Probabilistic Predictions of Climate Change using the Reliability Ensemble Average (REA) of IPCC AR4 Model Simulations

Transcription

1 Probabilistic Predictions of Climate Change using the Reliability Ensemble Average (REA) of IPCC AR4 Model Simulations Summary: MOISE, Aurel F and HUDSON, D A Bureau of Meteorology Research Centre Melbourne, VIC, Australia Probabilistic predictions of climate change are produced for Australia, using the reliability ensemble average (REA) method applied to an ensemble of the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) model (CGCM) simulations. The REA method creates a weighted average of the ensemble, taking into account model ability to simulate observed climate and convergence in the predicted climate change. Results from this study provide a framework for applying quality criteria to assess model reliability of projecting climate change, for quantifying model uncertainty range and for producing climate change in probabilistic form. Temperature and precipitation changes for three emission scenarios (B1, A1B, A2) are analyzed, with a special focus on the A2 scenario. Regional differences in temperature and rainfall changes are identified and threshold probabilities and probability density functions (PDFs) for key sub-regions are produced. The skill of the CGCMs over Australia is also assessed. The PDFs of temperature change reveals specific regional details. In summer, A2 scenario results show a narrow PDF over south-west Western Australia (SWWA), reflecting agreement between models, partially caused by down-weighting of outlier models due to their large biases. In contrast, the PDF for the Murray-Darling Basin (MDB) of Australia is much broader and that for tropical Australia has a bi-modal character. There are significant decreases in rainfall under the A2 scenario in winter in SWWA (25-30% decrease) (simulated by all the CGCM simulations) and the MDB (15-25% decrease). Nevertheless, there are no significant changes of rainfall above natural variability in monsoonal summer, hence no changes in Australian monsoon rainfall. The value of the REA methodology in providing regional detail in climatic changes over Australia is demonstrated, as well as its merit as a tool for producing probabilistic climate change projections using multi-model ensembles. Keywords Probabilistic climate change, REA, model simulations, Australia 1. Introduction It is essential that we acquire knowledge of possible future climate changes so as to develop adaptation and mitigation planning for the future. General Coupled Circulation Model (CGCM) simulations forced by changes in greenhouse gas and aerosol emission scenarios, are the most common tool used for addressing future climate change. There are, however, considerable uncertainties associated with these GCM projections and difficulties in appropriately assessing multi-model ensembles. The most common approach has been to produce mean or median climate change response from the ensemble using the poor-man s ensemble approach, qualified by some measure of dispersion or agreement (eg. [Santer et al., 1990]; [Joubert and Tyson, 1996]; [Hulme and Carter, 2000]; [Cubash et al., 2001]; [Räisänen, 2001], [Moise et al, 2005]). In these analyses, each model is treated as being equally reliable in its projection of the future climate. This has raised questions of how to define reliability and wether it is appropriate to treat each model equal. Mearns et al. [2001] note that a criterion most often used to assess reliability is the ability of the CGCM(s) to simulate present-day conditions. In addition, more credence is given to a particular climate change prediction if it is simulated by multiple CGCMs. To these considerations, Giorgi and Mearns [2002] developed a methodology that produces a

2 weighted average of an ensemble of climate change results, taking into account the ability of a particular model to simulate the observed climate, and its degree of convergence in the predicted climate change with respect to the other models. Their procedure, termed Reliability Ensemble Averaging (REA), also allows an assessment of the reliability of the projected climate change with weighted averages, as well as the calculation of an uncertainty range. Given the inherent uncertainties of the climate system, which prevent deterministic prediction of future climate change, possible future conditions are more favourably to be presented as a likelihood of occurrence. Various approaches have been proposed recently to produce probabilistic predictions of climate changes (see [Giorgi, 2005], for an overview). An extension of the REA method further allows for calculating PDFs for climate variables under climate change conditions [Giorgi and Mearns 2003]. The present study uses the REA technique to examine detailed spatial patterns and magnitude of climate changes over Australia. Parts