Probabilistic predictions of climate change for Australia and southern Africa using the reliability ensemble average of IPCC CMIP3 model simulations

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2007jd009250, 2008 Probabilistic predictions of climate change for Australia and southern Africa using the reliability ensemble average of IPCC CMIP3 model simulations A. F. Moise 1 and D. A. Hudson 1 Received 3 August 2007; revised 10 February 2008; accepted 22 April 2008; published 7 August [1] Reliability ensemble averaging (REA) is applied to a multimodel ensemble of coupled atmosphere-ocean general circulation models (AOGCMs) from the third phase of the Coupled Model Intercomparison Project (CMIP3) to produce mean and probabilistic climate change projections for Australia and southern Africa. CMIP3 was conducted in preparation for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). REA produces a weighted average of the ensemble of climate change results, based on a measure of each model s bias and each model s degree of convergence in the predicted climate change. The methodology allows an assessment of the reliability of the projected climate change, the quantification of model uncertainty range, and the production of climate change projections in probabilistic form. Temperature and precipitation changes for three emission scenarios (B1, A1B, and 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 subregions are produced. In summer for temperature the A2 scenario results show a narrow PDF over southwestern Australia, reflecting agreement between models, partially caused by down weighting of outlier models due to their large biases. In contrast, the PDF for southeastern Australia is much broader, and that for tropical Australia has a bimodal character. There are significant decreases in rainfall under the A2 scenario in winter in the southwest (25 30% decrease) (common in sign to all AOGCM simulations) and the southeast (15 25% decrease) of Australia. There are no changes above natural variability in summer, hence no significant changes in the magnitude of Australian monsoon rainfall. Over southern Africa the region of maximum warming is over the Kalahari Desert in summer and winter. Under the A2 scenario in summer, there is a significant increase in rainfall over Kenya and Tanzania and a significant drying (10 20%) over parts of Namibia and Botswana. In winter, most of southern Africa south of 15 S shows significant decreases in rainfall. Citation: Moise, A. F., and D. A. Hudson (2008), Probabilistic predictions of climate change for Australia and southern Africa using the reliability ensemble average of IPCC CMIP3 model simulations, J. Geophys. Res., 113,, doi: /2007jd Introduction [2] Australia and Southern Africa both experience highly variable climates and present significant vulnerabilities to human-induced climate change. Both are situated in the subtropical high-pressure belt with their southern regions affected by midlatitude westerlies and their associated fronts in winter, while their northern parts experience disturbances that are of tropical origin. Rainfall exhibits a large degree of interannual variability and water is a limited resource in Australia and much of southern Africa. River runoff is relatively low, with variable flows, and Australia and South 1 Centre for Australian Weather and Climate Research, Melbourne, Victoria, Australia. Copyright 2008 by the American Geophysical Union /08/2007JD Africa have runoff variability (determined by the coefficient of variation) well in excess of the world average [Finlayson and McMahon, 1988]. In addition, southern Africa is characterized by high population growth rates, a reliance in many areas on subsistence level agriculture, generally low levels of income and high population densities on marginal lands. Australia, although a large country, has its population aggregated predominantly in coastal regions and its main agricultural areas are prone to drought. [3] It is essential that we acquire knowledge of possible climate changes in order to develop adaptation and mitigation strategies for the future. Coupled general circulation models (AOGCMs), along with appropriate regionalization methods (e.g., regional climate models, statistical downscaling), are the most appropriate tools for addressing future climate change. There are, however, considerable uncertain- 1of26

