• Cai, W., and T. Cowan. 2006. SAM and regional rainfall in IPCC AR4 models: Can anthropogenic forcing account for southwest Western Australian winter rainfall reduction? Geophys. Res. Lett. 33.L24708, doi:10.1029/2006GL028037.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H. Coauthors 2007. Regional climate projections. Climate Change 2007: The Scientific Basis, S. Solomon, Ed., Cambridge University Press, 847–940.

    • Search Google Scholar
    • Export Citation
  • Colombo, A., , D. Etkin, , and B. Karney. 1999. Climate variability and the frequency of extreme temperature events for nine sites across Canada: Implications for power usage. J. Climate 12:24902502.

    • Search Google Scholar
    • Export Citation
  • CSIRO 2007. Climate change in Australia. Australian Greenhouse Office Tech. Rep., 152 pp. [Available online at www.climatechangeinaustralia.gov.au.].

  • Cubasch, U. Coauthors 2001. Projections of future climate change. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 525–582.

    • Search Google Scholar
    • Export Citation
  • England, M. H., , C. C. Ummenhofer, , and A. Santoso. 2006. Interannual rainfall extremes over southwest Western Australia linked to Indian Ocean climate variability. J. Climate 19:19481969.

    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C. 2003. Extratropical Southern Hemisphere cyclones: Harbingers of climate change? J. Climate 16:28022805.

  • Hope, P. 2006. Projected future changes in synoptic systems influencing southwest Western Australia. Climate Dyn. 26:765780. doi:10.1007/s00382-006-0116-x.

    • Search Google Scholar
    • Export Citation
  • Hope, P., , W. Drosdowsky, , and N. Nicholls. 2006. Shifts in the synoptic systems influencing southwest Western Australia. Climate Dyn. 26:751764. doi:10.1007/s00382-006-0115-y.

    • Search Google Scholar
    • Export Citation
  • Hughes, L. 2000. Biological consequences of global warming: Is the signal already apparent? Trends Ecol. Evol. 15:5662.

  • IOCI 2002. Climate Variability and Change in South West Western Australia. The Indian Ocean Climate Initiative Panel, 34 pp. [Available from The Indian Ocean Climate Initiative Panel, c/o Department of the Environment, Water and Catchment Protection, Hyatt Place, 3 Plain St., East Perth, WA, 6004 Australia.].

    • Search Google Scholar
    • Export Citation
  • Johns, T. C. Coauthors 2006. The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. J. Climate 19:13271353.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., , G. A. Meehl, , M. R. Allen, , and D. A. Stainforth. 2006. Constraining climate sensitivity from the seasonal cycle in surface temperature. J. Climate 19:42244233.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , F. Zwiers, , J. Evans, , T. Knutson, , L. O. Mearns, , and P. H. Whetton. 2000. Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bull. Amer. Meteor. Soc. 81:427436.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A. Coauthors 2007. Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–843.

    • Search Google Scholar
    • Export Citation
  • Miller, R. L., , G. A. Schmidt, , and D. T. Shindell. 2006. Forced annular variations in the 20th century IPCC AR4 simulations. J. Geophys. Res. 111.D18101, doi:10.1029/2005JD006323.

    • Search Google Scholar
    • Export Citation
  • Murphy, B. F., and B. Timbal. 2007. A review of recent climate variability and climate change in southeastern Australia. Int. J. Climatol. 28:859879. doi:10.1002/joc.1627.

    • Search Google Scholar
    • Export Citation
  • Parkinson, G. 1986. Climate. Vol. 4, Atlas of Australian Resources, Third Series, Commonwealth of Australia, 60 pp.

  • Perkins, S. E., and A. J. Pitman. 2008. Do weak AR4 models bias projections of future climate changes over Australia? Climatic Change submitted.

    • Search Google Scholar
    • Export Citation
  • Perkins, S. E., , A. J. Pitman, , N. J. Holbrook, , and J. McAneney. 2007. Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions. J. Climate 20:43564376.

    • Search Google Scholar
    • Export Citation
  • Piani, C., , D. J. Frame, , D. A. Stainforth, , and M. R. Allen. 2005. Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys. Res. Lett. 32.L23825, doi:10.1029/2005GL024452.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., , G. T. Narisma, , R. Pielke, , and N. J. Holbrook. 2004. The impact of land cover change on the climate of southwest Western Australia. J. Geophys. Res. 109.D18109, doi:10.1029/2003JD004347.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., , G. T. Narisma, , and J. McAneney. 2007. The impact of climate change on Australian bush fire risk. Climatic Change 84:383401. doi:10.1007/s10584-007-9243-6.

    • Search Google Scholar
    • Export Citation
  • Power, S., , B. Sadler, , and N. Nicholls. 2005. The influence of climate science on water management in Western Australia: Lessons for climate scientists. Bull. Amer. Meteor. Soc. 86:839844.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A. Coauthors 2007. Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

    • Search Google Scholar
    • Export Citation
  • Shukla, J., , T. DelSole, , M. Fennessy, , J. Kinter, , and D. Paolino. 2006. Climate model fidelity and projections of climate change. Geophys. Res. Lett. 33.L07702, doi:10.1029/2005GL025579.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., , R. T. Wetherald, , G. L. Stenchikov, , and A. Robock. 2002. Global cooling after the eruption of Mount Pinatubo: A test of climate feedback by water vapor. Science 296:727730.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., , D. Qin, , M. Manning, , Z. Chen, , M. Marquis, , K. B. Averyt, , M. Tignor, , and H. L. Miller. 2007. Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

    • Search Google Scholar
    • Export Citation
  • Sun, Y., , S. Solomon, , A. Dai, , and R. W. Portmann. 2007. How often will it rain? J. Climate 20:48014818. doi:10.1175/JCLI4263.1.

  • Tanaka, H. L., , N. Ishizaki, , and D. Nohara. 2005. Intercomparison of the intensities and trends of Hadley, Walker and monsoon circulations in the global warming projections. SOLA 1:7780.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E. 2001. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106:D7. 71837192.

  • Timbal, B., and J. M. Arblaster. 2006. Land cover changes as an additional forcing to explain the rainfall decline in the south west of Australia. Geophys. Res. Lett. 33.L07717, doi:10.1029/2005GL025361.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E. 1998. Atmospheric moisture residence times and cycling: Implications for rainfall rates with climate change. Climatic Change 39:667694.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., , A. Dai, , R. M. Rasmussen, , and D. B. Parsons. 2003. The changing character of precipitation. Bull. Amer. Meteor. Soc. 84:12051217.

    • Search Google Scholar
    • Export Citation
  • Trigo, R. M., , R. García-Herrera, , J. Díaz, , and I. F. Trigo. 2005. How exceptional was the early August 2003 heatwave in France? Geophys. Res. Lett. 32.L10701, doi:101029/2005GL022410.

    • Search Google Scholar
    • Export Citation
  • Watterson, I. G. 1996. Non-dimensional measures of climate model performance. Int. J. Climatol. 16:379391.

  • Whetton, P. H., , K. L. McInnes, , R. N. Jones, , K. J. Hennessy, , R. Suppiah, , C. M. Page, , J. Bathols, , and P. J. Durack. 2005. Australian Climate Change Projections for Impact Assessment and Policy Application: A Review. Climate Impact Group, CSIRO Marine and Atmospheric Research, 34 pp.

    • Search Google Scholar
    • Export Citation
  • Yin, J. H. 2005. A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett. 32.L18701, doi:10.1029/2005GL023684.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Location map of Australia showing state boundaries, major cities, and the regions discussed in the text.

  • View in gallery

    PDF of TMAX from three climate models for region 2 (see Table 2) for the present day and for 2100 showing a very high degree of overlap between the present and future for a given climate model. Solid lines are for the present day, dashed are for 2100. MIROC-m is in blue, MRI in red, and CSIRO in black. All lines are smoothed for visual clarity.

  • View in gallery

    Change from the present climate in the averaged daily (°C) over Australia simulated for the B1 emission scenarios for (a) spring (2050), (b) summer (2050), (c) autumn (2050), (d) winter (2050), (e) annual (2050), (f) spring (2100), (g) summer (2100), (h) autumn (2100), (i) winter (2100), and (j) annual (2100). Only models with a skill score >0.8 are included (see text).

  • View in gallery

    As in Figure 3, but for the A2 emission scenario.

  • View in gallery

    As in Figure 3, but for the change in TMAX99.

  • View in gallery

    As in Figure 4, but for the change in the TMAX99.

  • View in gallery

    As in Figure 3, but for the change in the frequency of TMAX99 (days per season or days per year).

  • View in gallery

    As in for Figure 7, but for the A2 emission scenario.

  • View in gallery

    Changes in (left column) , (middle column) TMAX99, and (right column) the frequency of TMAX99. The bars represent the range of projections within each ensemble. The five regions (see Table 2) are, from the top, region 1, region 2, region 3, region 10, and region 11. The number of models included in each period is shown in each panel. The first four bars are for B1 (2050), followed by A2 (2050), then B1 (2100), and finally A2 (2100).

  • View in gallery

    As in Figure 3, but for .

  • View in gallery

    As in Figure 4, but for .

  • View in gallery

    As in Figure 3, but for the change in TMIN0.3.

  • View in gallery

    As in Figure 4, but for the change in TMIN0.3.

  • View in gallery

    As in Figure 7, but for TMIN0.3.

  • View in gallery

    As in Figure 8, but for TMIN0.3.