of our analysis, in particular the production of PDF s of climate change, are based on sub-regions within Australia (Figure 1) using updated climate model predictions. Three sub-regions within Australia tropical Australia, south-west Western Australia (SWWA) and south-east Australia - have been selected for this analysis. Firstly, Tropical Australia reflects changes associated with the Australian monsoon. Secondly, south-west Western Australia has experienced a decline in winter precipitation during the later part of the 20 th century ([Timbal, 2004] and [Hope, 2006]). A number of studies have attributed such changes to large-scale circulation changes [Frederiksen and Frederiksen, 2005], changes in land-cover ([Timbal and Arblaster, 2005]; [Pitman, 2004]) as well as other man-made forcings [Timbal et al., 2006]. Thirdly, south-east Australia has experienced declines of winter rainfall since 1996 [Trewin and Jones 2004]. This region encompasses the Murray-Darling River Basin (MDB) which is one of Australia s most important agricultural areas and the economic consequences of previous droughts have been severe [Lawrance et al., 2005]. Tropical Australia Southwest WA South-east Australia Fig. 1: Maps of Australia (1=Western Australia, 2=Northern Territory, 3=South Australia, 4=Queensland, 5=New South Wales, 6=Australian Capital Territory, 7=Tasmania). Also shown are the sub-regions used in parts of the analysis (boxes) and latitudinal dividing line used by the IPCC Third Assessment Report (TAR) for their analysis (dashes line). 2. Methodology and Data A suit of up to 14 CGCMs are used in this study, drawn from the database of the CMIP3 model simulations used for the IPCC Fourth Assessment Report (AR4). Precipitation flux (PR) and surface air temperature (TAS) fields from the 20 th century (20C3M) and three scenario runs (SRES-B1, SRES- A1B, SRES-A2) have been used for this analysis. Details of the greenhouse gases in the three

3 scenarios considered are detailed in the IPCC Special Report on Emission Scenarios [IPCC, 2000]. The CO 2 forcing associated with the B1, A1B and A2 scenarios reaches a maximum of 550 ppm, 720 ppm and 850 ppm respectively by In the analysis, observations from high quality gridded datasets at the Australian Bureau of Meteorology (National Climate Centre, NCC) for Australia 1 are used. We have compared the austral summer (DJF) and winter (JJA) seasonal means of these observations to the 20 th century runs of the CGCMs ( average) as recommended by PCMDI. 20-yr means from scenario runs are compared to the 20 th century runs ( average). All fields were re-gridded to a common 2.5 x 2.5 grid, and only results over land are analysed. Figure 1 shows the sub-regions used in parts of this analysis. There are three sub-regions for Australia: tropical Australia {(10-20 S), ( E)}, south-west Western Australia {SWWA: (30-36 S), ( E)}, and south-east Australia {(28-37 S), ( E)}. The REA method is the same as described in Giorgi and Mearns [2002, 2003], and is based on the following two basic assumptions: (1) a model reliability is defined by its ability to reproduce the observed 20 th century climate conditions (reliability measured by bias or bias criterion, R B ); and (2) a model is deemed more reliable in its ability to project future climate changes if it tends to agree on the magnitude and sign of the changes with the other models (reliability according to distance or convergence criterion, R D ). This method produces a weighted average of the ensemble of climate change results (called REA mean from here on), taking into account these two measurements of reliability. The overall model reliability is the product of the two separate reliability factors for bias (R Bi ) and convergence (R Di ). Both the bias and convergence reliabilities depend on the observed natural variability ( ) of the field of interest. The natural variability is calculated by taking the difference between the maximum and minimum values of the observed 100 year de-trended time series (which is smoothed with a 30 year running window) at each grid point. If the bias, B i, of a model or its simulated change from the REA mean future climate, distance D i, is within the bounds of the observed natural variability, the corresponding reliability is set to one. The reliability factor thus ranges in value from 0 (unreliable) to 1 (reliable) and a model is deemed reliable (i.e. R i = 1) when both the bias and distance are within natural variability. The uncertainty range around the REA mean is given by the root mean square distance measure. Probabilities of regional climate change are calculated using the REA method by assuming each model s reliability as an indicator of the likelihood of its simulation (Giorgi and Mearns, 2003). The change simulated by a more reliable model is more likely to occur: Ri P( mi ) = (1) N R j = 1 j P(m i ) is the contribution from