2 Figure 1. Maps of (left) Australia (1, Western Australia; 2, Northern Territory; 3, South Australia; 4, Queensland; 5, New South Wales; 6, Australian Capital Territory; and 7, Tasmania) and (right) southern Africa (1, Gabon; 2, Congo; 3, Democratic Republic of the Congo; 4, Tanzania, Rwanda, Burundi, and Uganda; 5, Kenya; 6, Angola; 7, Zambia; 8, Malawi; 9, Mozambique; 10, Namibia; 11, Botswana; 12, Zimbabwe; 13, Madagascar; and 14, South Africa, Lesotho, and Swaziland). Also shown are the subregions used in parts of the analysis. ties involved in the modeling process [Meehl et al., 2007], and more generally in the production of climate change scenarios, often referred to as the uncertainty cascade or explosion [e.g., Mitchell and Hulme, 1999; Jones, 2000]. Over recent years there has been an increasing focus on the production of probabilistic results that incorporate at least some of this uncertainty [e.g., Räisänen and Palmer, 2001; Murphy et al., 2004; Giorgi, 2005; Tebaldi et al., 2005; Greene et al., 2006; Christensen et al., 2007; Meehl et al., 2007]. This paper focuses on uncertainty due to intermodel differences, and to some extent uncertainty in future emissions. A suite of up to 14 AOGCMs, whose results were used for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), are analyzed in the present study. A suite of up to 14 AOGCMs from the third phase of the Coupled Model Intercomparison Project (CMIP3), whose results were used for the IPCC Fourth Assessment Report (AR4), are analyzed in the present study. The most common way of synthesizing results from a multimodel ensemble is to produce an ensemble mean (e.g., as done by Cubasch et al. [2001] and Christensen et al. [2007] and Meehl et al. [2007]), which treats each model as equally reliable and has been shown to produce results closer to observed than any individual model [e.g., Ebert, 2001]. Here, however, we use the method of Giorgi and Mearns [2002, 2003] to produce a weighted mean of the ensemble of climate change results based on the reliability of each model. Reliability takes into account the ability of a particular model to simulate the observed climate, and the degree of convergence in its predicted climate change with respect to the other models in the ensemble [Giorgi and Mearns, 2002]. Their procedure, termed reliability ensemble averaging (REA), also allows an assessment of the reliability of the projected weighted average climate change, the calculation of an uncertainty range and the production of probabilistic results [Giorgi and Mearns, 2002, 2003]. [4] Giorgi and Mearns [2002, 2003] apply their REA approach to area-averaged temperature and precipitation changes for 22 land regions of the world (later updated by Giorgi and Bi [2005]). [5] Australia was represented by two regions (NAU and SAU, separated at 30 S) and southern Africa fell into three regions, but was dominated by one (SAF). In contrast, the present study uses the REA technique to examine detailed spatial patterns and magnitude of climate changes over Australia and southern Africa. Parts of our analysis, in particular the production of PDFs of climate change, are based on subregions within Australia and southern Africa (Figure 1). The three subregions within Australia, tropical Australia (10 20 S, E), southwest Western Australia (30 36 S, E) and southeast Australia or the Murray-Darling Basin (28 37 S, E), have been targeted in earlier work reporting particular concern for present and future climate change. First, tropical Australia reflects changes associated with the Australian monsoon. Second, southwest Western Australia has experienced a decline in winter precipitation during the later part of the 20th century [Timbal, 2004; Hope et al., 2006], which has been attributed to large-scale circulation changes [Frederiksen and Frederiksen, 2005], changes in land cover [Timbal and Arblaster, 2006; Pitman et al., 2004] and other man-made forcings [Timbal et al., 2006]. Third, southeast Australia has experienced a decline in winter rainfall since 1996 [Trewin and Jones, 2004]. This region encompasses the Murray-Darling River Basin which is one of Australia s most important agricultural areas and the economic consequences of previous droughts have been severe [Lawrance et al., 2005]. In this study, Southern Africa is divided into six regions, with latitude cuts at 15 S and 25 S separating 2of26

3 Table 1. CMIP3 Models and Scenarios Used in This Analysis a Organizations/Group(s) Country IPCC ID Resolution 20C3m B1 A1B A2 National Center for United States ccsm DA DA DA DA Atmospheric Research Météo-France/Centre National de France cnrm-cm DA DA DA DA Recherches Météorologiques CSIRO Atmospheric Research Australia csiro-mk DA DA DA DA Max Planck Institute for Meteorology Germany echam5/mpi-om DA DA DA DA LASG/Institute of Atmospheric Physics China fgoals-g DA DA U.S. Department of Commerce/NOAA/Geophysical United States gfdl-cm DA DA DA DA Fluid Dynamics Laboratory U.S. Department. of Commerce/NOAA/Geophysical United States gfdl-cm DA DA DA DA Fluid Dynamics Laboratory NASA/Goddard Institute for Space Studies United States giss-aom 4 3 DA DA DA Institute for Numerical Mathematics Russia inm-cm DA DA DA DA Institut Pierre Simon Laplace France ipsl-cm DA DA DA DA Center for Climate System Research (University of Tokyo), Japan miroc3.2(medres) DA DA DA DA National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) Meteorological Research Institute Japan mri-cgcm DA DA DA DA National Center for Atmospheric Research United States pcm DA DA DA DA Hadley Centre for Climate Prediction and Research/Met Office United Kingdom ukmo-hadcm DA DA DA DA a DA indicates data available at the time of analysis and thus used in the present study. IPCC ID is Intergovernmental Panel on Climate Change identification. northern, central and southern areas, while 24 E longitude separates eastern and western areas. These selections were made because of distinct rainfall patterns seen in the observed climate during the seasons. [6] Section 2 provides a brief description of the REA method and how it is applied to the models used in the present study. Section 3 considers the model skills in reproducing present-day climate in terms of their biases and spatial patterns of precipitation and temperature. This is vital in order to be able to assess the climate change results, which are addressed in section 4. Apart from examining the REA mean changes in temperature and precipitation, section 4 also provides probabilistic results of future climate change for selected regions. Finally, section 5 provides a general discussion and conclusion. 2. Methodology and Data [7] This study uses up to 14 of the CMIP3 AOGCMs used for the IPCC Fourth Assessment Report (AR4) [Intergovernmental Panel on Climate Change (IPCC), 2007] which were available at the time of the analysis (Table 1). A more detailed documentation of participating models is available on the PCMDI (Program for Climate Model Diagnosis and Intercomparison) website: www-pcmdi.llnl.gov/ipcc/about_ipcc.php. Precipitation (PR) and surface air temperature (TAS) fields from the 20th century (20C3M) runs and three scenario runs (SRES-B1, SRES-A1B, SRES-A2) are used for this analysis (Table 1). Details of the greenhouse gases in these three scenarios are given in the IPCC Special Report on Emission Scenarios [IPCC, 2000]. The CO 2 concentration associated with the B1, A1B and A2 scenarios reaches 550 ppm, 720 ppm and 850 ppm, respectively, by [8] Model performance is assessed using the high-quality gridded temperature and precipitation data sets from the Australian Bureau of Meteorology for Australia, and the University of East Anglia s Climate Research Unit time series (CRU TS2.1) temperature and precipitation data sets [Mitchell and Jones, 2005; New et al., 2002] for southern Africa. These data sets are hereinafter referred to as the observed data. We compare the summer (December, January, and February (DJF)) and winter (June, July, and August (JJA)) seasonal means of these observations to the 20th century runs of the AOGCMs ( average). As recommended by PCMDI, the 20-year means from the SRES scenario runs ( average) are compared to the 20th century model runs ( average). Multiple realizations from any one model are averaged before processing, although some modeling groups have not submitted more than one realization. [9] The model and observed data are regridded to a common grid, with only land points used in the interpolation. All REA calculations are performed for each grid box, but larger area averages of the REA results (for the subregions shown in Figure 1) are used in parts of the analysis. [10] The REA method is the same as described by Giorgi and Mearns [2002, 2003], and is only briefly summarized here. It is based on the following two basic assumptions: (1) A model s reliability is defined by its ability to reproduce present-day climate conditions (reliability according to bias). (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 in the ensemble (reliability according to distance or degree of convergence). This method produces a weighted average of the ensemble of climate change results taking into account these two measures of reliability. Thus, the weighting factor is a product of two separate reliabilities, one based on the model s bias with respect to observations (R B ) and the other based on the distance of its simulated climate change from the REA average change (R D ). Values of the reliability (weighting) factor increase as the bias and distance decrease, thus models that are poor performers or who are outliers are weighted less in the calculation of the REA mean change. The bias of a model is calculated as the difference between the model and observed mean temper- 3of26