  • View in gallery

    Changes in (left column) , (middle column) TMIN0.3, and (right column) the frequency of TMIN0.3. The bars represent the range of projections within each ensemble. The five regions (see Table 2) are, from the top, region 1, region 2, region 3, region 10, and region 11. The number of models included in each period is shown in each panel. The first four bars are for B1 (2050), followed by A2 (2050), then B1 (2100), and finally A2 (2100).

  • View in gallery

    As in Figure 3, but for P (mm day−1).

  • View in gallery

    As in Figure 17, but for the A2 emission scenario (mm day−1).

  • View in gallery

    As in Figure 5, but for P99 (mm day−1).

  • View in gallery

    As in As in Figure 6, but for P99 (mm day−1).

  • View in gallery

    As in Figure 7, but for the frequency of P99 (mm day−1).

  • View in gallery

    As in Figure 8, but for the frequency of P99 (mm day−1).

  • View in gallery

    As in Figure 5, but for the change in no-rain days (daily rainfall amounts less than 0.2 mm day−1).

  • View in gallery

    As in Figure 23, but for the A2 emission scenario (mm day−1).

  • View in gallery

    Changes in (left column) P, (middle column) P99, and (right column) the frequency of P99. The bars represent the range of projections within each ensemble. The five regions (see Table 2) are, from the top, region 1, region 2, region 3, region 10, and region 11. The number of models included in each period is shown in each panel. The first four bars are for B1 (2050), followed by A2 (2050), then B1 (2100), and finally A2 (2100).

  • View in gallery

    As in Figure 9, but for the change in frequency of rain-free days (daily rainfall amounts less than 0.2 mm day−1). (top left) Region 1, (top right) region 2, (middle left) region 3, (middle right) region 10, and (bottom left) region 11.

  • View in gallery

    Relationship between intermodel range of climate models’ simulation of , , and P and the models’ simulation of the range in TMAX99, TMIN0.3, and P99. This is the length of the bars shown in Figures 9 and 16 (column 1 vs column 2). The individual dots are color coded: blue denotes region 1; red denotes region 2; green denotes region 3; orange denotes region 10; pink denotes region 11.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 129 129 8
PDF Downloads 89 89 8

Regional Projections of Future Seasonal and Annual Changes in Rainfall and Temperature over Australia Based on Skill-Selected AR4 Models

View More View Less
  • 1 Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia
© Get Permissions
Full access

Abstract

Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.

* Corresponding author address: Professor A. J. Pitman, Climate Change Research Centre, Red Centre Building, University of New South Wales, Sydney, NSW, 2052 Australia.

a.pitman@unsw.edu.au

Abstract

Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.

* Corresponding author address: Professor A. J. Pitman, Climate Change Research Centre, Red Centre Building, University of New South Wales, Sydney, NSW, 2052 Australia.

a.pitman@unsw.edu.au

1. Introduction

Reliable projections of the impact of increasing greenhouse gases on the global, continental, and regional scale remains a major scientific challenge. At the heart of climate projections are coupled climate models. These tools underpin assessments by the Intergovernmental Panel on Climate Change (IPCC) including the recent Fourth Assessment Report (AR4; Solomon et al. 2007). Climate models are based on well-established physical principles, and Randall et al. (Randall et al. 2007) conclude that there is now considerable confidence that coupled climate models provide credible quantitative estimates of future climate change, particularly at continental scales and above.

For impact assessment, projections are needed for planning, for providing specific advice for adaptation, or for gauging the impact of climate changes on biophysical, human, or economic systems. One reason why climate models are deemed “credible” at “continental scales and above”—and therefore, at least by implication, less credible at subcontinental scales—is that models vary in their response to increasing greenhouse gas concentrations at regional scales. Some of this variability is due to limits in the capacity of some climate models to capture the observed climate of a region. A variety of measures of climate model skill have been developed. For example, Watterson (Watterson 1996), Taylor (Taylor 2001), Knutti et al. (Knutti et al. 2006), Piani et al. (Piani et al. 2005), Shukla et al. (Shukla et al. 2006), and Johns et al. (Johns et al. 2006) all provide measures of model skill, but typically using monthly to annual time-scale data, sometimes over ensemble means of several climate models. Further, some measures of model skill, while useful for building a general view of a climate model’s capacity, are not directly relevant to impact assessment on human, social, or economic systems (e.g., entropy as used by Shukla et al. 2006).

Perkins et al. (Perkins et al. 2007) used the probability density functions (PDFs) of daily maximum temperature (TMAX), daily minimum temperature (TMIN), and daily rainfall (P) from each AR4 model and compared these to daily observed data to identify, region by region over Australia, the level of skill each AR4 model showed. A map of these regions and the locations of states referred to in this paper are shown in Figure 1. An advantage of Perkins et al.’s (Perkins et al. 2007) method is, by using a skill score over a whole PDF, models can be compared across whole distributions rather than at a selected a priori point such as the mean. The measure then compares the observed and simulated probabilities to give an overall performance score for each climate model. Thus, using daily data, region by region for TMAX, TMIN, and P, Perkins et al. (Perkins et al. 2007) ranked the AR4 models using the PDF-based skill score to show that the Model for Interdisciplinary Research on Climate, medium-resolution version (MIROC-m), Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Meteorological Research Institute (MRI) performed best overall (Table 1 lists the details of each model).

In this paper we use this skill score to select the better models, and to explore how these selected models project future climate over Australia. Our goal is to provide as reliable a set of projections for three key variables over Australia as is possible at this time. Fundamental to this paper is our assertion that a model that is able to simulate the PDF of a variable well for the twentieth century is likely to be able to simulate the future PDF of the same variable well. Clearly, we cannot prove this assertion because we cannot know the future climate. However, consider a model that has a high level of skill in simulating the current PDF of daily TMAX. This model must be able to simulate the drivers and associated feedbacks for the current climate. To simulate the observed PDF, the model must capture, at a daily time scale, the interactions between the surface, boundary layer, clouds, and radiation well, else the PDF would be biased toward high values (too little soil moisture, too little evaporation, too little cloud, etc.) or low values (too much surface moisture, high evaporation and associated cloud leading to too little radiation). It is hard to imagine a model capturing the observed PDF of daily TMAX with a high degree of skill fortuitously. Now, imagine the PDF for TMAX for 2100. There will be considerable overlap between this future PDF and the current PDF. This is illustrated in Figure 2 for three models for a 10° × 10° region (region 2; see Table 2). The solid lines show the PDF of daily TMAX for 20 years centered on 1990. The dashed lines show the PDF of daily TMAX for 20 years centered on 2090. In each case there is a shift to the right of the model PDF. This shift is in the upper and lower tails for the CSIRO model, in the whole distribution for MRI and in the lower tail and midrange for MIROC-m. However, in each case the overlap of the two PDFs for a given model remains very high (the amount of overlaps is CSIRO = 0.92, MIROC-m = 0.94, and MRI = 0.95).

Within this region where the current and future PDFs overlap is a region of physical and biophysical climate space where the model has already demonstrated that it can capture the processes and feedbacks. The demonstration that a model has skill in this overlap region gives us more confidence that it can capture these processes and feedbacks in the future than demonstrating that a model can simulate the observed mean. As the change in the PDF increases such that the overlap is reduced, our confidence may decline, but Earth would be uninhabitable well before this overlap becomes negligible (mean temperature would need to exceed 60°C). We cannot take this logic too far since if the climate reorganizes on longer time scales (El Niño becomes permanent, for example), skill in the present may be a poor guide to skill in the future.

There is a rich history of using climate models to project the future climate of Australia. These have become outdated as the science and the climate models evolve, and hence we do not provide a thorough review of earlier studies (see Whetton et al. 2005). In a recent assessment of warming on the Australian climate using the AR4 models, Christensen et al. (Christensen et al. 2007) state the following:

All of Australia…[is] very likely to warm during this century…comparable overall to the global mean warming. The warming is smaller in the south, especially in winter…Increased frequency of extreme high daily temperatures [will occur] in Australia…and [a] decrease in the frequency of cold extremes is very likely; and precipitation is likely to decrease in Southern Australia in winter and spring. Precipitation is very likely to decrease in Southwestern Australia in winter…Changes in rainfall in Northern and Central Australia are uncertain. Extremes of daily precipitation will very likely increase. The effect may be offset or reversed in areas of significant decrease in mean rainfall (southern Australian in winter and spring).

Perkins et al. (Perkins et al. 2007) demonstrate that an ensemble of all AR4 models would include some climate models with very poor performance over Australia in comparison with observations. It is important to clarify whether some of the apparently robust results in Figure 11.17 in Christensen et al. (Christensen et al. 2007; rainfall change projected by the AR4 models over Australia) are reliable or biased by weak models. For example, is the projected decrease in rainfall over Western Australia of 10%–20% in the annual total robust? CSIRO and the Bureau of Meteorology (BOM) in Australia have also provided detailed assessments of the future climate of Australia based on the AR4 models (hereafter CSIRO 2007). Their assessment evaluates models using seasonal mean temperature, precipitation, and sea level pressure. This is, of course, valid particularly when the subsequent analysis is restricted to changes in seasonal means. However, CSIRO (CSIRO 2007) then use all the AR4 models for rainfall and a subset for warm nights and frosts, weighted by the capacity of the models to simulate seasonal climate. Again, these risks including biased models, which may lead to unrealistic projections, while using seasonal performance and then examining projections of rarer events, may also introduce biases. We assess the models on the time scales that we then use to explore the projections.

