year 2150(WRE 550 ppm) and (c) a scenario that stabilises the CO2 concentration at 450 ppm by 2100 (WRE450 ppm).Map colours apply to all three scenarios, and the boxes show which range corresponds to each colour for eachscenario. See Table 3 for ranges of change from 2020 to 2080.
warming, the magnitude of the warming varies amongthe seasons. Greatest warming occurs in spring, andleast warming in winter. During spring, a warming of0.2 to 1.5°C occurs within 200 km of the south andeast coasts, 0.5 to 1.9°C within 200 km of the north-west coast, and 0.5 to 2.2°C almost everywhere else.In winter, a warming of 0.2 to 1.5°C occurs within200 km of the east coast and most of the southern halfof Australia, and 0.5 to 1.9°C elsewhere.In 2070, for the SRES scenarios, an annual-aver-
age warming of 0.4 to 4.5°C occurs within 200 km ofthe coast, 1.1 to 5.7°C beyond 200 km from the coast,and 1.1 to 6.8°C along inland parts of the Tropic ofCapricorn. During spring, a warming of 0.4 to 4.5°Coccurs within 200 km of the south and east coasts, 1.1to 5.7°C within 200 km of the northwest coast, and1.1 to 6.7°C almost everywhere else. In winter, a
warming of 0.4 to 4.5°C occurs within 200 km of theeast coast and most of the southern half of Australia,and 1.1 to 5.7°C elsewhere.If CO2 concentrations are stabilised at 550 ppm by
the year 2150, the upper limit of warming is reducedby 23% by 2030 and 38% by 2070. For example, in2030, the annual-average warming becomes 0.2 to1.2°C within 200 km of the coast, 0.5 to 1.5°C beyond200 km from the coast, and 0.5 to 1.7°C along inlandparts of the Tropic of Capricorn. If CO2 concentra-tions are stabilised at 450 ppm by the year 2100, theupper limit of warming is reduced by 25% by 2030and 48% by 2070.
Extreme daily temperature. Changes in averagetemperature can have a large impact on extreme dailytemperatures. From 1910 to 2004, the Australian-
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 143
Table 3. Projected changes in temperature (°C) forAustralia for 2020 to 2080 based on (a) the SRES (2000) emission sce-narios, (b) CO2 concentration stabilised at 550 ppm (WRE 550) by the year 2150, and (c) CO2 concentrationstabilised at 450 ppm (WRE 450) by the year 2100. The colours in the legend apply to regions in Fig. 14.
(a)
SRESLegend 2020 2030 2040 2050 2060 2070 2080
low high low high low high low high low high low high low high
average maximum temperature rose 0.6°C and theminimum temperature rose 1.2°C, mostly since 1950(Nicholls and Collins 2006). It is very likely thatincreases in greenhouse gases and aerosols have sig-nificantly contributed to this warming in the secondhalf of the 20th century (Karoly and Braganza 2005a,2005b). From 1957 to 2004, the Australian-averageshows an increase in hot days (35°C or more) of 1.0day per decade, an increase in hot nights (20°C ormore) of 1.8 nights per decade, a decrease in colddays (15°C or less) of 1.4 days per decade and adecrease in cold nights (5°C or less) of 1.5 nights perdecade (Nicholls and Collins 2006). Therefore, pro-jected increases in average temperature are likely tobe associated with significant increases in very hotdays and decreases in cold nights.Potential changes in cold days below 0°C and hot
days above either 35°C or 40°C were calculated for20 Australian cities. Daily temperature data for 40years (from 1964 to 2003, except for BrisbaneAirportwhich only had data from 1960 to 1999) were provid-ed by the Bureau of Meteorology. The observed num-ber of days above or below the specified temperaturethresholds was calculated for each year and the 40-year average was derived. The mid-range tempera-ture projections for 2020, 2030, 2040, 2050, 2060,2070 and 2080 at each city were then added to each ofthe observed daily values, and the average number ofdays below 0°C and above 35 or 40°C was recalcu-lated for 2020 to 2080. This method effectively shifts
the frequency distribution toward warmer conditions,while maintaining the shape of the distribution.Changes in daily temperature variance were excludedfor simplicity, but we acknowledge that changes invariance can be important if they are large relative tothe mean changes. Information about simulatedchanges in mean maximum and minimum tempera-ture was not available for the fifteen models selected.The present and projected annual average numbers
of days below 0°C are shown in Table 4. For example,the present average is 16 in Alice Springs, 35 inLaunceston and 62 in Canberra. By 2030, these aver-ages decline to 5-13, 16-30 and 41-57, respectively.By 2070, these averages decline to 0-9, 0-25 and 10-51, respectively.The present and projected annual average numbers
of days above 35°C are shown in Table 5. For exam-ple, the present average is 10 in Melbourne, 11 inDarwin, 17 in Adelaide, 27 in Perth, and 65 inCharleville. By 2030, these averages rise to 10-15 inMelbourne, 18-73 in Darwin, 19-25 in Adelaide, 29-38 in Perth and 72-105 in Charleville. By 2070, theseaverages rise to 12-31 in Melbourne, 31-305 inDarwin, 22-46 inAdelaide, 33-68 in Perth and 82-177in Charleville. The response is enhanced in Darwinsince it has a very narrow frequency distribution. Fortemperate cities (Adelaide, Alice Springs, Canberra,Cobar, Longreach, Melbourne, Perth and Sydney) theaverage number of days above 35°C increases by 4-18 days by 2030, and by 9-56 days by 2070.