model m i to the overall reliability. Threshold probabilities can be derived by summing over all P(m i ) exceeding a given threshold of climate change. For example, to find the probability of a temperature change exceeding T th, we calculate T > T P i = i th ( m ) P( m ) i i, Ti > Tth (2) This procedure is applied over Australia to determine the probabilities of exceeding particular thresholds of temperature and precipitation changes. The resulting threshold probabilities provide the basis for the calculation of probability density functions through differentiation: P( mi ) Probability density = (3) ( Tth ) The averaging and reliability calculations presented in this study are determined on a 2.5 x 2.5 resolution and different models may be more or less reliable over different areas of the domain. 1 For further detail and data access, see the NCC website at

4 3. Bias in present-day climate The model bias is examined over the analyzed time period ( ) and a more detailed description of each models performance over Australia is given in [Moise et al. 2006]. During the austral summer (DJF), the observed rainfall climatology displays heavy rainfall in tropical Australia and eastern coastal regions generated by the summer monsoon system. The ensemble mean model shows a negative bias of around 0.7 mm/day (~20 mm/month), which is confined to coastal areas in tropical Australia and southern Victoria. Positive biases in precipitation during the summer season are seen mainly over inland Australia, particular northern and western Australia, by up to about 25 mm/month. This suggests that the heavy tropical precipitation may be penetrating too far south towards inland Australia, although the heavy rainfall in its monsoon region is overall underestimated. During austral winter (JJA), there are negative biases in precipitation over southern parts of Australia and along the east coast, extending somewhat into tropical Australia. Much of this region, dominated by frontal rainfall in winter, shows up to 30 mm/month negative biases. Furthermore, there is a strong agreement between the models in the sign of this bias, particularly over south-west Western Australia. This agreement is also largely true for the region of positive bias, which suggests that the models are simulating too much precipitation over central Australia. This bias reaches about 15 mm/month in the ensemble mean model. In general, where there is large observed precipitation over Australia (i.e. tropics in DJF, southern Australia in JJA), the ensemble mean model tends to underestimate rainfall, but in areas of low observed rainfall the models tend to simulate too much rainfall. The analysis also shows that the ensemble mean underestimates surface air temperature across large parts of Australia in summer and winter, seen in more than half of the models in both seasons. In winter the ensemble mean has a negative bias of up to 2 C, with some individual models showing a cold bias of up to 5 C. In summer there is a warm bias largely confined to south-east Australia and patches along the coastal areas of Western Australia. This bias is generally smaller than the cold bias with values below 1 C in the ensemble mean model. 4. Future climate scenarios 4.1 REA Mean change under the A2 scenario Over Australia the area-averaged simple and REA ensemble mean warming for summer (DJF) under the A2 scenario are both 3.9 ºC. Maximum warming (> 4 ºC) is found between 20ºS and 30ºS, with a focal point (> 4.5 ºC) over Western Australia (Figure 2). These magnitudes of change are well in excess of natural variability, which has an area-average of 0.3 ºC, i.e. all simulated future warming is greater than the observed natural variability in the climate of the 20 th century. The REA ensemble mean is very similar to the simple mean, and the main effect of calculating the weighted average is to reduce the uncertainty range (i.e. ±RMSD). The area-averaged simple and REA RMSDs are 0.9 ºC and 0.6 ºC respectively. The regions of largest REA RMSD (i.e. > 0.7 ºC) are found in southern Queensland, northern New South Wales and parts of the Northern Territory. The magnitude of temperature change in winter (JJA) is similar to summer, with a REA mean area-averaged change of 3.8 ºC. The predicted warming exhibits a north-south gradient, with maximum warming in the north (Figure 2). As for summer, the predicted warming is much greater than natural variability, which has an area-average of 0.3 ºC. Compared to the simple ensemble mean, the REA mean increases the warming over portions of the northern states, particularly Western Australia. The REA procedure markedly reduces the uncertainty range (i.e. ±RMSD) about the mean, particularly over central and northern regions (area-averaged simple RMSD = 0.7 ºC; area-averaged REA RMSD = 0.4 ºC). Values of REA RMSD range from 0.2 ºC in Victoria to 0.6 ºC in northern Queensland.