Our approach therefore contrasts with Christensen et al. (Christensen et al. 2007) by excluding models that are unable to simulate the observed PDFs obtained using daily data. Our approach also contrasts with CSIRO (CSIRO 2007) who weight models based on seasonal performance. Since we wish to focus, in part, on changes at the tails of the PDFs, obtained and evaluated using daily data, we wish to exclude models unable to capture other parts of the distribution as well as the mean to provide a more robust estimate of how TMAX, TMIN, and P may change. Most of all, however, we wish to assess whether the projections provided by Christensen et al. (Christensen et al. 2007) are similar to those obtained using different selection and evaluation methodologies.

In this paper, we therefore provide projections of climate change over Australia. We use the methodology of Perkins et al. (Perkins et al. 2007) to select those models with demonstrated skill in simulating the daily PDFs of three variables: P, TMIN, and TMAX region by region over Australia. We only include those AR4 models that can capture at least 80% of the overlap (a skill score >0.8) in the daily PDFs between the observed and modeled distributions. We provide the projected changes in the mean for all three variables, the 99.7th percentile (for TMAX, P), and 0.3rd percentile (for TMIN). These percentiles equate to events that occur approximately once per year. The choice of these variables and statistics was based on available data and on their role in driving many human, industrial, and biological systems (Colombo et al. 1999; Meehl et al. 2000; Trigo et al. 2005). We denote the ensemble mean of TMAX, TMIN, and P as , , and P and the percentiles as TMAX99, TMIN0.3, and P99, etc. We provide a methodology in section 2, results in sections 35, discussion in section 6, and conclusions in section 7.

2. Data and methods

2.1. Data

Daily climate model data over Australia for P, TMIN, and TMAX were taken from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archive (http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). Data from 1981–2000 from the Climate of the Twentieth Century simulations were used as the control (these are fully discussed in Perkins et al. 2007). In this paper we also use results from the B1 (relatively low emissions) and A2 (relatively high emissions) scenarios for two time periods: a 20-yr time period from 2046 to 2065 (hereafter 2050) and a 20-yr period from 2081 to 2100 (hereafter 2100). These time periods were common to all models. By using daily data we retain the maximum time resolution possible and minimize the hiding of biases through averaging.

Some datasets contained erroneous data (gaps, periods of repetitive data), or data were not available at the time this study was undertaken, and were therefore omitted from subsequent analysis. Table 1 lists all models used. When calculating differences in means and yearly returns, only models that had data for both the twentieth-century simulation and the respective twenty-first-century simulation and time period could be used. We used each independent realization directly in the initial analysis rather than average these realizations to produce an ensemble. However, we present our best estimates of the multimodel ensemble over included models. In selected regions, we highlight the differences between models included in the ensemble results.

Daily observed P, TMIN, and TMAX were obtained from BOM for the period 1981–2000. The use of the observed data is fully discussed in Perkins et al. (Perkins et al. 2007).

2.2. Skill score

Perkins et al. (Perkins et al. 2007) present a simple skill score to rank the AR4 climate models over Australia. This metric measures the similarity between two PDFs by calculating the cumulative minimum value of two distributions of each binned value. This measurement of the common area between two PDFs provides a skill score that will equal 1.0 where the modeled and observed PDFs are identical and 0.0 where the two PDFs fail to overlap at all. Perkins et al. (Perkins et al. 2007) demonstrate that this measure is robust against uncertainty in the temporal and spatial coverage of the observations. Following Perkins et al. (Perkins et al. 2007) bin sizes were 0.5°C for TMAX and TMIN and 1 mm day−1 for P. The ranges of the bins were dependent on the range of the observed data for each region. All daily values of P below 0.2 mm day−1 were set to 0.0 because rates below this amount are not recorded in the observations (Parkinson 1986).

In this paper we use the skill scores obtained by Perkins et al. (Perkins et al. 2007) as the basis for omitting models. We omit models based using a threshold of 0.8. The choice of this was subjective, balancing the desire to only include those climate models with demonstrated skill, while recognizing that the sample size of models needs to be kept reasonable. Had we chosen a skill score of 0.6 virtually no models would be excluded while 0.9 would mean virtually no models were included.

2.3. Calculation of continent ensemble and regional statistics

This study highlights the change in , , P, TMAX99, TMIN0.3, P99 as well as the change in the frequency of a twentieth-century value for the respective percentiles and the change in the number of no-rain days. The 99.7th and 0.3rd percentiles were chosen as they represent an event that occurs once every year, on average, in the present day. The respective statistics were calculated for A2 and B1 for 2050 and 2100 for all seasons. When calculating the change in frequency of a one-in-one-year twentieth-century value, the number of times an event of the same magnitude or greater was counted in the twenty-first-century dataset and divided by 20 to give the mean yearly occurrence. All statistics were calculated at a regional level for regions 1, 2, 3, 10, and 11 (see Figure 1 and Table 2) and at a continental scale. These regions were chosen because they represent different climatic types (Table 2) and contain most of Australia’s population (6 out of 8 capital cities). When a model provided more than one realization, data were averaged to form a model ensemble prior to statistical calculation since Perkins et al. (Perkins et al. 2007) found very little difference in skill between a model’s realizations.

At a regional level, all model data that were contained by the specified region’s boundaries were concatenated. Models that demonstrated a skill of <0.8 defined by Perkins et al. (Perkins et al. 2007) were then omitted. This means that the number of models in the ensemble varies from region to region and across variables. However, this is kept constant (region specific) throughout the seasons, twenty-first-century time periods, and scenarios. Perkins and Pitman (Perkins and Pitman 2008, manuscript submitted to Climatic Change) identify the amounts the specific projections change given a choice of a specific skill level.

At the continental scale, all statistics were calculated at the models’ native resolution for each scenario and each time period. Model skill masks were also constructed at the native resolution. This comprised using the skill score for each region and fitting the values to a land–sea mask of the model. This maintained the exact skill values by avoiding interpolation. The skill masks were fitted to the model such that grid boxes within regions with a skill <0.8 were changed to missing values. All model data was then interpolated to a common 1° × 1° grid. The continental ensemble was created by simply averaging the interpolated statistics, giving the mean statistic value across the “more skilled” models at each grid box. Continental results for each season, scenario, and time period are presented as maps and regional results as stock plots to illustrate the range of values across the ensemble.

3. Simulation of changes over Australia ()

3.1. Mean maximum temperature

Figure 3 shows the results for for 2050 and 2100 for the B1 emission scenario. In 2050, warming exceeding 1.0°C is projected over virtually all the continent in all seasons with the exception of southeastern Australia. In spring, warming exceeds 1.5°C almost everywhere, exceeding 2°C in central regions (Figure 3a). A similar result occurs in summer (Figure 3b). Few areas warm by more than 1.5°C in autumn (Figure 3d). Warming is generally 1.0°–1.5°C in the southern half and 1.5°–2.0°C in the northern half of Australia in winter.

By 2100, the increase in is substantially higher. Warming exceeds 3°C in central regions in spring (Figure 3f) and widespread regions experience 2.0°–2.5°C increases in spring, summer (Figure 3g), and winter (Figure 3i). The least amount of warming occurs in autumn (Figure 3h), but warming still exceeds 1.5°C everywhere except for areas of high orography in the east and southeast. Clearly, the annual average (Figure 3j) hides significant seasonality and geographical variability in the response to warming. There is about 0.5°–1.0°C additional warming between 2050 and 2100.

Under A2 (Figure 4) additional warming in is projected in most regions. By 2050, spring warming (Figure 4a) reaches 3°C in western regions. In summer (Figure 4b), warming reaches about 1.0°–1.5°C over Queensland (this is less than under B1) but 2.0°–2.5°C in the western regions which is higher than under B1. This is duplicated in autumn (Figure 4c). In winter, warming is mainly 2.0°–2.5°C, a full 1°C higher than under the B1 scenario. The lack of large differences in greenhouse gas concentrations by 2050 between the B1 and A2 emission scenarios coupled with natural variability in the models explains how summer warming in some regions is lower under the A2 scenario than B1.

By 2100, under a high-emissions scenario, warming of at least an additional 2°C occurs in comparison to 2050 and in comparison to results for 2100 under the B1 scenario. In spring, large areas of warming exceed 4°C and exceed 5°C in Western Australia. The least warming simulated exceeds 2.5°C (over the northern Queensland coast and the extreme southern area of Victoria). A generally similar result occurs in summer (Figure 4g) and winter (Figure 4i). In autumn parts of Queensland experience slightly less warming in the coastal region, but this still generally exceeds 2°C.

3.2. 99.7th percentile of maximum temperature (TMAX99)

An analysis of the change in TMAX99 illustrates how depending on changes (section 3.1) can mislead. Figure 5a shows the change in TMAX99 in spring under B1. While the northern half of Australia experiences a similar change to the (Figure 3a), the southern half displays increases of about 4°C (double the increase in ). A similar result is seen in summer (Figure 5b) and autumn (Figure 5c). In summer, the region of higher rises in TMAX99 extends northward through New South Wales and into Queensland. In winter, while increases by 1.0°–2.0°C, TMAX99 increases by 2.5°–3.5°C over wide regions.

This basic pattern is reproduced for 2100 under B2. Along the southern coast of Australia warming in TMAX99 exceeds 5°C in spring, summer, and autumn. This is closely related to the rainfall pattern discussed later. Most of the Murray–Darling Basin (MDB) experiences significant warming of more than 3.5°C in spring and winter that is likely to negatively impact on a basin which is already undergoing major challenges with respect to water availability.