144 Australian Meteorological Magazine 56:3 September 2007
Table 4. Present (1964-2003) and projected average number of days below 0°C at 20 Australian locations. Low (L) andhigh (H) scenarios are based on data shown in Fig. 14. All values are rounded to the nearest integer.
City Present 2020 2030 2040 2050 2060 2070 2080L H L H L H L H L H L H L H
The present and projected average numbers ofdays above 40°C are shown in Table 6. For example,the present average is 2 inAdelaide, 4 in Perth, and 16in Alice Springs. By 2030, these averages rise to 3-5,5-8 and 20-43, respectively. By 2070, these averagesrise to 3-15, 6-23 and 27-113, respectively.
Average annual and seasonal rainfall. Figure 15shows ranges of annual and seasonal percentagechanges in rainfall for 2030 and 2070, relative to1990. Table 7 shows rainfall changes for each decadebetween 2020 and 2080, corresponding to Fig. 15.Note that seasonal rainfall changes are not shown for
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 145
Table 5. Present (1964-2003) and projected number of days above 35°C at 20 Australian locations. Low (L) and high (H)scenarios are based on data shown in Fig. 14. All values are rounded to the nearest integer.
City Present 2020 2030 2040 2050 2060 2070 2080L H L H L H L H L H L H L H
Table 6. Present (1964-2003) and projected number of days above 40°C at 20 Australian locations. Low (L) and high (H)scenarios are based on data shown in Fig. 14. All values are rounded to the nearest integer.
City Present 2020 2030 2040 2050 2060 2070 2080L H L H L H L H L H L H L H
regions in which the seasonal average is less than 30mm (i.e. white areas in winter and spring).The annual rainfall changes are dominated by
decreases across most of the country, but there is atendency for increased rainfall in some parts of south-eastern Australia. Decreases of 3 to 22% occur in theextreme southwest, 0 to 22% in the rest of the south-west and 0 to 15% for southern coastal regions. InlandWestern Australia, Northern Territory and coastalQueensland show rainfall changes between -15 and+7%. Regions within 200 km of the northern coastshow changes between -7 and +7%, broadening tobetween -15 and +15% over inland Queensland.Changes of -7 to +15% are projected for northernNew South Wales, south-central Queensland,Tasmania and central Northern Territory. If CO2 con-centrations are stabilised at 550 ppm by the year2150, the upper and lower limits of rainfall change areboth reduced by 23%. If CO2 concentrations are sta-bilised at 450 ppm by the year 2100, the upper andlower limits are both reduced by 25%. Details forregional rainfall changes are given in Table 8.The changes in 2070 are simply scaled-up versions
of those for 2030. Decreases of 7 to 70% occur in the
extreme southwest, 0 to 70% in the rest of the south-west and 0 to 45% for southern coastal regions. InlandWestern Australia, Northern Territory and coastalQueensland show rainfall changes between -45 and+23%. Regions within 200 km of the northern coastshow changes between -23 and +23%, broadening tobetween -45 and +45% over inland Queensland.Changes of -23 to +45% are projected for northernNew South Wales, south-central Queensland,Tasmania and central Northern Territory. If CO2 con-centrations are stabilised at 550 ppm by the year2150, the upper and lower limits of rainfall change areboth reduced by 38%. If CO2 concentrations are sta-bilised at 450 ppm by the year 2100, the upper andlower limits are both reduced by 48%. Details forregional rainfall changes are given in Table 8.