5 Figure 2: Australia REA mean for ( ) temperature in summer (DJF, left) and winter (JJA, right). Results are for the A2 scenario Australia: precipitation In summer (DJF) there are no predicted precipitation changes greater than natural variability for both the REA ensemble mean and the simple ensemble mean under the A2 scenario (Figure 3). The REA mean shows small changes over tropical regions of northern Australia, but these are well within the noise of natural variability (Figure 3), which is high in this region due to the erratic monsoon rainfall. In winter (JJA) a more interesting signal emerges. The decreases in REA mean rainfall over southwest Western Australia (WA) and south-eastern Australia (Victoria and southern New South Wales) that are greater than 0.2 mm/day are significant. This is equivalent to a 25-30% decrease in rainfall over the south-west and a 15-25% decrease over the south-east. The decrease over these regions is still evident when examining the upper bound of the REA uncertainty range (i.e. REA mean + REA RMSD) (not shown) and is evident, to varying degrees, in all of the 12 models examined. The REA RMSD is less than 0.3 mm/day over these regions and a clear consequence of the REA procedure is to reduce the RMSD over Australia compared to the simple RMSD. Figure 3: Australia REA mean ( ) precipitation in summer (DJF, left) and winter (JJA, right). Results are for the A2 scenario. The ensemble mean bias suggests that the models are too dry over south-western WA, and this is indeed a signal found in all 12 models. This raises the possibility that the signal of a decrease in future rainfall may be related to the negative bias i.e. the scenario forcing is intensifying the process that is responsible for the bias. However, the fact that we are seeing a reduction in rainfall over this region in the recent observed record ([IOCI, 2002] and [Timbal, 2004] and [Timbal et al., 2006]) suggests that it may be a true signal. It is clear from the results presented here that model convergence in the simulation of future climate change is greater than the ability of the models to capture the present-day

6 climate. This is a result that Giorgi and Mearns [2002] obtained in virtually all the area-averaged land regions they considered, although they mention that it is not necessarily an obvious conclusion since many aspects of models are in some way tuned to represent the present-day climate. This result is also illustrated by looking at the range of change (for the A2 scenario future) and bias at each grid box from the ensemble of models. The range in the simulated future climate change is much smaller than the range in model bias for both temperature and precipitation (not shown here). 4.3 Forcing-related uncertainty: the inclusion of other emission scenarios The results discussed above are based on the A2 emissions scenario, only one of many possible forcing projections for the future. We have repeated the procedure using model output based on the B1 and A1B emissions scenarios. The results from these two scenarios are very similar to that of the A2 scenario, in terms of the patterns of REA mean temperature and precipitation change over Australia, as well as the associated reliability analysis, and are therefore not shown here. The main difference is the degree of warming reached by the end of the 21 st century. Figure 4 summarises the REA mean results from the three emissions scenarios, area-averaged over the sub-regions of Australia shown in Figure 1. Precipitation changes are plotted against the respective temperature changes. The temperature changes shown for all the regions are significant, and the precipitation changes that are significant have symbols surrounded by a dashed circle (a change is significant if it is greater than the area-averaged observed natural variability calculated for the specific region). Over all the Australian regions there are clear relationships between temperature change and the emissions scenario: the temperature increases as the forcing associated with the emissions scenario increases. However, the same is not always true for precipitation. DJF JJA 0.15 SWWA MDB TROP B1 A1B A Precipitation change (mm/day) Precipitation change (mm/day) Temperature change ( o C) -0.3 Temperature change ( o C) Figure 4: Area-averaged changes across three scenarios (SRES-B1, A1B, A2) for all sub-regions for summer (left) and winter (right). All temperature changes are greater than the observed natural variability and the dashed-circled symbols indicate if the precipitation changes are greater than the observed natural variability. 4.4 Probabilistic approach: the A2 scenario Threshold probabilities for Australian precipitation and temperature Probabilities for future changes in rainfall and temperature for the A2 scenario exceeding successive thresholds have been computed using equation (2). As an example, figure 5 shows the probability of exceeding precipitation thresholds (-0.2, -0.1, mm/day) in JJA for Australia under the A2