Under A2, increases in TMAX99 are higher over southeastern Australia in spring and winter compared to B1 (Figure 6). Warming of 2.0°–2.5°C is widespread. Smaller increases occur elsewhere in spring (Figure 6a). Local increases exceeding 4.0°C occur over Victoria. In summer (Figure 6b) and autumn (Figure 6c) increases in TMAX99 exceeding 1°C are isolated to southeastern Australia and Western Australia (Figure 6b). However, increases exceeding 3.5°C occur over Victoria. Indeed, each season shows anomalously high increases over southern Victoria commonly exceeding 3.5°C and locally exceeding 5°C.

Significant areas of Australia are projected to warm by less than 1°C in TMAX99 by 2050 under A2 (Figures 6a–e). This result is quite noticeable in comparison to B1 (Figures 5a–e). However, a close inspection of these figures shows that the differences are small: while the A2 future does project less warming, it is only slightly less. By 2100, however, the amount of warming in TMAX99 is confronting (Figures 6f–j). Widespread areas warm by more than 5.0°C, particularly over the south-east, south, and western parts of the continent. This occurs in all seasons but is most dramatic in winter and spring.

Figure 7 shows the change in the frequency of the value of TMAX99 that currently occurs once per year on average (1981–2000). Under B1, this value increases in frequency by 2050 to 5–10 times per year in summer over large areas of northwestern Australia and by 2–4 times elsewhere (Figure 7b). In spring (Figure 7a) and winter (Figure 7d) widespread increases of 2–3 times and local increases of 3–5 times are common. By 2100, however, most of the country experiences the current TMAX99 4–10 times per year in summer (Figure 7g) and 4–5 times over eastern states and 5–10 times over western states in summer and winter (Figures 7g,i).

Under A2, the changes are highly variable by 2050 (Figure 8). The frequency over the northwest increases much more than under B1 in spring, but in many other areas the increase in frequency is either similar or less. Broadly, at least in terms of adaptation to large-scale climate change, the two scenarios for 2050 are similar. The frequency of the current TMAX99 increases quite dramatically by 2100, however. In spring, the current once per year event occurs 5–10 times over widespread areas (Figure 8f) and 20–40 times over the northwest region of the continent. This is 3 times the change projected under B1. Similarly, in summer, the current annual event occurs more than 10 times per year over the entire continent except the extreme southeast (Figure 8g). By winter, the major changes over Western Australia (Figure 8i). Overall, it is the dramatic increase in extreme summer temperatures that is most confronting. At present, is around 36°C in inland New South Wales (NSW). Ten to twenty days a year of this extreme temperature is not something that would be easy to adapt to and would be confronting in terms of fire hazard (Pitman et al. 2007) and the impacts on flora and fauna (Hughes 2000).

3.3. Regional change in the magnitude and frequency of maximum temperature

Our projections are based on only those climate models that can capture over 80% of the observed PDF of a variable over a given region (see Figure 1). Despite this, there is still high intermodel variability in the projections of changes (i.e., skillful models may simulate the twentieth century similarly, but can still project very different future climates). This is problematic since there is no a priori reason to accept the mean change in climate of n models over any individual model when each individual model has been chosen on the basis of good performance.

Figure 9 shows the change in , TMAX99, and the frequency of TMAX99 for each season and for each emission scenario. Each bar shows the range of warming projected by the total sample of the included models. While this is by no means a measure of uncertainty in the projections, it provides a guide as to how variable the good models are in each region. The number of models in each sample varies by region but is the same in each variable shown in Figure 9. Thus, the range in projections cannot be compared across regions, but can be compared between the changes in with respect to the change in TMAX99 or the frequency of TMAX99.

In region 1, for each scenario, the models agree the most on the increase in winter , and agree least on summer warming. There is 2–3 times more variability among the models in summer compared to winter in . Warming is consistently higher in summer and spring. The amount of warming by 2100 under A2 is very clear relative to B1. The amount of uncertainty in the projections does change between scenarios but not in a way that is clearly associated with either time or emissions. In region 2, tends to be highest in winter and spring. While the higher amount of warming by 2100 under A2 is clear, the amount of variability between the models is higher than in region 1. In region 3, there is no clear seasonal pattern to warming and A2 (2100) again shows higher warming. In region 10, there is substantially less variability among the models. Most warming tends to occur in winter and spring. There appears to be a very strong response to the emission scenario with high emissions driving considerably more warming in both 2050 and 2100 than low emissions. Finally, in region 11, warming in is greater in summer and spring, leading to a pattern similar to region 1 but with substantially more warming.

Figure 9 also shows a common pattern of change in TMAX99 across all regions. For a given region, the increase in TMAX99 is therefore similar for both time periods for B1 emissions and for A2 (2050) emissions. There is generally a larger increase in TMAX99 under the A2 scenario by 2100; this is particularly clear in regions 10 and 11. In all regions, the amount of model agreement on the projected change in TMAX99 is noticeably less than for . It is also noteworthy that some of the skillful climate models simulate a reduction in the magnitude of TMAX99, almost always in summer and autumn and despite (Figure 9) always increasing. This reduction occurs through to 2100 under A2 emissions in regions 1 and 2 (see section 6).

The change in the frequency of TMAX99 is also shown in Figure 9. In regions 1, 2, and 3 the differences in the changes in frequency are small until 2100 under the A2 scenario. Within a region, there is no clear seasonality in the changes in the frequency; however, under A2 (2100) a noticeable increase in the frequency of TMAX99 occurs. In region 10, however, there are clear differences in the changes in TMAX99 with emission scenario and time period. The highest changes occur in winter and spring. Substantially larger increases occur under high emissions by 2100, but the variability among the models is also very high. For example, in 2100 (A2) one model projects an increase in the frequency of TMAX99 from 1 time per year to 5 times per year, while another projects over 40 times per year (summer). Region 11 provides a similar pattern of changes to region 1.

4. Simulation of changes over Australia (TMIN)

4.1. Mean minimum temperature ()

Under B1, warms in a consistent pattern (Figure 10). By 2050, warming is 1.5°–2.0°C in the continental interior and mainly 1.0°–1.5°C near the southern coast. Less than 1.0°C is simulated in winter along the southern coast (Figure 10d) and in the extreme north in autumn (Figure 10c). This warming pattern is largely reproduced in 2100, but the amount of warming in is increased by 0.5°C. The greatest warming is projected in spring (Figures 10a,f) and the least warming in winter (Figures 10d,i).

Under A2, warming in is generally higher by 2050 than under B1, but the additional warming is at most 0.5°C. By 2100, the pattern of warming in is more dramatic. In spring and summer large areas warm by 3.5°–4°C. In autumn and winter, similar increases are identified in the northern half of the continent. Farther south, warming is less, but is at least 2°C along the southern coast. Thus, at best, A2 generates an additional 0.5°C warming in isolated regions but across most of the continent; a comparison of Figures 11f–i identifies large regions of more than 2°C additional warming under A2 in comparison with B1 (Figures 10f–i).

4.2. 0.3rd percentile of minimum temperature (TMIN0.3)

Figure 12 shows the change in TMIN0.3 that occurs on average once per year (this is the cold event, at the lower end of the distribution). Under the B1 scenario TMIN0.3 generally warms by less than 1.5°C. Large areas warm by less than 1°C (e.g., Figure 12c). By 2100, the warming is more distinctive (Figures 12f–i) but is generally less than 2°C. There are local areas of additional warming, but these do not exceed 3°C.

Figure 13 shows the results for the high-emission scenario. By 2050, there is larger warming over many regions, particularly in spring. The patterns show the maximum changes in the northern half of Australia so this is not related to snow feedback (snow is isolated to the southeast over high orography). By 2100 the warming in TMIN0.3 is much higher. Large areas show an increase in this value of 3.5°–4.0°C and the smallest increases exceed 1.5°C most seasons. The increase in this event is approximately double for 2100 under A2 in comparison to B1.

Figure 14 shows the change in the frequency of TMIN0.3 under B1 emissions. Values less than 1.0 indicate the event becomes less frequent (0.5 means 50% occurrence per year, 0.2 means 20% occurrence per year, 0.01 means 1% occurrence per year, etc.). In general, in summer, autumn, and winter, the frequency of TMIN0.3 decreases such that it occurs half as often. In summer (Figure 14a) some regions in Western Australia undergo a larger change such that the current once per year minimum occurs only once per decade. By 2100 this is more confronting with more regions experiencing a change of this scale in spring (Figure 14f) and locally in other seasons.

Under A2 (Figure 15) the results are similar by 2050 to B1 (Figure 14). However, by 2100 there is a large-scale change in the frequency of the current TMIN0.3 that is substantially larger than under B1. Figure 15f shows, in spring for example, large areas of Australia no longer experience a daily minimum of the current value that occurs annually. Only over Victoria is the current value experienced more than once per decade. Similarly, in winter, the current annual minimum is only ever experienced by 2100 over the southern half of the continent and it is rare—mainly once every 4 years.