DiscussionThe CSIRO (2001) climate change projections werebased on results from a set of nine models that per-formed well in the Australian region. Eight of themodel simulations were performed for the IPCC’s
146 Australian Meteorological Magazine 56:3 September 2007
Fig. 15 The 15-model range of projected annual and seasonal rainfall changes for 2030 and 2070, relative to 1990, for(a) SRES emission scenarios, (b) a scenario that stabilises the CO2 concentration at 550 ppm by the year 2150(WRE 550 ppm) and (c) a scenario that stabilises the CO2 concentration at 450 ppm by 2100 (WRE450 ppm).Map colours apply to all three scenarios, and the boxes show which range corresponds to each colour for eachscenario. See Table 7 for ranges of change from 2020 to 2080.
Third Assessment Report. The updated projectionsreported here are based on results from a new set offifteen models that perform well in the Australianregion. All these fifteen model simulations were per-formed for the IPCC’s Fourth Assessment Report. In
both projections, the same statistical tests were usedto assess each model’s ability to simulate the climateof 1961-1990, and the same pattern-scaling techniquewas used to derive patterns of future temperature andrainfall change. Therefore, a comparison of the
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 147
Table 7. Projected changes in rainfall (%) for Australia for 2020 to 2080 based on (a) the SRES (2000) emission scenar-ios, (b) CO2 concentration stabilised at 550 ppm (WRE 550) by the year 2150, and (c) CO2 concentration sta-bilised at 450 ppm (WRE 450) by the year 2100. Changes above +30% and below -30% are rounded to the near-est 5%. The colours in the legend apply to regions in Fig. 15.
(a)
SRESLegend 2020 2030 2040 2050 2060 2070 2080
low high low high low high low high low high low high low high
CSIRO (2001) and updated projections gives insightinto the robustness of the earlier projections, and thedependence of the results on the number and age ofthe models.
On the basis of statistical tests, we selected nineout of ten models to construct climate change projec-tions in 2001. Only the Center for Climate SystemResearch (CCSR) GCM was not included in that
148 Australian Meteorological Magazine 56:3 September 2007
Table 8. Summary of rainfall changes simulated by 15 climate models. Ranges of uncertainty include the full range ofIPCC (2001) global warming and regional differences among 15 climate models.
2030 2070
Annual
Summer
Autumn
Winter
Spring
Decreases of 3 to 22% occur in the extreme southwest, 0 to 22%in the rest of the southwest and 0 to 15% for southern coastalregions. Inland Western Australia, Northern Territory and coastalQueensland show rainfall changes between -15 and +7%.Regions within 200 km of the northern coast show changesbetween -7 and +7%, broadening to between -15 and +15% overinland Queensland. Changes of -7 to +15% are projected fornorthern New South Wales, south-central Queensland, Tasmaniaand central Northern Territory.
Most of New South Wales, southern and western Queensland,parts of the Northern Territory and northeastern South Australiashow rainfall changes between -7 and 15%. Southern coastalregions indicate rainfall changes between -15 and +7%, andTasmania between -22 and +7%. The southwestern coast has adecrease of 0 to 15%. Elsewhere, the range of uncertainty is cen-tred on zero.
Decreases of 0 to 22% occur in the extreme southwest and 0 to15% along southern coastal regions of Western Australia.Changes of -15 to +7% occur in most parts of Western Australia,inland Queensland, the Northern Territory, south-central NewSouth Wales, southwest Victoria and South Australia’s EyrePeninsula. Changes of -22 to +7% are projected for coastalQueensland. Central Australia, Tasmania and isolated parts ofQueensland and New South Wales show changes of -15 to +15%and small regions in Victoria, southwestern New SouthWales andsoutheastern South Australia show changes between -7 and +7%.Northwestern Australia, southwest South Australia and south-eastern New South Wales show changes of -7 to +15%.Southwestern Queensland and north-central New South Walesshow changes of -15 to +22%
Decreases dominate rainfall changes, except for Tasmania and asmall region in eastern Victoria. Decreases of 0 to 22% are pro-jected for southwestern Western Australia, South Australia’s EyrePeninsula, northern New South Wales and southern Queensland.Decreases of 0 to 15% are projected for the southern coast ofWestern Australia, western Victoria, southwestern New SouthWales and southeastern South Australia. Changes between -22and +7% are projected for inland Western Australia, southwest-ern South Australia, northwestern New South Wales and central-coastal Queensland. Coastal New South Wales shows changes of-15 to +7%. Eastern Victoria and Tasmania show changes of -7to +15%.