7 scenario. Areas where the threshold probability is above 50% are shown in increasing shades of orange, reaching red at 100% mm/day -0.1 mm/day 0.1 mm/day 0.2 mm/day JJA Figure 5: Probability of exceeding precipitation thresholds (from left to right: -0.2, -0.1, 0.1, 0.2 mm/day) during the Australian winter (JJA). In summer (DJF, not shown here), there are only small areas across Australia showing probabilities of above 50% for both increased or decreased rainfall. The increases are mainly seen in a north-west to south-east band through tropical Australia, and also in a smaller region in south-east Australia. The decreases are seen in central Western Australia. All of these areas show a fairly low probability of occurrence with a maximum of 60-70% for the ± 0.1 mm/day thresholds. The most probable future changes in rainfall are the decreases seen during winter (JJA). The areas affected are the south-west of Western Australia and the south and south-east of Australia. The probability is up to 90% within these areas, even for setting the threshold to 0.2 mm/day. The probability for rainfall increases above 0.1mm/day is practically zero everywhere in Australia Selected PDF s for Australian precipitation and temperature Using the threshold probabilities from the previous section, probability density functions were calculated for the SRES-A2 scenario, using equation (3), for various regions in Australia. Figure 6 shows the seasonal probability density functions of temperature and precipitation change for the A2 scenario for southwest Western Australia, the Murray-Darling-Basin, tropical Australia, and the whole continent. The probability density range for changes in summer temperatures over the entire Australian continent is between 2 C and 6 C with a saddle-like peak between 3.4 C and 4.2 C (Median = 3.9 C). The shape of the PDF s for the three regions under study are quite different. While the greenhouse enhanced warming over south-west Western Australia shows a fairly narrow PDF (Median = 3.6 C), the PDF for the Murray-Darling-Basin is spread as wide as the All-Australian one (Median = 3.6 C), and the PDF for tropical Australia has a very distinct bi-modal PDF with peaks at 3.2 C and 4.2 C (Median = 3.7 C). The reason for finding narrow uni-modal distributions could be two-fold: (1) there is an obvious agreement among models; and/or (2) outlier models are down-weighted due to their large bias. The multi-modal distributions might be related to the fact that the coupled GCMs give disparate predictions, none of which can be discarded on the basis of model bias. In this case, the mean or median change would not be good summaries (i.e. summer temperature change in tropical Australia). It is not clear if the bi-modality for tropical Australia has an obvious physical interpretation, but it certainly highlights the problematic nature of predictions in this specific area. The PDF s for winter temperature changes are different to their summer counterparts, except for the whole continent. Here, the probability density range is again between 2 C and 6 C with a saddle-like peak between 3.5 C and 4.4 C (median = 3.9 C). The PDF for SWWA has shifted towards lower temperatures (median = 2.9 C) but has a similar width to summer. The PDF for the MDB region is distinctly bi-

8 modal with a peak at 3.4 C and 4.2 C (median = 3.5 C). The PDF for tropical Australia has become uni-modal for winter and shifted towards higher temperatures (median = 4.4 C) compared to summer. T Probaility Density DJF Probaility Density JJA All_oz swwa mdb tropics Temperature Change ( o C) Temperature Change ( o C) Pr Probaility Density Probaility Density Figure 6: Precipitation Change (mm/day) Seasonal probability density functions of ( ) temperature and precipitation change for the A2 scenario for southwest Western Australia (swwa), the Murray-Darling-Basin (mdb), tropical Australia (tropics), and the whole continent (All_oz) Precipitation Change (mm/day) The PDF s for rainfall changes in summer are uni-modal and symmetrically centred around zero change, reflecting the fact that most models simulate equally small increases and decreases. In the tropics, the PDF is spread out further (almost without a peak), but still symmetrical around zero changes. Changes in the Australian monsoonal rainfall are not decisively simulated, with a 50% probability of small increases or decreases. All PDF s for winter rainfall changes are uni-modal. There are decreases in rainfall in SWWA and the MDB region during winter which can be seen from the location of the corresponding PDFs. The median change in rainfall for SWWA is 0.3 mm/day and for the MDB region 0.2 mm/day. Most of the two distributions lie in the negative-change half (90% SWWA, 80% MDB), indicating a good consensus between the models. The PDF for tropical winter rainfall changes is narrow and very symmetrical around zero changes. The PDF for the entire Australian continent is slightly shifted towards negative changes (median = 0.1 mm/day) 5. Discussion and Conclusion We have applied the REA method of ensemble weighing to the IPCC AR4 model simulations for 20 th century and future climate change conditions, focusing on a spatially resolved analysis for Australia. Australia is situated in the subtropical high pressure belt with its southern extremities affected by midlatitude westerlies and their associated fronts in winter, while their northern parts experience disturbances that are of a tropical origin. Our results have shown a predicted reduction in rainfall for southern portions of the continent in winter under climate change.