4.3. Regional change in the magnitude and frequency of minimum temperature

The change in shows a clear time-dependent increase in all regions (Figure 16). The substantially higher increase in is particularly visible by 2100 under A2. Given is largely constrained by incoming infrared radiation and radiative cooling at night, a seasonal variation in the amount of warming is not to be expected, and Figure 16 shows that in all regions the amount of increase in is largely independent of season. Figure 16 shows that for the change in TMIN0.3, there is little difference between the scenarios until 2100 under A2 where the increase is clearer. While the average increase in TMIN0.3 is always positive, some individual models project a reduction in the percentile value through to 2100. As with TMAX99, there is more variability among the models in the percentile than in the mean. This is most clear in region 3 where there is a very high variation between the models’ projection of the change in TMIN0.3 in all seasons, but most clearly in autumn. There is also considerable uncertainty in the projected change in the frequency of TMIN0.3. Figure 16 shows that models generally project a reduction in the frequency of TMIN0.3 as expected. In all regions except region 10 (the tropics), the average result is 0.3–0.5 (i.e., the current annual event occurs once every two to three years) under both B1 and A2 to 2050. In these regions, this decreases to a much rarer event by 2100 under A2. However, there are clear anomalies (see region 3, autumn, A2, 2100). In region 10, the reduction in the frequency of TMIN0.3 is less distinct and the mean response is close to 1.0 (no change) in many seasons until 2100 (A2).

5. Simulation of changes over Australia (P)

5.1. Mean rainfall (P)

Figure 17 shows the change in P under B1. Increasing P over most of the continent is clear in spring (Figure 17a), summer (Figure 17b), and winter (Figure 17d) with more variable changes in autumn (Figure 17c). Given that Australia is the driest inhabited continent and is currently in severe drought, this result might appear encouraging, but the increases are small (rarely in excess of 0.5 mm day−1). Through to 2100, areas of P increase contract to northern regions in spring (Figure 17f) and the eastern half of the continent in summer (Figure 17g). Drying over Victoria in spring also occurs (Figure 17f).

The projections under A2 (Figure 18) in 2050 also show widespread increasing P, particularly over the northern and eastern regions of the continent, in all seasons except winter. Over the tropics and Queensland, this exceeds 0.5 mm day−1 in summer (Figure 18b). Overall, this is seen in the annual average (Figure 18e). A tendency toward drying is apparent along coastal Victoria in spring and winter and clear winter drying is visible in southwest Western Australia in winter (Figure 18d). By 2100 a pattern of spring, autumn, and winter drying has become clearly established along the eastern, southern, and western coasts, extending inland to include the Murray–Darling Basin in winter and spring and including most of Western Australia in autumn and winter. Increased P in the tropics and eastern subtropics occurs in all seasons except winter. The overall effect is visible in Figure 18j which shows coastal drying affecting all significant urban centers in the country except Brisbane. The amount of reduction in P along the coasts is not enormous (mainly a reduction of 0.1–0.5 mm day−1). However, combined with increased evaporative demand and higher temperatures this is likely to exacerbate drought and further stress urban water supply.

5.2. 99.7th percentile of rainfall (P99)

The change in P99 reflects the change in the event that currently occurs on average once per year in the models. Figure 19 shows that the larger events increase in intensity (i.e., the size of the annual event becomes larger) over most of Australia by 2050 under B1. However, the magnitude of this increase is small and is limited to less than 10 mm day−1 almost everywhere. The exception is the tropics where in summer (wet season, Figure 19) increases of 10–30 mm day−1 occur. By 2100, the increase in P99 is similar in most regions (increases of less than 10 mm day−1). Across the tropics, there is again evidence of a larger increase, extending later in the year into winter (Figure 19i). A surprising result occurs in spring. There is a hint of a decrease in 2050 (Figure 19a) over Western Australia that is maintained through to 2100.

Figure 20 shows a very similar result for 2100 under A2 to 2050 under B1. By 2100, there is further intensification of rainfall in summer in the tropics (Figure 20g) exceeding 30 mm day−1. The similarity in the change in P99 under A2 is quite striking compared to low emissions except in winter. The increases in intensity seen under B1 in the north and east are not apparent under A2. However, there are also reductions in Western Australia in winter that are not apparent under B1 (cf. Figures 20i and 19i). Thus, the reductions in winter rainfall shown in Figure 18i (A2, 2100) and absent in Figure 17i (B1, 2100) are likely due to declines in higher precipitation events.

5.3. Change in the frequency of P99

Figure 21 shows the change in the frequency of the current P99. Values higher than 1.0 identify locations where the existing annual rainfall event becomes more frequent. Figures 21a–d show that over most of Australia the current annual event becomes more common such that it occurs 2–4 times per year. Parts of the tropics see a small decrease in the frequency of P99. This tendency to an increased frequency of P99 becomes clearer in 2100 (Figures 21f–i). Under A2 (Figure 22) the changes by 2050 are similar to those under B1. There are differences in detail, but these are minor. The impact of A2 to 2100 leads to decreasing frequency of the annual event over much of the east and northeast coasts, particularly in summer (Figure 22g) and autumn (Figure 22h). However, these decreases are not particularly strong.

5.4. Change in the frequency of no-rain days

Figure 23 shows a highly regional pattern of changes in rain-free days over Australia. The pattern is strongly seasonal; for example, there are more rain days by 2050 in the inland areas in spring (Figure 23a) and in the tropics in winter (Figure 23d). In summer and autumn the impacts are small and mainly coastal (Figures 23b,c). In spring and winter, there are decreases in rain days over Victoria with significant areas affected by reductions of 5–10 days. Larger changes are visible by 2100 (Figures 23–i). Inland, Queensland seems slightly less affected by 2100 with the reductions in rain days being small. In contrast, by 2100, there is a strong reduction in rain days in the northwest quarter of the continent (by 5–10 days) in summer and autumn. The increase in rain-free days over southwest Western Australia visible in spring and winter over Victoria and southern New South Wales highlights a confronting reduction in rain days, commonly exceeding 5–10 days.

The pattern of changes under A2 (Figure 24) for 2050 is very similar to the changes shown under B1 for 2100 (Figures 23f–i). These intensify further through to 2100 (Figures 24f–i). By 2100, all of Australia experiences a reduction in rain days in summer (5–10 days) and autumn (mainly 5–10 days, except over the south coast where reductions reach 10–20 days). In winter the impact is stronger. The whole southern half of Australia sees reductions of 10–20 days and southwest Western Australia and small regions of Victoria show reductions of 20–30 days. The negligible changes simulated over the tropics in winter in 2050 and 2100 under B1 and in 2050 under A2 changes by 2100 to a small (2–5) reduction in rain days.

5.5. Regional change in the magnitude and frequency of rainfall

Figure 25 shows the range of individual model projections for the mean change in precipitation for the five selected regions. It is useful to reinforce that these projections only include those models able to simulate the twentieth-century PDF of rainfall with a skill exceeding 0.8. The change in P (Figure 25) is a clear reduction in region 1, with autumn rainfall reduced most. However, the amount of reduction is very small and there are models projecting increases in all seasons in all time periods. There is clearly a tendency toward drying across region 1 with the bars illustrating the range of individual model projections largely being negative. There is also a tendency for the larger negatives to be in summer and autumn. There is no evidence of drying intensifying further into the future. In region 2 there are suggestions of an increase in P in most seasons with a hint of reduced autumn rains. Rainfall tends to increase more further into the future in the multimodel average, but both increases and decreases are projected by the models. In region 3, winter drying is common to all models in all time periods and increases in rainfall in summer, autumn, and spring occurring in all models. The apparent lack of disagreement in the projections is not due to sample size, which remains constant in all data points in each panel. In region 10, it is difficult to identify a clear response although most models suggest an increase in rainfall in most seasons. This is most clear in A2 (2100). Finally, in region 11, an increase in summer rainfall occurs in model models in all time periods but in general the changes are small.

Figure 25 shows changes in P99. In region 1, in 2050, the change is unclear with the average of the models near zero and the range of individual models equally positive and negative. Under A2, the model means point to an increase in intensity in all seasons, but the uncertainty remains high. In region 2, P99almost always increases pointing to an increase in the annual rainfall event by less than 10 mm. The larger increases tend to be in summer suggesting an increase in the intensity of convective events. Generally larger increases occur by 2100 under A2. In summer (A2, 2100) the increase reaches up to 20 mm. In region 3 results appear to be highly uncertain with very different patterns for 2050 depending on emission scenario. No consensus is clear on the sign of the change in P99. In contrast, in region 10, summer and autumn events uniformly increase while winter and spring events change little. Under B1 and under A2 to 2050 the increase in P99 ranges from 10 to 30 mm in summer and autumn with increasing variability among the models in 2100. Under A2 in 2100 there is a further increase to a model average of about 60 mm. However, it is noteworthy that though the upper range of the increases in summer and autumn rainfall is quite significant (exceeding 160 mm in 2100 in autumn under A2) the low end of each range is near zero. In region 11 increases in summer and autumn is seen in P99 with larger increases by 2100 under A2. Changes in winter and spring are small.

The change in days with no rainfall can also be assessed (Figure 26). In region 1, the average across the models is a consistent increase in rain-free days. The increase is around 5 days yr−1 except in A2 (2100) where the increase doubles to 10 days. There is a seasonal pattern to the change with a smaller increase in winter. However, the range of model estimates is considerable. In region 2, in 2050, there is an increase in rain days. This is maintained through 2100, with the exception of winter (2100, B1) and winter and spring (2100, A2). It would be hard to conclude that there was any pattern in region 3 or 4, except for a hint of an increase in rain-free days under high emissions in 2100. Similarly, in region 11, there is no clear picture that points to major changes until 2100 under A2 where in autumn, winter, and spring there is a large and quite consistent increase in rain-free days.