Widespread decreases in rainfall occur. Southwestern WesternAustralia shows decreases of 3 to 22%. The remainder of south-ern Western Australia, southern South Australia and Victoria andcentral coastal Queensland show decreases of 0 to 22%. Southernregions of Northern Territory, northeastern Queensland, westernNew South Wales and northeastern Western Australia showchanges of -22 to +7%. Tasmania, eastern New South Wales andinland Queensland and northern Northern Territory show changesof -15 to +7%. Cape York has changes of -15 to +22%.
Decreases of 7 to 70% occur in the extreme southwest, 0 to 70%in the rest of the southwest and 0 to 45% for southern coastalregions. Inland Western Australia, Northern Territory and coastalQueensland show rainfall changes between -45 and +23%.Regions within 200 km of the northern coast show changesbetween -23 and +23%, broadening to between -45 and +45%over inland Queensland. Changes of -23 to +45% are projectedfor northern New South Wales, south-central Queensland,Tasmania and central Northern Territory.
Most of New South Wales, southern and western Queensland,parts of the Northern Territory and northeastern South Australiaindicate rainfall changes between -23 and +45%. Changesbetween -45 and +23% occur in southern coastal regions, andTasmania shows from -70 to +23%. The southwestern coast has adecrease of 0 to 45%. Elsewhere, the range of uncertainty is cen-tred on zero.
Decreases of 0 to 68% occur in the extreme southwest and 0 to45% along southern coastal regions of Western Australia.Changes of -45 to +23% occur in most parts of WesternAustralia,inland Queensland, the Northern Territory, south-central NewSouth Wales, southwest Victoria and South Australia’s EyrePeninsula. Changes of -68 to +23% are projected for coastalQueensland. Central Australia, Tasmania and isolated parts ofQueensland and New South Wales show changes of -45 to +45%and small regions in Victoria, southwestern New SouthWales andsoutheastern South Australia show changes between -23 and+23%. Northwestern Australia, southwest South Australia andsoutheastern New South Wales show changes of -23 to +45%.Southwestern Queensland and north-central New South Walesshow changes of -45 to +70%
Rainfall decreases dominate most parts of the country, except forTasmania and a small region in eastern Victoria. Decreases of 0to 70% are projected for southwestern Western Australia, SouthAustralia’s Eyre Peninsula, northern New SouthWales and south-ern Queensland. Decreases of 0 to 45% are projected for thesouthern coast of Western Australia, western Victoria, southwest-ern New South Wales and southeastern South Australia. Changesbetween -70 and +23% are projected for inland WesternAustralia, southwestern South Australia, northwestern NewSouth Wales and central-coastal Queensland. Coastal New SouthWales shows changes of -45 to +23%. Eastern Victoria andTasmania show changes of -7 to +15%.
Most parts of the country show decreases in rainfall.Southwestern Western Australia shows decreases of 7 to 70%.The remainder of southern Western Australia, southern SouthAustralia and Victoria and central coastal Queensland showdecreases of 0 to 68%. Southern regions of Northern Territory,northeastern Queensland, western New South Wales and north-eastern Western Australia show changes of -68 to +23%.Tasmania, eastern New South Wales and inland Queensland andnorthern Northern Territory show changes of -45 to +23%. CapeYork has changes of -45 to +70%.
analysis as this model failed to capture the basic cir-culation features over the Australian region. In thepresent analysis, we have selected fifteen out of 23models. Compared with previous model results, thereis a clear improvement in simulating the spatial pat-terns of MSLP, temperature and rainfall as shown bystronger pattern correlations. However, rms error val-ues do not show substantial differences between thetwo sets of models. In particular, previous and presentsets of models do not show much improvement incapturing the present spatial patterns of rainfall inwinter and spring over the Australian region.The annual and seasonal temperature projections
are very similar, despite the fact that slightly differentranges are used in the CSIRO (2001) and the updatedprojections. As in the 2001 projections, coastalregions warm at a slower rate than inland regions, andgreatest warming occurs in spring, with least warmingin winter. In the annual maps for 2070, regions with-in 200 km of the coast warm by 0.8 to 5.2°C in the2001 projections, but slightly less (0.4 to 4.5°C) in theupdated projections. Most of inland Australia warmsby 1.0 to 6.0°C by 2070 in the 2001 projections,which is similar to the updated projections of between1.1 to 5.7°C. In 2070, the seasonal areas of warmingof 1.0 to 6.8°C in the 2001 projections and 1.1 to6.7°C in the updated projections are similar indicatinggreater warming in inland tropical Australia.However, the region of extreme summer warming innorthwestern Australia in the 2001 projections is notevident in the updated projections.The 2001 and updated annual and seasonal rainfall
projections have similarities and differences. Thetrend in the majority of models is referred to as the‘tendency’ and model-to-model difference is referredto as the ‘bias’.