9 Using the REA technique, we have also derived regional probabilistic climate change projections for sub-regions in Australia. These are the first probabilistic regional climate change projections for important regions in Australia defined on grounds other than the currently used gross geographical division seen in IPCC and other publications (e.g. [Giorgi and Bi, 2005], and [Tebaldi et al., 2005]). With the advent of both regional and probabilistic climate change projections, there is a strong case to look more closely at specific regions along natural boundaries rather than latitudinal cuts. Giorgi and Bi [2005] updated an earlier assessment [Giorgi and Mearns, 2002] of regional changes in precipitation and temperature over 26 land regions worldwide based on the REA method. Our results are qualitatively similar to Giorgi and Bi [2005], but exact comparisons are difficult for a number of reasons. Firstly, they area-average their results over large regions of Australia. Secondly, Giorgi and Bi define dry and wet seasons as extended six-month periods (which is generally not the case across Australia), whereas we have looked at 3-month periods (DJF and JJA). Additionally, the 20 th century comparison period used by Giorgi and Bi [2005], , is different to the one used here, In their area-averaged assessment, Australia is divided into 2 regions (NAU, SAU), basically cut in half at 28 S, thereby assigning more than 60% of the Australian continent to NAU. This does not represent tropical Australia alone and exceeds the area affected by the Australian monsoon system. Our subregions SWWA and MDB are part of their SAU region, while tropical Australia lies within NAU. Giorgi and Bi s [2005] precipitation changes are in % and not in mm/day and regional warming is given as a fraction of global ensemble average warming: (regional REA warming)/(ensemble average global warming). Keeping this in mind, the area-averaged REA warming over NAU and SAU for the A2 scenario was reported at between 3.4 C and 4.2 C, exceeding 4.2 C (up to 5 C) for NAU in the dry season (May-Oct) [Giorgi and Bi, 2005]. In addition, they found no change in precipitation during the wet season in NAU and the dry season in SAU (both being defined as the period November- April). However, they reported a 13% decrease in precipitation for the NAU region in the dry season and the SAU region in the wet season (both being defined as the period May-Oct). While it is not surprising that there is an overlap with our results, we have achieved to spatially resolve the projected climate changes and thereby identify regions that are more affected than others. Consequently, we find areas in central and north-west Western Australia which are projected to warm more than 4.5 C during summer and winter respectively more than anywhere else in Australia. Also, we can see that the decreases in rainfall are projected to occur over south-west Western Australia and south-east Australia with declines as high as 30%. The results we have presented, including a spatially resolved analysis over Australia, and an ability to produce PDF s of climate change for important sub-regions, is a significant advancement from the type of results shown in the last IPCC Third Assessment Report (TAR) [Giorgi et al., 2001a]. Figure 1 shows the latitudinal dividing lines for Australia (northern, NAU and southern, SAU) used in the TAR. Consistent with our study, the TAR reported small decreases in rainfall for the B2 and A2 scenarios in winter in SAU, but we do not see the small decreases in rainfall that they report for the NAU region. In addition, we do not see the small increases in rainfall that they report for the SAU region in summer. Finally, it is important to note, that the quality of the results presented in our paper are dependent on a number of important factors. Firstly, the calculation of the REA average is influenced by the estimate of natural variability. If the bias with respect to observed and the distance from the REA average are within natural variability, then the model result for that particular grid box is deemed reliable. If the natural variability estimates used here were increased, then the REA average and RMSD would approach the simple average and RMSD. However, we have found that the results are relatively insensitive to small changes in natural variability. In addition, the area-averaged temperature values for natural variability in Australia compare well with those published earlier by [Tebaldi et al 2005], the difference being less than 0.1 C for the Australian regions.