6. Discussion

The use of a PDF-based criteria, utilizing daily data, for assessing the performance of climate models provides one means to identify weak models over Australia. Perkins et al. (Perkins et al. 2007) showed that some of the AR4 models were unable to capture the observed PDFs of TMAX, TMIN, and P. It seems reasonable to exclude these models in deriving projections for climate over Australia. We excluded models in any region, and for any variable, where the overlap of the modeled and observed PDFs was less than 0.8. This means that our projections are based on a subset of the AR4 models.

In terms of temperature, all models included project warming in and for every region for both 2050 and 2100 for both the B1 and A2 emission scenarios. This is not surprising but does reinforce Christensen et al.’s (Christensen et al. 2007) conclusion for temperature over Australia. We also agree with Christensen et al.’s (Christensen et al. 2007) conclusion that mean warming is generally smaller in the south. The lower mean warming in the southern part of the continent is common to both and but is very much clearer in . However, this is not true of TMAX99 where the southern coast of Australia appears particularly vulnerable. Thus, while we agree with Christensen et al. (Christensen et al. 2007) that extreme high daily temperatures will increase, we note that the amount of increase is highly regionalized. Over most of the continent, TMAX99 increases by an amount similar to , but along the southern coast of the continent the increase in TMAX99 is much higher than the increase in .

We have also explored the relative uncertainty in the projected changes in and relative to the projected change in the TMAX99 and TMIN0.3. The climate models agree substantially better in their simulation of and . Figure 27 shows the relationship between the intermodel range of climate models’ simulation of and and the models’ simulation of the range in TMAX99 and TMIN0.3. In virtually every instance, the range in those models with good skill over regional Australia in the projected change in and is substantially less than the range in TMAX99 and TMIN0.3. This is not related to sample size (each range has the same number of models included), and it is not due to the magnitude of the actual projected changes (the same behavior is seen in TMAX and TMIN). Figure 27 simply shows that while we may be reasonably confident in how much the regional mean climate of Australia might warm, we are less confident on how the more extreme values will change. That said, and not surprisingly, there is full consensus that warming will occur in , , TMAX99, and TMIN0.3; it is just that we have less confidence in the amount the tails of the distribution will change by. This is most likely due to the rarer occurrence of values toward the tails (which make them harder to model), but it may, in part, be that climate models have been more thoroughly evaluated against means.

In terms of changes in rainfall, Christensen et al. (Christensen et al. 2007) note that precipitation is likely to decrease in southern Australia in winter and spring. We generally concur, but note that the amount that rainfall will be reduced by is not large. Some models omitted from our ensemble showed substantially larger changes in rainfall: we omit these because they perform poorly compared to observations of rainfall. Our results are difficult to summarize since they vary regionally and vary with emission scenario (Figures 17 and 18). In 2050 (B1) there is a suggestion of a broadscale increase in P, but the amounts are small and a close investigation shows either a decrease or little change along the south coast. This is much clearer in 2050 under the A2 scenario with drying along the south coast in winter, spring, and (locally) autumn. This is reinforced in 2100 under the B1 scenario and is dramatic in the A2 scenario by 2100. Thus, we agree with Christensen et al.’s (Christensen et al. 2007) broad conclusion but note that, until 2100 under A2, the reduction appears to be very localized to the coast. This is not reassuring since the population of Australia is also highly concentrated on the coast.

Christensen et al. (Christensen et al. 2007) also conclude that P is very likely to decrease in southwest Western Australia. Southwest Western Australia is a much-studied region of Australia as it underwent a sudden decline in rainfall in around 1970 (IOCI 2002) that has been partially attributed to a variety of mechanisms including global warming, natural variability, ocean warming (England et al. 2006), and land-cover change (Pitman et al. 2004; Timbal and Arblaster 2006). Our results are not encouraging for this region. Drying is seen in almost every panel of Figures 17 and 18, and this region is projected to undergo the most intense drying of any region in 2100 (winter, A2). There is a hint of an increase in rainfall over southwest Western Australia in summer under the high-emission scenario in 2050. However, since every other season for this time and scenario projects declines in rainfall, this region appears highly likely to need to confront the consequences of further reductions in rainfall.

Christensen et al. (Christensen et al. 2007) suggest changes in rainfall are particularly uncertain in northern Australia. We do not agree—in the wet season (summer) the more skillful models all point to increasing rainfall under all scenarios for both 2050 and 2100. There are also consistent increases in rainfall in spring and autumn (Figures 17 and 18), which hint at a lengthening of the monsoon, although we have not explored this in detail. We find little change in central Australia, which is understandable given it rains little in this region.

Christensen et al. (Christensen et al. 2007) finally note that daily precipitation extremes will very likely increase. The annual extreme increases in our projections over almost the whole continent in all periods and in all emission scenarios. The increase is largest in summer in the tropics and least (in fact sometimes negative) in parts of Western Australia. However, the annual precipitation event increases little (generally the increase is 1–10 mm per event). It is possible that rarer events increase more dramatically and we will explore this in the future. So while we agree with Christensen et al. (Christensen et al. 2007), we note that the magnitude of the increase in daily extreme rainfall is quite small even by 2100 under A2. There is one sense that this is unfortunate—higher rainfall intensities can aid runoff into dams and help flush rivers.

Thus, the climate models simulate an increase in rainfall intensity and a small change in the total rainfall. There is also an increase in the frequency of the annual rainfall event under B1 (Figure 21) and an increase in western and southern parts but a decrease in eastern and northern parts under A2 (Figure 22) in all seasons excluding winter. This is not contradictory because the number of days with no rainfall (<0.2 mm day−1) also changes. In general, the changes in no-rain days is small (within a range of ±5 days yr−1), but along the southern coast there is an increase in no-rain days that exceeds 5 and occasionally 10 days yr−1 by 2100 under B1 (Figure 23). This reduction is throughout the year. Under A2, in particular in winter, there is also a reduction in no-rain days along the south coast. This intensifies by 2100 such that in spring and autumn there are 10–20 fewer rain days, and in winter the reduction exceeds 20 days yr−1. The area of increase in no-rain days extends northward along the east coast of the continent and over southwest Western Australia.

Thus, in terms of rainfall, the key mechanism that explains the reduction in rainfall is not a reduction in rainfall extremes (these tend to increase) but an increase in the days with no rainfall. This is consistent with Murphy and Timbal (Murphy and Timbal 2007) who note these changes in the recent observed record. Thus, a future of slightly less rainfall falling more intensively but on substantially fewer days each year is a strong and consistent result across those AR4 models that can simulate the current rainfall patterns.

There is also a clear link between the changes in P and the changes in TMAX99. Areas where rainfall decreases dry and limits evaporation. Evaporation is effective at reducing TMAX so a relationship between changes in the amount or distribution of rainfall is likely to affect TMAX (but not TMIN). We could not identify any relationships between and rainfall. However, TMAX99 is broadly related to P. This is to be expected but implies that to simulate TMAX99 requires a good simulation of rainfall.

Rainfall is harder to simulate than temperature, thus it is to be anticipated that there would be considerable uncertainty in models’ projection of rainfall. Figure 25 shows, region by region, that there is considerable uncertainty among the skillful models in the projection of the change in mean rainfall. These are large regions and our results include areas of both increase and decrease in rainfall shown in the maps. Large regions were required to implement the methodology of comparing the simulated and observed PDFs since several climate model grids were needed in each region to ensure a reliable analysis. While uncertainty is very apparent, the means of the skillful models in region 1 point to drying. Regions 2, 3, and 10 are generally shown to be regions of increasing rainfall (region 11 is mixed). In terms of uncertainty in the projection of the change in P99, while there is considerable uncertainty in region 1 there is a strong consensus of an increase in intensity in regions 2, 10, and 11. Apparent anomalous results (e.g., region 3, winter 2050 A2 and winter 2100 A2) can be reconciled since the reduced intensity is due to a reduction in the total rainfall. There is a strong increase in rainfall intensity in the tropics (region 10) in summer and autumn and while the error bars are large, the sign of the change is quite clear. The change in the frequency of P99 is also clear with Figure 25 showing a very consistent pattern of the current once-per-year event occurring twice per year. Finally, Figure 25 showed a strong pattern of fewer rain days particularly in region 1. This is clearly highly uncertain but the skillful models tend to simulate less rain days particularly in winter in this region. Less rain days are also clear in region 2 but unclear elsewhere. Figure 27 confirms that the uncertainty range within the ensemble of models is always higher for the 99.7th/0.3rd percentile than for the mean.

What causes this pattern of changes?

Explaining all of the changes identified in this paper is beyond our capacity. We can, however, link some of the changes identified in this paper with known phenomenon or reasonably established mechanisms identified in earlier studies.