4 ature or precipitation for the period. The convergence distance of a model is the difference between its simulated temperature or precipitation change and the REA ensemble mean change (where change refers to the mean difference between the future and present-day periods). Both the bias and convergence reliabilities depend on the observed natural variability of the field of interest. Natural variability is calculated by taking the difference between the maximum and minimum values of the observed 100-year detrended time series (which is smoothed with a 30-year running mean) at each grid point. If the bias of a model or its simulated change from the REA mean future climate is within the bounds of the observed natural variability, the corresponding reliability is set to one. The reliability factor ranges in value from 0 (unreliable) to 1 (reliable) and a model is deemed reliable 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 (RMSD) and is a measure of the differences among the models in the simulated climate change. Giorgi and Mearns [2002] also describe a measure of the collective model reliability (~r) in the simulated changes, which is essentially the REA average of the individual model reliability factors. This quantity is useful for comparing the reliability of the REA mean change over different regions. 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]. That is, the change simulated by a more reliable model is more likely to occur. This procedure is applied over Australia and southern Africa 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. For a detailed account of the REA method and the relevant equations, see Giorgi and Mearns [2002, 2003]. [11] As mentioned in section 1, Giorgi and Mearns [2002, 2003] and Giorgi and Bi [2005] applied their technique to area-averaged changes over large subregions over Australia and southern Africa. In contrast, the averaging and reliability calculations presented in this study are determined on a resolution and different models may be more or less reliable over different areas of the domain. 3. Present-Day Climate [12] This section describes some general features of the temperature and precipitation climatologies from the models such as the bias for the multimodel ensemble mean (a simple averaging of all the model simulations at each grid point). A more detailed description of each models performance over Australia is also given by A. F. Moise and Participating IPCC-CMIP Modelling Groups (Australian climate and its potential changes simulated by some IPCC AR4 models, manuscript in preparation, 2008) Bias Australia [13] 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 (Figure 2). The ensemble mean shows a negative bias of around 0.7 mm/d (20 mm/ month), which is confined to coastal areas in tropical Australia and southern Victoria (Figure 3). Positive biases in precipitation during the summer season are seen mainly over inland Australia, particularly northern and Western Australia, up to about 25 mm/month. This suggests that tropical precipitation may be penetrating too far south toward inland Australia, although the heavy rainfall in the monsoon region is underestimated. [14] During 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 strong similarity between the models in the sign of this bias (not shown here), particularly over southwest 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. [15] In general, where there is large observed precipitation over Australia (i.e., tropics in DJF, southern Australia in JJA) (Figure 2), the ensemble mean model tends to underestimate rainfall, but in areas of low observed rainfall the models tend to simulate too much rainfall. [16] Figure 3 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 (not shown). 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 (not shown). In summer there is a warm bias largely confined to southeast Australia and patches along the coast of Western Australia Southern Africa [17] Rainfall over most of southern Africa is markedly seasonal (Figure 2) (except for the south coast, the arid southwest and the moist tropics), with more than 80% of rain falling in the summer half of the year (October to March) [Hobbs et al., 1998]. The seasonal biases in precipitation and temperature (Figure 3) are dominated by too much rainfall in summer (30 60 mm/month above observed) and too low temperatures during summer and winter. Thirteen of the 14 models examined exhibit the cold bias over the central southern African plateau in summer, and in most this is associated with too much rainfall (not shown). In addition, many models tend to underestimate the extent and aridity of the dry southwest region of South Africa and Namibia (not shown) Spatial Analysis [18] The spatial characteristics of the models temperature and precipitation fields have been examined in more detail. Taylor plots [Taylor, 2001] (Figure 4) show the normalized standard deviation of each model with respect to the observations (distance from origin), the spatial pattern correlation of each model with the observations (azimuth position) and the normalized pattern-centered root-meansquare error of each model (RMSE) (dashed circles in intervals of 0.2, indicating the distance from observations 4of26