10 Secondly, some of the differences in the performance of the models could be related to differences in the forcing: a number of AR4 models have only anthropogenic forcings in their 20 th century runs (CNRM, IPSL, CSIRO, UKMO-HADCM3, ECHAM5), while others have both natural and anthropogenic forcing (CCSM, INM, MIROCm, GFDL2, GFDL2.1, MRI, PCM). Thirdly, the performance criterion and hence the REA average are dependant on the quality of the observational dataset used to determine the model bias. This may be important to some extent for interior regions of Australia, where data is limited by inadequate spatial and temporal coverage and therefore are represented by only a few stations (for example, the Northern Territory is represented by only 18 stations in the NCC high quality rainfall data set). Fourthly, our analysis does not include or take into account possible future changes in variability. The PDFs resulting from our analysis are PDFs of possible future mean temperature and precipitation changes. Lastly, there are caveats associated with the two criteria (performance and convergence) used to weight the various model responses. A good simulation of the observed climate, or convergence on a future climate change, does not necessarily mean that the climate change signal is robust and reliable. In terms of the convergence criterion, it is possible that the GCMs possess common errors in their response to the emissions forcing. Although the models have been developed at different institutions and have significant differences in their formulation, they still possess common or similar components and are all based on our current understanding of the operation of the earth-atmosphere system. In terms of the performance criterion, a reasonably realistic simulation of the observed climate does not guarantee that the feedbacks important for the response to anthropogenic forcing are adequately modelled ([Mearns et al., 2001]; [Räisänen, 2001]). This may be particularly applicable to the present study because of the way performance is judged. Comparing temperature and precipitation with observed does not assure a good control climate, since there may be compensating errors which produce agreement. What is critical is that important processes and feedbacks should be correctly modelled. A possible way forward is to use process-based studies to determine reliability. For example, if a model is capable of modelling observed cloud-feedback processes, then we may have more confidence in its ability to simulate the cloud response to anthropogenic forcing [Williams et al., 2003]. While there are currently other methods being studied to produce regional probabilistic climate change projections, the procedure applied here based on the REA work of Giorgi and Mearns [2002, 2003] provides a quick and elegant way of using multi-model ensembles to identify regions of particular concern in the future climate. Acknowledgements This study was supported by the Australian Greenhouse Office (AGO), Canberra, Australia. The Australian Bureau of Meteorology s observational data was provided by the National Climate Centre. NCEP Reanalysis data provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, USA, from their Web site at A special thanks to L. Hanson, BMRC Climate Dynamics Groups, for his contribution in the data retrieval. We acknowledge the international modelling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis

11 activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy. References Cubasch U., G. A. Meehl., G. J. Boer, R. J. Stouffer, M. Dix, A. Noda, C. A. Senior., S. Raper, and K. S. Yap (2001), Projections of future climate change, in Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change. edited by J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, pp , Cambridge University Press: Cambridge, United Kingdom and New York, NY,USA. Frederiksen, J.S., and C. Frederiksen (2005), Decadal Changes in Southern Hemisphere Winter Cyclogenesis, CSIRO Marine and Atmospheric Research Paper No. 002, CSIRO CMAR, Melbourne, Australia. Giorgi, F., B. Hewitson, J. Christensen, M. Hulme, H. Von