  1. The decline in rainfall projected over southwest Western Australia. This appears robust and is consistent with the assessment of others (Hope 2006; Christensen et al. 2007). This is an important region in Australia (IOCI 2002; Power et al. 2005), and it is useful to show that earlier findings are not affected significantly by the inclusion of weak climate models.In a thorough analysis of the rainfall decline in this region, Cai and Cowan (Cai and Cowan 2006) explored the extent to which the observed decline in rainfall could be explained by anthropogenic forcing and was congruent with the southern annular mode (SAM). They showed that in winter there was a relationship between the AR4 models’ simulation of the SAM and the decline in rainfall over this region. They suggested that anthropogenic forcing contributes to about 50% of the observed rainfall decline. The key connection is a southward migration of the Southern Hemisphere storm tracks (Miller et al. 2006) that cause rain-bearing frontal systems to miss the southern coastline of Australia. The AR4 models appear to show a continuation of the trends observed over this region since about 1970 (IOCI 2002) and point to ongoing regional drying through the twenty-first century (Hope 2006). The changes in the synoptic systems were explored by Hope (Hope 2006) who indicates that most models project decreases in troughs and the emergence of more high pressure systems across the region. However, the Goddard Institute for Space Studies Model E-R (GISS-ER) produced a different change in the behavior of the synoptic patterns. Perkins et al. (Perkins et al. 2007) shows that GISS-ER is relatively weak over this region in rainfall simulation and is not included in our projections over Western Australia.
  2. The decline in rainfall along the southern coast of Australia. A similar mechanism involving the latitude of the storm tracks appears to cause coastal drying in the models. Miller et al. (Miller et al. 2006) explored changes in the AR4 models and showed that the multimodel average of sea level pressure over the Southern Hemisphere exhibited a decreasing trend over the pole and a compensating increase in midlatitudes. The trend in the multimodel average was associated with a poleward shift of the storm track in both hemispheres and a strengthening of the upper-level westerly flow. There are alternative mechanisms that might explain the coastal decline in rainfall. For example, Hope et al. (Hope et al. 2006) noted that changes in the hemispheric circulation could affect the frequency of storms in the region, and Fyfe (Fyfe 2003) showed that increasing greenhouse gases decreased the number of subantarctic surface cyclones. Finally, Yin (Yin 2005) found a southward shift in the strong baroclinic zones under higher greenhouse gas concentrations. All these mechanisms have the potential to decrease the likelihood of rainfall along the southern coast of Australia and all point to further declines in rainfall in the future.
  3. The amplification of warming in the TMAX99 is associated with changes in rainfall. Because of the increases in greenhouse gases, and TMAX99 increase. However, TMAX99 increases by an additional increment because of the decline in P in some regions. Reduced rainfall tends to dry a surface and suppress the cooling effect of evaporation. Thus, there is a correlation between those regions that show the highest increases in TMAX99 and decreases in P (e.g., along the southern coast of Australia). This coupling of the surface energy and water balance is expected, but it highlights the need to capture changes in regional rainfall in order to project changes in regional temperatures.
  4. The reduction in rain days and the increase in rainfall intensity. It is no surprise that we find an increase in the intensity of rainfall over many regions. Trenberth et al. (Trenberth et al. 2003) noted that increases in greenhouse gases leads to increases in the water-holding capacity of the atmosphere by about 7% K−1 (Trenberth 1998). Soden et al. (Soden et al. 2002) indicate that models suggest that changes in relative humidity are small, hence the actual moisture content of the atmosphere should increase by roughly 7% K−1 (Trenberth et al. 2003). However, because of the nonlinear dependence of moisture with temperature, the largest absolute increase in moisture would be expected in the tropics. Trenberth et al. (Trenberth et al. 2003) use this insight, coupled with changes in moisture convergence, to argue that in a warmer climate, heavy precipitation intensity should also increase by about 7% K−1. However, since heavy rainfall intensity increases by 7% K−1 while total rainfall amounts increase by 1%–2% K−1 (Cubasch et al. 2001), this implies a reduction in low intensity and/or a reduction in the frequency of rainfall. This is what we find, and indeed Sun et al. (Sun et al. 2007) found similar results at a global scale. Thus, our results over Australia—an increase in intense rainfall, a reduction in light rainfall, and a decrease in the frequency of precipitation events—are consistent with expectations, and consistent with global analyses (Sun et al. 2007).
  5. The increase in the amount and intensity of rainfall in the tropics in summer. Meehl et al. (Meehl et al. 2007) note that many climate models simulate an increase in monsoonal precipitation under warming scenarios. However, the assumption that this increase is related to the more rapid warming over land compared to the oceans and a stronger summer land–sea contrast is too simplistic. Rather, the intensification may be related to weakening of the Walker and monsoon circulations (Tanaka et al. 2005). Despite weakening of the dynamical monsoon circulation, atmospheric moisture buildup due to increased greenhouse gases and consequent temperature increase results in a larger moisture flux and a higher probability of intense rainfall (Meehl et al. 2007). Part of the monsoonal intensification may also relate to the mechanisms discussed by Trenberth et al. (Trenberth et al. 2003) and noted above.

7. Conclusions

Using the capacity of each AR4 model to simulate the observed probability distribution for daily TMAX, TMIN, and P, we select skillful models region by region over Australia. This subset of AR4 models is then used to explore the changes in these variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100.

Consistent with earlier assessments using earlier climate model results, and consistent with analyses of the AR4 models, we find a consistent pattern of mean warming in and in the future. The patterns of warming are highly regional but a general pattern of lower amounts of warming in the south is apparent in the mean. More warming is clear further into the future and under higher emissions. We show that increases in TMAX99 are also highly regional, but the highest increases occur along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall. Large-scale changes in the climate of the southern oceans likely cause storm tracks to move poleward, which leads to reduced rainfall along the southern coast. This causes drying, suppresses evaporation, and allows extreme temperatures to rise more than mean temperatures. Elsewhere, in general, the amount of rainfall changes relatively little, the frequency of rainfall decreases, and the intensity of rainfall at the upper tail of the distribution increases. This is consistent with theory (Trenberth et al. 2003) and earlier global analyses (Sun et al. 2007).

We show that in all cases, the confidence we can have in climate model projections in the change in the mean is at least twice as high as the confidence we can have in the changes in the 99.7th/0.3rd percentiles (Figure 27) in the case of temperature. For rainfall, the uncertainty represented in our AR4 ensembles is of order 10 times higher in the 99.7th percentile compared to the mean. This limits the use of climate model data to explore sectors that are vulnerable to extremes and suggests a research strategy that evaluates climate models on more than their capacity to simulate the means.

Overall, we conclude that the AR4 model results, when selected by skill, provide similar changes in temperature and rainfall to many earlier projections. We conclude that it is useful to select models: Perkins et al. (Perkins et al. 2007) have shown that some models submitted as part of the AR4 are flawed in their capacity to simulate temperature and rainfall over Australia, and it is inappropriate to then use these models to explore possible future climates. Clearly, more sophisticated metrics are required that allow the community to routinely exclude models and we suggest that these metrics might well utilize high-resolution data (i.e., daily) and evaluate the distribution of these data using something like a probability density function.

Acknowledgments

We acknowledge the international modeling 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 Modeling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis 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. We also thank Silicon Graphics for help in porting Matlab. This work was funded in part by the Australian Research Council.

REFERENCES

  • Cai, W., and T. Cowan. 2006. SAM and regional rainfall in IPCC AR4 models: Can anthropogenic forcing account for southwest Western Australian winter rainfall reduction? Geophys. Res. Lett. 33.L24708, doi:10.1029/2006GL028037.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H. Coauthors 2007. Regional climate projections. Climate Change 2007: The Scientific Basis, S. Solomon, Ed., Cambridge University Press, 847–940.

    • Search Google Scholar
    • Export Citation
  • Colombo, A., , D. Etkin, , and B. Karney. 1999. Climate variability and the frequency of extreme temperature events for nine sites across Canada: Implications for power usage. J. Climate 12:24902502.

    • Search Google Scholar
    • Export Citation
  • CSIRO 2007. Climate change in Australia. Australian Greenhouse Office Tech. Rep., 152 pp. [Available online at www.climatechangeinaustralia.gov.au.].

  • Cubasch, U. Coauthors 2001. Projections of future climate change. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 525–582.

    • Search Google Scholar
    • Export Citation
  • England, M. H., , C. C. Ummenhofer, , and A. Santoso. 2006. Interannual rainfall extremes over southwest Western Australia linked to Indian Ocean climate variability. J. Climate 19:19481969.

    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C. 2003. Extratropical Southern Hemisphere cyclones: Harbingers of climate change? J. Climate 16:28022805.

  • Hope, P. 2006. Projected future changes in synoptic systems influencing southwest Western Australia. Climate Dyn. 26:765780. doi:10.1007/s00382-006-0116-x.

    • Search Google Scholar
    • Export Citation
  • Hope, P., , W. Drosdowsky, , and N. Nicholls. 2006. Shifts in the synoptic systems influencing southwest Western Australia. Climate Dyn. 26:751764. doi:10.1007/s00382-006-0115-y.

    • Search Google Scholar
    • Export Citation
  • Hughes, L. 2000. Biological consequences of global warming: Is the signal already apparent? Trends Ecol. Evol. 15:5662.

  • IOCI 2002. Climate Variability and Change in South West Western Australia. The Indian Ocean Climate Initiative Panel, 34 pp. [Available from The Indian Ocean Climate Initiative Panel, c/o Department of the Environment, Water and Catchment Protection, Hyatt Place, 3 Plain St., East Perth, WA, 6004 Australia.].

    • Search Google Scholar
    • Export Citation
  • Johns, T. C. Coauthors 2006. The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. J. Climate 19:13271353.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., , G. A. Meehl, , M. R. Allen, , and D. A. Stainforth. 2006. Constraining climate sensitivity from the seasonal cycle in surface temperature. J. Climate 19:42244233.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , F. Zwiers, , J. Evans, , T. Knutson, , L. O. Mearns, , and P. H. Whetton. 2000. Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bull. Amer. Meteor. Soc. 81:427436.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A. Coauthors 2007. Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–843.