Similarities• Winter and spring: a tendency for decreases, withpatterns of change in the updated projections beingremarkably similar to those from CSIRO (2001),although the updated projections have strongerdecreases.
• Annual: a tendency toward decreases in the southand most of WesternAustralia, with ranges centredon zero over inland Queensland and the Top Endof the Northern Territory.
• Summer: a tendency for increases in New SouthWales and the southern half of the NorthernTerritory, and a tendency for decreased rainfallalong the South Australian and Victorian coastsand Tasmania.
• Autumn: a tendency toward decreases in south-western Western Australia and northernQueensland, with a tendency for increases in west-
ern Western Australia, southwestern SouthAustralia, the Northern Territory, northern NewSouth Wales and southwestern Queensland.
Differences• Annual: a tendency for increases in Tasmania,northern New South Wales, south-centralQueensland (whereas the range of change was cen-tred on zero in the 2001 projections) and southernNorthern Territory (compared with a tendency fordecrease in 2001), and a tendency for decreasesalong the central Queensland coast (comparedwith a range centred on zero in 2001).
• Summer: more widespread tendency for increas-es in the east (compared with a patchy tendencyfor increases in 2001). The range of change iscentred on zero along the Queensland coast(compared with a tendency for increase in 2001),with a tendency for increases in inlandQueensland (compared with a range centred onzero in 2001), and a tendency toward decreases insouthern Western Australia (compared to a rangecentred on zero in 2001).
• Autumn: a clearer tendency for decreases inQueensland and eastern Northern Territory. Therange is centred on zero in much of centralAustralia (compared with a tendency for increasein 2001) and Tasmania (compared with a tenden-cy for decrease in 2001), a tendency for decreasein southeast Queensland (compared with a rangecentred on zero in 2001), and a tendency fordecreases in western Queensland and easternNorthern Territory (compared with a tendency forincrease in 2001).
• Winter and spring: decreases are stronger andclearer than in the 2001 projections (the updatedprojections rarely include small increases).Therefore, using a larger sample of more-recent
climate models has not substantially changed the con-clusions drawn from the CSIRO (2001) projections.The patterns and magnitudes of warming are similar,although the updated projections have slightly lesswarming in coastal regions. The patterns of rainfallchange are also similar, but the updated projectionsgive a more widespread tendency for increases insummer in eastern Australia and a clearer tendencyfor decreases in autumn in Queensland and easternNorthern Territory.The updated projections are based on linearly scaling
simulated patterns of change by the IPCC (2001a) glob-al warming for selected years. Hence, we do not explic-itly allow for interannual or decadal variability, althoughobserved records indicate thatAustralian climate is high-ly variable on interannual and decadal time-scales (e.g.Pittock 1975; Nicholls and Lavery 1992; Power et al.
Suppiah et al.: Australian climate change projections from IPCC AR4 simulations 149
1998, 1999b; Smith 2004; Suppiah 2004). However, ananalysis of temperature and rainfall variability overSouth Australia (Suppiah et. al. 2006), suggest that thecurrent GCMs fairly well simulate interannual anddecadal-scale variability, although there are significantmodel-to-model variations. Previous studies (e.g. Poweret al. 1999a, 2006) also demonstrated the modulation ofENSO variability on Australian climate. Although thecurrent projections do not explicitly include the influ-ence of ENSO variability on Australian climate, in thefuture, an allowance for multi-decadal variability, andinterannual variability and circulation changes associat-ed with ENSO variability, will be incorporated in cli-mate change projections. In future, the arbitrary thresh-old for accepting or rejecting models based on their sim-ulation of the observed average climate will be replacedwith a more objective scheme (Whetton et al. 2007). Inthis scheme, each model is given a weighting based onpattern correlations and rms errors for temperature, rain-fall and MSLP, rather than omitting some models. Dueto demand from decision-makers and risk managers forprobabilistic projections, we are developing a methodfor generating regional projections, scaled by a probabil-ity density function for global warming (CSIRO and theBureau of Meteorology 2007). For finer-resolutionapplications, statistical and dynamical downscalingmethods will be used.