Storch, P. Whetton, R. Jones, L. Mearns, and C. Fu (2001), Regional Climate Information Evaluation and Projections, in Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change. edited by J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, pp , Cambridge University Press: Cambridge, United Kingdom and New York, NY,USA. Giorgi, F., and L.O. Mearns (2002), Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the Reliability Ensemble Averaging (REA) Method, J Clim, 15, Giorgi, F., and L. Mearns (2003), Propability of regional climate change based on the Reliability Ensemble Averaging (REA) method, Geophys Res Let, 30 (12), art.no 1629, doi: /2003gl Giorgi, F. and X. Bi (2005), Updated regional precipitation and temperature changes for the 21 st century from ensembles of recent AOGCM simulations, Geophys Res Let, 32, doi: /2005gl Giorgi, F. (2005), Climate Change Prediction, Clim Change, 73, Hope, P.K., W. Drosdowsky and N. Nicholls, (2006). Shifts in the synoptic systems affecting southwest Western Australia, Clim Dyn,26, p Hulme M. and T. R. Carter (2000), The changing climate of Europe, in Assessment of potential effects and adaptations for climate change in Europe: The Europe ACACIA Project, edited by M.L. Parry, 350pp, Jackson Environment Institute, University of East Anglia: Norwich. IOCI, (2002), Climate variability and change in south west Western Australia. Technical report, 34pp., Indian Ocean Climate Initiative Panel, Perth. IPCC (2000), Intergovernmental Panel on Climate Change Special Report on Emission Scenarios, IPCC SRES, pp.570, Geneva,. Cambridge University Press, Cambridge, UK. Lal M., P. H. Whetton, A. B. Pittock and B. Chakraborty (1998), The greenhouse gas-induced climate change over the Indian subcontinent as projected by general circulation model experiments. TAO, 9, Lawrance, L., S. Kinsella, S. Hardcastle, H. Rodgers, and F. Drum (2005), Australian Crop Report, Crop report no. 133, Australian Bureau of Agriculture and Resource Economics (ABARE), Canberra. Mearns L. O., M. Hulme, T. R. Carter, R. Leemans, M. Lal and P. Whetton (2001). Climate Scenario Development, in: Climate Change 2001: The Scientific Basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, pp , Cambridge University Press: Cambridge, United Kingdom and New York, NY,USA.

12 Moise, A.F. and Participating IPCC-CMIP Modelling Groups (2007), Australian Climate and its Potential Changes Simulated by Some IPCC AR4 Models, submitted to JGR. Moise, A.F., R. A. Colman, and H. Zhang, and Participating CMIP2 modeling groups (2005), Coupled Model Simulations of Current Australian Surface Climate and Its Changes under Greenhouse Warming: An Analysis of 18 CMIP2 Models, Aust Met Mag, 54, Pitman, A. J., G. T. Narisma, R. A. Pielke, and N. J. Holbrook (2004), The impact of land cover change on the climate of south west Western Australia, J Geophys Res, 109, doi: /2003jd Räisänen J. (2001), CO 2 -induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J Clim, 14: Santer B. D., T. M. L. Wigley, M. E. Schlesinger and J. F. B. Mitchell (1990), Developing climate scenarios from equilibrium GCM results. Report no. 47, 29pp., Max-Planck-Institut-für-Meteorologie: Hamburg,. Taylor, K.E. (2001), Summarizing multiple aspects of model performance in a single diagram. J Geophys Res, 106 (D7), Tebaldi, C., R. Smith, D. Nychka, and L.O. Mearns (2005), Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles, J Clim, 18, Timbal, B. (2004), Southwest Australia past and future rainfall trends, Clim Res, 26, Timbal, B., and J. Arblaster (2005), Land cover changes as an additional forcing to explain the rainfall decline in the South West of Australia, Geophys Res Let, 33, doi: /2005gl Timbal, B., J. Arblaster, and S. Power (2006), Attribution of the late 20 th century rainfall decline in South-West Australia, J Clim, 19(10), Trewin, B., and D. Jones (2004), Notable recent rainfall anomalies in Australia, paper presented at 16 th Australia New Zealand Climate Forum, Lorne, Victoria, Australia, Novermber Williams, K.D., M. A. Ringer and C. A. Senior (2003), Evaluating the cloud response to climate change and current climate variability. Clim Dyn, 20,