5 Figure 2. Observed (top) precipitation (in mm/d) and (bottom) temperature (in C) climatology for Australia (Bureau of Meteorology data) and southern Africa (University of East Anglia s Climate Research Unit time series data set CRU TS2.1 [Mitchell and Jones, 2005; New et al., 2002]) in summer (DJF) and winter (JJA) for period. at (1,0)). Each Taylor plot in Figure 4 shows both temperature (capital gray letters) and precipitation (small black letters), as well the ensemble mean for both variables (letters Q and q, respectively) Australia [19] In general, modeled spatial variation (standard deviation) in temperature is closer to observed than for precipitation, particularly in winter, and temperature also shows a Figure 3. Ensemble mean bias for (top) precipitation and (bottom) temperature for Australia in summer (DJF, first column) and winter (JJA, second column) and southern Africa in summer (DJF, third column) and winter (JJA, last column). Positive (negative) biases are in dark (light) gray shades. 5of26

6 Figure 4. Taylor plot for (top) Australian and (bottom) southern African precipitation (black lowercase letters) and temperature (gray capital letters) model performance. Shown are the results for (left) summer (DJF) and (right) winter (JJA) for models listed in Table 1. The ensemble mean is depicted as q (in gray) and Q (in black) for precipitation and temperature, respectively. smaller RMSE for most models. The spatial pattern of surface air temperature is reasonably simulated, with all models showing a relatively high correlation, mostly above 0.9, in both seasons. There is a seasonal difference in how the models capture the spatial variation (standard deviation) for temperature: temperature variation in summer is underestimated while most models show a higher than observed variation in winter. For precipitation, most models simulate below observed spatial variability in both seasons, but with a larger spread in spatial correlations and pattern-centered RMSE in winter compared to summer. The Australian summer monsoon does not coincide with markedly increased spatial correlations of the simulated climatologies, since some models have higher correlations in summer than in winter and vice versa. In both seasons, the ensemble mean (letter Q for temperature and q for precipitation) shows a high pattern correlation with observations, but the spatial variation is lower than observed, particularly for precipitation. As expected, the temperature spatial distribution is better simulated by the ensemble than the precipitation spatial distribution. As has been found in other studies [e.g., Ebert, 2001], the ensemble averaging 6of26

7 process generally provides better results than any individual model Southern Africa [20] Temperature spatial variation (standard deviation) is generally well simulated, although most models show slightly higher than observed values in both seasons, up to 30% above observed variability. The spatial correlations for temperature are fairly low in summer (ranging from 0.5 to 0.8, with an average of 0.7) compared to winter (ranging from 0.89 to 0.96, with an average of 0.92), and the patterncentered RMSEs are much larger in summer. The spatial variation of southern African precipitation in summer is quite reasonably simulated with a slight majority of the models showing above observed variability. Similar to the temperature simulations, the summer spatial correlations are fairly low (ranging from 0.47 to 0.86, with an average of 0.67), suggesting that a lot of the models do not capture the location and spatial extent of the major convergence zones. During winter, the spatial correlations of model precipitation with observed are poor, and in this respect the ensemble mean performs better than any individual model in both seasons. In contrast to summer, the range in spatial variability of the models precipitation fields in winter is much larger, with some models showing spatial variability more than 50% above observed variability. 4. Future Climate Scenarios 4.1. REA Mean Change Under the A2 Scenario Australia: Temperature [21] The change in summer surface air temperature under the A2 scenario is given in Figure 5. To illustrate the effect of calculating the REA weighted average the REA average, and RMSD are shown as well as the simple ensemble average and RMSD (i.e., calculated with no weighting). Over Australia the area-averaged simple and REA ensemble mean warming for summer under the A2 scenario are both 3.9 C. Maximum warming (>4 C) is found between 20 S and 30 S, with a focal point (>4.5 C) over Western Australia (Figure 5). These magnitudes of change (Figure 5) are well in excess of observed natural variability (Figure 6), which has an area average of 0.3 C. While the REA ensemble mean change is very similar to the simple mean, the main result of calculating the weighted average is the reduced uncertainty range, i.e., ±RMSD (Figure 5), which is the desired effect. The RMSD measures the differences among models in the simulated mean climate change. The area-averaged simple and REA RMSDs are 0.9 C and 0.6 C, respectively. The regions of largest REA RMSD (i.e., >0.7 C) are found in southern Queensland, northern New South Wales and parts of the Northern Territory. [22] The REA mean change can be assessed in terms of collective model reliability (~r). Giorgi and Mearns [2002] highlight that the significance of this measure is to compare the level of reliability across different regions. As described in section 2, reliability is defined by the performance and convergence reliability criteria. The method filters out, or down weights, those models that are poor performers at each grid box or who are outliers in their simulation of the future climate. This is the reason Figure 5 shows lower REA RMSD compared to the simple ensemble RMSD, and Giorgi and Mearns [2002] note this effect over most of the area-averaged land regions they considered. For the REA mean temperature change in summer, the area-averaged collective model reliability (~r) is 0.5 (Figure 6). The area-averaged collective model reliability with respect to performance alone (R B ) is 0.4. This low level of performance reliability implies that on average over the domain the model biases are about 2.5 times observed natural variability. Lowest performance reliability is found over the southeast and northwest of the country, with biases in excess of 3 times observed natural variability (Figure 6). Two thirds of the models are too cold over the northwest of Australia (not shown, but reflected in the ensemble mean bias in Figure 3), but over the southeast there is a mixture of positive and negative biases from the different models. The area-averaged collective model reliability with respect to convergence (R D ) is higher than the performance reliability (Figure 6; area average is 0.6), suggesting that the reliability of the REA mean change is degraded more by poor model performance than by lack of convergence on a future temperature prediction. It is interesting to note that the REA mean change greater than 4.5 C over Western Australia (Figure 5) has low levels of reliability (~r < 0.4) (Figure 6). Low levels of reliability (R B, R D and ~r) are often associated with regions of low natural variability (Figure 6). Reliability is explicitly related to the estimate of natural variability, with tighter constraints in regions of lower natural variability i.e., in these regions, smaller biases and distances can make the grid box from a model unreliable compared to regions of higher natural variability. As Giorgi and Mearns [2002] point out, this difference in precision over different regions is necessary because of differences in the underlying natural variability. [23] The magnitude of temperature change in winter is similar to summer, with a REA mean area-averaged change of 3.8 C. The predicted warming exhibits a north-south gradient, with maximum warming in the north (Figure 5). As for summer, the predicted warming is much greater than natural variability (Figures 5 and 6). 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 C; area-averaged REA RMSD = 0.4 C). Values of REA RMSD range from 0.2 C in Victoria to 0.6 C in northern Queensland. [24] Collective model performance reliability (R B ) over Australia in winter (JJA) is low (values range from 0.1 to 0.6 with an area average of 0.3) (Figure 6). This implies that on average the model biases are as large as 3.3 times observed natural variability. R B values less than 0.2 are found over a large proportion of Western Australia and parts of Victoria and New South Wales (Figure 6), indicating biases exceeding natural variability by factors of 5 to 10. These results suggest that in general the models have greater difficulty in simulating JJA than DJF presentday temperature over Australia. As was found for summer, the collective model convergence reliability (R D ) is considerably higher than the performance reliability, with an area average of 0.7 (Figure 5). In general, the overall reliability (~r) of the REA mean change is greatest over northern regions (north of about 20 N) of Australia, where 7of26