    • Search Google Scholar
    • Export Citation
  • Miller, R. L., , G. A. Schmidt, , and D. T. Shindell. 2006. Forced annular variations in the 20th century IPCC AR4 simulations. J. Geophys. Res. 111.D18101, doi:10.1029/2005JD006323.

    • Search Google Scholar
    • Export Citation
  • Murphy, B. F., and B. Timbal. 2007. A review of recent climate variability and climate change in southeastern Australia. Int. J. Climatol. 28:859879. doi:10.1002/joc.1627.

    • Search Google Scholar
    • Export Citation
  • Parkinson, G. 1986. Climate. Vol. 4, Atlas of Australian Resources, Third Series, Commonwealth of Australia, 60 pp.

  • Perkins, S. E., and A. J. Pitman. 2008. Do weak AR4 models bias projections of future climate changes over Australia? Climatic Change submitted.

    • Search Google Scholar
    • Export Citation
  • Perkins, S. E., , A. J. Pitman, , N. J. Holbrook, , and J. McAneney. 2007. Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions. J. Climate 20:43564376.

    • Search Google Scholar
    • Export Citation
  • Piani, C., , D. J. Frame, , D. A. Stainforth, , and M. R. Allen. 2005. Constraints on climate change from a multi-thousand member ensemble of simulations. Geophys. Res. Lett. 32.L23825, doi:10.1029/2005GL024452.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., , G. T. Narisma, , R. Pielke, , and N. J. Holbrook. 2004. The impact of land cover change on the climate of southwest Western Australia. J. Geophys. Res. 109.D18109, doi:10.1029/2003JD004347.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., , G. T. Narisma, , and J. McAneney. 2007. The impact of climate change on Australian bush fire risk. Climatic Change 84:383401. doi:10.1007/s10584-007-9243-6.

    • Search Google Scholar
    • Export Citation
  • Power, S., , B. Sadler, , and N. Nicholls. 2005. The influence of climate science on water management in Western Australia: Lessons for climate scientists. Bull. Amer. Meteor. Soc. 86:839844.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A. Coauthors 2007. Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

    • Search Google Scholar
    • Export Citation
  • Shukla, J., , T. DelSole, , M. Fennessy, , J. Kinter, , and D. Paolino. 2006. Climate model fidelity and projections of climate change. Geophys. Res. Lett. 33.L07702, doi:10.1029/2005GL025579.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., , R. T. Wetherald, , G. L. Stenchikov, , and A. Robock. 2002. Global cooling after the eruption of Mount Pinatubo: A test of climate feedback by water vapor. Science 296:727730.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., , D. Qin, , M. Manning, , Z. Chen, , M. Marquis, , K. B. Averyt, , M. Tignor, , and H. L. Miller. 2007. Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

    • Search Google Scholar
    • Export Citation
  • Sun, Y., , S. Solomon, , A. Dai, , and R. W. Portmann. 2007. How often will it rain? J. Climate 20:48014818. doi:10.1175/JCLI4263.1.

  • Tanaka, H. L., , N. Ishizaki, , and D. Nohara. 2005. Intercomparison of the intensities and trends of Hadley, Walker and monsoon circulations in the global warming projections. SOLA 1:7780.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E. 2001. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106:D7. 71837192.

  • Timbal, B., and J. M. Arblaster. 2006. Land cover changes as an additional forcing to explain the rainfall decline in the south west of Australia. Geophys. Res. Lett. 33.L07717, doi:10.1029/2005GL025361.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E. 1998. Atmospheric moisture residence times and cycling: Implications for rainfall rates with climate change. Climatic Change 39:667694.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., , A. Dai, , R. M. Rasmussen, , and D. B. Parsons. 2003. The changing character of precipitation. Bull. Amer. Meteor. Soc. 84:12051217.

    • Search Google Scholar
    • Export Citation
  • Trigo, R. M., , R. García-Herrera, , J. Díaz, , and I. F. Trigo. 2005. How exceptional was the early August 2003 heatwave in France? Geophys. Res. Lett. 32.L10701, doi:101029/2005GL022410.

    • Search Google Scholar
    • Export Citation
  • Watterson, I. G. 1996. Non-dimensional measures of climate model performance. Int. J. Climatol. 16:379391.

  • Whetton, P. H., , K. L. McInnes, , R. N. Jones, , K. J. Hennessy, , R. Suppiah, , C. M. Page, , J. Bathols, , and P. J. Durack. 2005. Australian Climate Change Projections for Impact Assessment and Policy Application: A Review. Climate Impact Group, CSIRO Marine and Atmospheric Research, 34 pp.

    • Search Google Scholar
    • Export Citation
  • Yin, J. H. 2005. A consistent poleward shift of the storm tracks in simulations of 21st century climate. Geophys. Res. Lett. 32.L18701, doi:10.1029/2005GL023684.

    • Search Google Scholar
    • Export Citation

Figure 1.
Figure 1.

Location map of Australia showing state boundaries, major cities, and the regions discussed in the text.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 2.
Figure 2.

PDF of TMAX from three climate models for region 2 (see Table 2) for the present day and for 2100 showing a very high degree of overlap between the present and future for a given climate model. Solid lines are for the present day, dashed are for 2100. MIROC-m is in blue, MRI in red, and CSIRO in black. All lines are smoothed for visual clarity.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 3.
Figure 3.

Change from the present climate in the averaged daily (°C) over Australia simulated for the B1 emission scenarios for (a) spring (2050), (b) summer (2050), (c) autumn (2050), (d) winter (2050), (e) annual (2050), (f) spring (2100), (g) summer (2100), (h) autumn (2100), (i) winter (2100), and (j) annual (2100). Only models with a skill score >0.8 are included (see text).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 4.
Figure 4.

As in Figure 3, but for the A2 emission scenario.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 5.
Figure 5.

As in Figure 3, but for the change in TMAX99.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 6.
Figure 6.

As in Figure 4, but for the change in the TMAX99.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 7.
Figure 7.

As in Figure 3, but for the change in the frequency of TMAX99 (days per season or days per year).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 8.
Figure 8.

As in for Figure 7, but for the A2 emission scenario.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 9.
Figure 9.

Changes in (left column) , (middle column) TMAX99, and (right column) the frequency of TMAX99. The bars represent the range of projections within each ensemble. The five regions (see Table 2) are, from the top, region 1, region 2, region 3, region 10, and region 11. The number of models included in each period is shown in each panel. The first four bars are for B1 (2050), followed by A2 (2050), then B1 (2100), and finally A2 (2100).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 10.
Figure 10.

As in Figure 3, but for .

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 11.
Figure 11.

As in Figure 4, but for .

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 12.
Figure 12.

As in Figure 3, but for the change in TMIN0.3.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 13.
Figure 13.

As in Figure 4, but for the change in TMIN0.3.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 14.
Figure 14.

As in Figure 7, but for TMIN0.3.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 15.
Figure 15.

As in Figure 8, but for TMIN0.3.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 16.
Figure 16.

Changes in (left column) , (middle column) TMIN0.3, and (right column) the frequency of TMIN0.3. The bars represent the range of projections within each ensemble. The five regions (see Table 2) are, from the top, region 1, region 2, region 3, region 10, and region 11. The number of models included in each period is shown in each panel. The first four bars are for B1 (2050), followed by A2 (2050), then B1 (2100), and finally A2 (2100).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 17.
Figure 17.

As in Figure 3, but for P (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 18.
Figure 18.

As in Figure 17, but for the A2 emission scenario (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 19.
Figure 19.

As in Figure 5, but for P99 (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 20.
Figure 20.

As in As in Figure 6, but for P99 (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 21.
Figure 21.

As in Figure 7, but for the frequency of P99 (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 22.
Figure 22.

As in Figure 8, but for the frequency of P99 (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 23.
Figure 23.

As in Figure 5, but for the change in no-rain days (daily rainfall amounts less than 0.2 mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 24.
Figure 24.

As in Figure 23, but for the A2 emission scenario (mm day−1).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 25.
Figure 25.

Changes in (left column) P, (middle column) P99, and (right column) the frequency of P99. The bars represent the range of projections within each ensemble. The five regions (see Table 2) are, from the top, region 1, region 2, region 3, region 10, and region 11. The number of models included in each period is shown in each panel. The first four bars are for B1 (2050), followed by A2 (2050), then B1 (2100), and finally A2 (2100).

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 26.
Figure 26.

As in Figure 9, but for the change in frequency of rain-free days (daily rainfall amounts less than 0.2 mm day−1). (top left) Region 1, (top right) region 2, (middle left) region 3, (middle right) region 10, and (bottom left) region 11.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Figure 27.
Figure 27.

Relationship between intermodel range of climate models’ simulation of , , and P and the models’ simulation of the range in TMAX99, TMIN0.3, and P99. This is the length of the bars shown in Figures 9 and 16 (column 1 vs column 2). The individual dots are color coded: blue denotes region 1; red denotes region 2; green denotes region 3; orange denotes region 10; pink denotes region 11.

Citation: Earth Interactions 12, 12; 10.1175/2008EI260.1

Table 1.

All climate models with daily data for TMAX, TMIN, and P available from PCMDI. Column 1 is the acronym used in the text. Column 2 is the name of the model used in the PCMDI archive, and column 3 is the source of the model (see http://www-pcmdi.llnl.gov/ipvv/about_ipcc.php).

Table 1.
Table 2.

Latitude and longitude boundaries for selected 9.75° × 10.75° regions over Australia, with climate type based on the Köppen classification scheme derived by BOM.

Table 2.
Save