ConclusionsUsing climate simulations performed for the IPCC 4thAssessment Report, the reliability of 23 GCMs is test-ed on the basis of how well they reproduce features ofAustralia’s average climate (1961-1990). The best fif-teen models are used to construct average annual andseasonal temperature and rainfall projections forAustralia. Two methods are used: (a) a simple 15-model average (excluding uncertainty due to differ-ences between models), for low, mid and high globalwarming, and (b) ranges of likely change that includelow and high global warming, and uncertainty due todifferences between models. For both methods, theglobal warming estimates are based on IPCC (2001a).The central tendency for regional climate change isgiven by method (a) for the mid-range global warmingestimate, whereas the full range of change (includingextremes) is given by method (b). Low global warmingis expected to be 0.54°C by 2030 and 1.17°C by 2070,mid global warming is expected to be 0.89°C by 2030and 2.47°C by 2070 and high global warming isexpected to be 1.24°C by 2030 and 3.77°C by 2070.The projections show greater warming over inland
areas than coastal areas. Most warming occurs inspring, and least warming in winter. When the warming
projections are applied to observed daily temperaturedata at five Australian cities, which have a significantnumber of days below 0°C, the average annual numberof days below 0°C decreases 3-13 days by 2030, and 7-24 days by 2070. For eight temperate cities, the averageannual number of days above 35°C increases 4-18 daysby 2030, and 9-56 days by 2070.The central tendencies for 2030 and 2070 indicate
widespread decreases in annual-average rainfall, withlargest reductions along the south and west coasts,especially near Perth. In summer, increases are simu-lated over the eastern half of Australia, with decreas-es elsewhere. In autumn increases extend in a bandfrom the northwest to New South Wales, but mostcoastal regions have decreases. In winter and spring,large decreases are simulated over the southwest withsmaller decreases elsewhere.When differences between models are included, the
full ranges of rainfall change for 2030 and 2070 arequite broad. Annual-average rainfall by 2030 decreases3 to 22% in the extreme southwest, 0 to 22% in the restof the southwest and 0 to 15% for southern coastalregions. Inland Western Australia, the NorthernTerritory and coastal Queensland show rainfall changesbetween -15 and +7%. Regions within 200 km of thenorthern coast show changes between -7 and +7%,broadening to between -15 and +15% over inlandQueensland. Changes of -7 to +15% are projected fornorthern New South Wales, south-central Queensland,Tasmania and central Northern Territory. For 2070, therainfall changes are about three times as large.If CO2 concentrations are stabilised at 550 ppm by
the year 2150, the upper limit of Australian averagewarming is reduced by 23% by 2030 and 38% by2070. If CO2 concentrations are stabilised at 450 ppmby the year 2100, the upper limit of warming isreduced by 25% by 2030 and 48% by 2070.Given that projections are commonly used in risk
management, it is hoped that these climate changeprojections will assist in planning for climate change.Notwithstanding the uncertainties that arise from thedevelopment of regional climate change projectionsusing climate models, the anthropogenic greenhousegas and aerosol emissions scenarios that are used todrive the climate models will continue to contributelarge uncertainty to climate projections. This meansthat climate projections must always be treated aslikely futures given particular emission scenarios andnot viewed as forecasts.
AcknowledgmentsObserved temperature and rainfall data were providedby the National Climate Centre of the Australian
150 Australian Meteorological Magazine 56:3 September 2007
Bureau of Meteorology. Observed mean sea-level pres-sure data were extracted from the NCEP re-analysis.Regarding simulated climate data, we acknowl-
edge the modelling groups for providing their data foranalysis, the Program for Climate Model Diagnosisand Intercomparison (PCMDI) for collecting andarchiving the model output, and the JSC/CLIVARWorking Group on Coupled Modelling (WGCM) fororganising the model data analysis activity. Themulti-model data archive is supported by the Office ofScience, US Department of Energy.Martin Dix from CSIRO Marine and Atmospheric
Research and two anonymous reviewers provideduseful comments on the manuscript.This work was supported by the CSIRO Climate
research program and the Australian Climate ChangeResearch Program administered by the AustralianGreenhouse Office.
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