8 Figure 5. Australia REA mean, simple mean, REA RMSD, and simple RMSD for temperature (TAS) ( C) and precipitation (PR) (mm/d) changes in summer (DJF) and winter (JJA). Shaded regions show precipitation changes greater than observed natural variability. Results are for the A2 scenario. 8of26

9 Figure 6. Australia reliability criteria RB, RD, ~r, and observed natural variability for temperature (TAS) and precipitation (PR) in summer (DJF) and winter (JJA). 9of26

10 some of the larger temperature changes (>4 C) are predicted (Figures 5 and 6) Australia: Precipitation [25] In summer 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 5). The REA mean shows small changes over tropical regions of northern Australia, but these are well within the noise of natural variability (Figures 5 and 6), which is high in this region because of the erratic monsoon rainfall. It is interesting to note that the collective model performance reliability of the REA mean is relatively high (area average R B = 0.7), suggesting that the models are relatively good in reproducing summer rainfall over Australia. The overall collective reliability of the REA mean change over the region is very high (area average ~r = 0.8), largely because of the high convergence reliability values (Figure 6). However, with precipitation this reliability must be assessed in relation to the size of the REA change with respect to natural variability. The reliability results here indicate that in summer over Australia there is high reliability in a change that is not significant, i.e., within natural variability. [26] In winter a more interesting signal emerges. The decreases in REA mean rainfall over southwestern Australia and southeastern Australia that are greater than 0.2 mm/d are significant (shading on Figure 5 indicates changes greater than natural variability). This is equivalent to a 25 30% decrease in rainfall over the southwest and a 15 25% decrease over the southeast. 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 (hence the high R D values). The REA RMSD is less than 0.3 mm/d over these regions (Figure 5). The values of R B are within the range of over southwestern Australia and over the southeast, suggesting slightly better model performance over the latter. These regions both receive frontal rainfall in winter, thus model deficiencies may be related to the positioning of the fronts and/or their rainfall intensity. The ensemble mean bias suggests that the models are too dry over southwestern Australia (Figure 3), 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 [Indian Ocean Climate Initiative Panel, 2002; Timbal, 2004; Timbal et al., 2006] suggests that it may be a true signal Southern Africa: Temperature [27] Southern Africa is predicted to experience a similar magnitude of warming to Australia in summer under the A2 scenario, with a REA area-averaged change of 4.0 C (Figure 7). A large proportion of southern Africa (and virtually all of the region south of 15 S) shows warming greater than 4.0 C, with maximum increases over southwestern regions, focused on the Kalahari Desert (Figure 7). The area-averaged natural variability for temperature over southern Africa in summer is 0.5 C (Figure 8), well below this predicted change. The REA ensemble mean differs from the simple ensemble mean by simulating a higher temperature increase (up to 1 C higher), particularly over the central plateau. The REA uncertainty range (areaaveraged RMSD = 0.6 C) is smaller than the simple uncertainty range (area-averaged RMSD = 0.8 C). Largest REA RMSDs (>0.8 C) are found over Botswana, coincident with some of the highest projected warming. [28] The collective model performance reliability (R B )of the REA temperature change is generally low (area average R B = 0.4), with biases on average as large as 2.5 times observed natural variability (Figure 8). The region of lowest performance reliability is over the Democratic Republic of Congo. Here the average bias exceeds the natural variability estimate by a factor of 5. This is a region of low natural variability, hence subject to tighter constraints on reliability than, for example, the central tropical plateau (Figure 8). There is also low performance reliability (<0.4) south of 25 S, particularly in the vicinity of the highest REA mean future warming. Virtually all of the models are too cold in this region, as is reflected in the ensemble mean bias (Figure 3). The reliability due to model convergence (R D ) is greater than reliability due to performance, having an area average of 0.7. Largest convergence reliability is found over the central inland plateau (Figure 8). The overall collective model reliability (area averaged ~r = 0.5)of the REA mean change is clearly degraded by poor model performance, and most of the REA temperature changes greater than 5 C are associated with a reliability of less than 0.5 (Figures 7 and 8). [29] The temperature increase in winter is generally slightly higher compared to summer, particularly north of 15 S (Figure 7). Virtually all of southern Africa is predicted to experience a warming greater than 4 C (area-averaged REA mean = 4.3 C), which far exceeds natural variability. The region of maximum warming (>5 C) is found over southwestern Botswana, which is largely Kalahari Desert. This region of maximum temperature increase is associated with quite high values of overall collective model reliability (>0.7) (Figure 8) Southern Africa: Precipitation [30] In summer there is a pattern of drying south of 15 S, particularly over the west (southern Angola, Namibia and Botswana), and wetter conditions north of this, particularly over eastern regions (Tanzania and Kenya) (Figure 7). The REA mean shows more extensive drying of southern regions compared to the simple ensemble mean. However, the magnitudes of the predicted changes over southern Africa are small and much of the drying is within natural variability (Figures 7 and 8). A small region of drying over Namibia and Botswana (10 20% decrease in rainfall) is significant (greater than natural variability), as is the region of increased rainfall over equatorial East Africa. [31] The performance reliability in summer shows a NW-SE axis of low R B over subtropical southern Africa, with reliability values less than 0.4 (indicating biases more than 2.5 times natural variability estimates) (Figure 8). South of about 20 S the primary rainfall producing synoptic systems are tropical-temperate troughs and tropical easterly waves/troughs [Van Heerden and Taljaard, 1998; Tyson and Preston-Whyte, 2000]. Tropical-temperate troughs arise from the interaction between a surface heat low (in the region of northern Namibia/Angola) and easterly wave in 10 of 26

11 Figure 7. Southern Africa REA mean, simple (S) mean, REA RMSD, and simple RMSD for temperature (TAS) ( C) and precipitation (PR) (mm/d) in summer (DJF) and winter (JJA). Shaded regions show precipitation changes greater than observed natural variability. Results are for the A2 scenario. 11 of 26

12 Figure 8. Southern Africa reliability criteria RB, RD, ~r, and observed natural variability for temperature (TAS) and precipitation (PR) in summer (DJF) and winter (JJA). 12 of 26

13 Table 2a. Normalized Contributions to the Overall Model Reliability for Each Atmosphere-Ocean General Circulation Model Over Australia a CCSM3 CNRM CSIRO GFDL_2.0 GFDL_2.1 INM IPSL MIROC ECHAM5 MRI PCM HADCM3 DJF Precipitation All_oz SWWA MDB Tropics JJA All_oz SWWA MDB Tropics DJF Temperature All_oz SWWA MDB Tropics JJA All_oz SWWA MDB Tropics a Good model performance and convergence leads to higher contribution. If all models were equally good, they would contribute 8.3% each. For each continent and climatic field, the top two models (bold) for each season have been selected according to two criteria: (1) above average performance in most subregions and (2) high total percentage. Normalized contributions are given in percent. The subregions are as in Figure 1. the tropical easterlies, and a midlatitude disturbance south of Africa. They typically produce a cloud band of NW SE oriented convection, with convergence at the surface and divergence aloft sustaining deep uplift and rainfall. These troughs contribute most to annual rainfall over the central plateau of South Africa [Tyson and Preston-Whyte, 2000]. Tropical-temperate troughs and easterly waves help to produce the marked east-west gradient in precipitation over South Africa and a NW-SE boundary separating relatively moist air in the northeast from drier air in the southwest of the country. Many of the models underestimate the extent and aridity of the dry southwest region of South Africa and Namibia (not shown). The NW-SE axis of low R B may therefore be related to errors in the representation of the position, frequency and/or intensity of tropical-temperate troughs and easterly waves over the region. As well as possible large-scale errors in circulation, shortcomings in the ability of the models to capture observed rainfall may be related to limitations in modeling convective rainfall and clouds, with the majority of rainfall over southern Africa in summer being convective in origin. [32] The significant decrease in rainfall (Figure 7) over the Namibia-Botswana border falls within this region of low R B values, with values less than 0.3. As was shown for Australia in summer, most of the high values of R D over southern Africa are indicative of high reliability in a change that is not significant (i.e., small with respect to natural variability). For precipitation, it is vital that the convergence reliability is interpreted in relation to the size of the REA change with respect to natural variability. Further analysis therefore focuses on the regions of significant rainfall changes. For the significant decrease over the Namibia-Botswana border the convergence reliability (R D ) is between 0.6 and 0.8. However, the overall reliability (~r) is low, between 0.3 and 0.6, and is clearly a consequence of poor model performance. The rainfall increase over the northeastern equatorial region (predominantly Tanzania and Kenya) is slightly more reliable, with overall reliability of the REA mean change ranging between 0.3 and 0.8 (Figure 8). [33] In winter a similar result to that found for Australia emerges, that is a decrease in rainfall over subtropical regions (Figure 7). In fact, a large proportion of southern Africa south of 15 S shows a REA mean decrease in rainfall that is outside natural variability. However, most of these reductions are very small (<0.1 mm/d) and in reality this projected change may be of little practical significance. Most of the region showing the decrease in rainfall experiences very little rain in winter (<1 mm/d). An important exception to this is the southwestern cape and portions of the south coast of South Africa, which receive the majority of their rainfall in winter. Over these regions the predicted change under the A2 scenario equates to about a 20 35% decrease. A large proportion of the region showing a significant decrease in rainfall is associated with low overall reliability (Figure 8). However, the southwestern cape, for which the results may have practical significance, has relatively high reliability (~r > 0.6). [34] It is clear from the results presented for Australia and southern Africa that model convergence in the simulation of future climate change is greater than the ability of the models to capture the present-day climate. This has been found in previous research [e.g., Kittel et al., 1997; Räisänen, 2007] and is a result that Giorgi and Mearns [2002] obtained in nearly all the area-averaged land regions they considered, although they mention that it is not necessarily an obvious 13 of 26

14 Table 2b. Normalized Contributions to the Overall Model Reliability for Each Atmosphere-Ocean General Circulation Model Over Southern Africa a CCSM3 CNRM CSIRO GFDL_2.0 GFDL_2.1 INM IPSL MIROC ECHAM5 MRI PCM HADCM3 DJF Precipitation All_SA Northwest West Southwest Northeast East Southeast JJA All_SA Northwest West Southwest Northeast East Southeast DJF Temperature All_SA Northwest West Southwest Northeast East Southeast JJA All_SA Northwest West Southwest Northeast East Southeast a Good model performance and convergence leads to higher contribution. If all models were equally good, they would contribute 8.3% each. For each continent and climatic field, the top two models (bold) for each season have been selected according to two criteria: (1) above average performance in most subregions and (2) high total percentage. Normalized contributions are given in percent. The subregions are as in Figure 1. conclusion since many aspects of models are in some way tuned to represent the present-day climate Contributions of Individual Models to the Ensemble Mean [35] On the basis of the reliability factors from each model we can calculate the contribution of each model toward the REA mean change. Tables 2a and 2b show the normalized contributions (in percent) of each AOGCM to the overall model reliability over Australia and southern Africa. Good model performance and convergence leads to higher contribution, i.e., larger weights in the calculation of the REA mean. If all the models were equally good, they would contribute 8.3% each (i.e., they would be equally weighted). [36] In Tables 2a and 2b the top two contributing models are in bold for each season for precipitation and temperature. Here, we look for general consistency defined by above-average performance in most subregions and a high total percentage (adding up the contributions for all subregions). Considering both precipitation and temperature and Australia and southern Africa together, we find that there are a number of models fulfilling these criteria once (CNRM, MRI, MIROC_m), twice (GFDL_2.0, GFDL_2.1, INM, IPSL) and one model which stands out five times (ECHAM5). Particularly for Australia, the ECHAM5 model shows above average performance (except for summer temperature). However, a primary conclusion from Tables 2a and 2b is that no one model contributes equally highly over all regions, seasons and variables. For example, on the basis of our criteria, IPSL is one of the top two models for southern African precipitation in winter and temperature in summer, but it is never highlighted as a top model for Australia. Tables 2a and 2b or this type of information may be useful for selecting models for detailed analyses in future studies Forcing-Related Uncertainty: Inclusion of Other Emission Scenarios [37] The results discussed above are based on the A2 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 and southern Africa, 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 21st century. 14 of 26

15 Figure 9. Area-averaged changes across three scenarios (SRES-B1, A1B, and A2) for all subregions: (top) Australia and (bottom) southern Africa for (left) summer (DJF) and (right) winter (JJA). All temperature changes are greater than the observed natural variability, and the dashed-circled symbols indicate precipitation changes greater than observed natural variability. The subregions are shown in Figure 1. Abbreviations are SWWA, southwest Western Australia; MDB, Murray-Darling Basin; Trop, tropics; CW, central west; and CE, central east. [38] Figure 9 summarizes the REA mean results from the three emissions scenarios, area averaged over the subregions of Australia and southern Africa as 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 (here a change is defined as significant if it is greater than the area-averaged observed natural variability calculated for the specific region). Over all the Australian and southern African 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, this is not always true for precipitation. In summer over the three Australian regions, there are no significant changes in precipitation. 15 of 26

16 However, over southwestern Australia there appears to be a relationship between precipitation and the emissions scenario (or temperature change), such that precipitation decreases as the forcing associated with the emissions scenario increases. This relationship is clear in winter, when the decreases in precipitation over southwestern Australia are significant under the A1B and A2 scenarios. [39] As for Australia, the precipitation changes over the southern African regions in summer are also not significant, although it appears that there maybe a relationship between temperature and rainfall over the northeast (increase in rainfall with an increase in forcing) and over the central west region (decrease in rainfall with an increase in forcing). In winter there are significant decreases in rainfall under all three emissions scenarios over the central west and southwest regions Probabilistic Approach: A2 Scenario Threshold Probabilities for Australian Precipitation and Temperature [40] Probabilities for future changes in rainfall and temperature for the A2 scenario exceeding successive thresholds are shown in Figure 10. For precipitation in summer, there are only small areas across Australia showing probabilities of above 50% for either increased or decreased rainfall (Figure 10a). The increases (0.1 and 0.2 mm/d thresholds) are mainly seen in a northwest to southeast band through tropical Australia, and also in a smaller region in southeast Australia. The most probable future changes in rainfall are the decreases seen during winter. The area affected is southern Australia, particularly the southwest and southeast. The probability is up to 90% within these areas, even for setting the threshold to 0.2 mm/d. The probability for rainfall increases above 0.1 mm/d is practically zero everywhere in Australia. [41] For temperature, there is a near 100% probability across Australia for increases above 2 C in both summer and winter. During summer, this is also true for warming above 3 C, but for thresholds above 4 only an east-west band through central Australia exhibits high probabilities. In winter, areas of high probability decline in a simple southnorth fashion as thresholds increase, with highest probability in northwest Western Australia for above 4 C warming. [42] In order to compare threshold probabilities for different emission scenarios, Table 3 shows the values of threshold probabilities across the three scenarios. For example, the probability of exceeding the 3 C threshold for southwestern Australia changes from 1% for the B1 to 19% for the A1B to 93% for the A2 emission scenario. Tropical Australia shows a fairly linear relationship for winter temperature change across the scenarios with similar probabilities of occurrence for thresholds of 2 C for B1, 3 C for A1B, and 4 C for A2. For summer rainfall over the tropical region, probabilities are nearly independent of the thresholds used. These thresholds are small compared to natural variability, which is greater than 1 mm/d across the tropics, indicating high variability associated with the monsoon system. The predicted decreases in rainfall over southwestern Australia and Murray Darling Basin during winter show a greater than 70% chance of a decrease of more than 0.2 mm/d and 0.1 mm/d for the southwest and southeast, respectively, for the A2 scenario Threshold Probabilities for Southern African Precipitation and Temperature [43] In summer, there are contrasting results north and south of 15 S, namely increases in future rainfall to the north and decreases in rainfall to the south (Figure 11a). The probability for increased rainfall in tropical countries such as Tanzania and Kenya along the east coast, and Angola, Congo and the Dem. Rep. of Congo on the west coast, remains high even at the +0.2 mm/d threshold. Separating these two areas of high probability is a small area of low probability located near the Rift Mountains in the east of the Congo Basin. Almost the entire African land south of 15 S shows future decreases in rainfall during summer more than 0.1 mm/d with the highest probability of up to 90% in southern Angola, Namibia and western Botswana, which includes the Kalahari Desert. This region also shows the highest probability for further rainfall decreases more than 0.2 mm/d, while probabilities in southeastern Africa decline below 50%. The threshold probabilities for rainfall changes in winter are somewhat similar to Australia: there are practically no increases in rainfall associated with substantial probabilities and decreases are mainly limited to the southeast of Africa, covering most of South Africa. Here, the threshold probability reaches 100% for more than 0.1 mm/d decreases, but declines quickly and only small areas indicate probable decreases greater than 0.2 mm/d. [44] Similar to Australia, there is a near 100% probability across all of southern Africa for increases above +2 C in both summer and winter (Figure 11b). This is also true for future warming above +3 C for most of the countries. During summer, most of the high probabilities for above +4 C warming are located over the west, encompassing mainly Angola, Namibia, Botswana, Zambia and South Africa, whereas in winter most areas show high probabilities of greater than 4 C warming. In addition, over the Kalahari Desert, there is even a 70% probability of above +5 C warming in winter Selected PDFs for Australian Precipitation and Temperature [45] Using the threshold probabilities from the previous section, probability density functions were calculated for the SRES-A2 scenario, for various regions in Australia and southern Africa. The probability density range for changes in summer temperatures over the entire Australian continent is between 2 C and 6 C (median is 3.9 C) (Figure 12). The shapes of the PDFs for the three regions under study are quite different. While the greenhouse enhanced warming over southwestern Australia shows a fairly narrow PDF (median is 3.6 C), the PDF for the Murray-Darling Basin is spread as wide as the All-Australian one (median is 3.6 C), and the PDF for tropical Australia has a very distinct bimodal PDF with peaks at 3.2 C and 4.2 C (median is 3.7 C). The reason for finding narrow unimodal distributions could be twofold: (1) there is an obvious agreement among models, and/or (2) outlier models are down weighted because of their large bias. The multimodal distributions might be related to the fact that the AOGCMs 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 bimodality for tropical Australia has an obvious physical interpretation, 16 of 26