Multi-model climate change scenarios for southwest Western Australia and potential impacts on streamflow

16 Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007. Multi-model climate change scenarios for southwest Western Australia and potential impacts on streamflow GUOBIN FU & STEPHEN P. CHARLES CSIRO Land and Water, PB 5, Wembley, Western Australia 6155, Australia guobin.fu@csiro.au Abstract This paper examines future climate change scenarios of annual and monthly temperature, precipitation, and sea-level pressure (SLP) from 19 general circulation models (GCMs) used in the IPCC Fourth Assessment Report, and discusses the potential impacts on streamflow for southwest Western Australia (SWWA). The ranges of annual temperature increase indicated by the 19 GCMs for three emission scenarios (A1B, A2 and B1) are 0.76 0.91 C, 1.17 1.57 C and 1.69 2.72 C for 2025, 2050 and 2085, respectively. The annual precipitation is projected to decrease by 16.0 19.9 mm (4.7 5.6%), 31.2 38.4 mm (9.1 11.6%), and 38.1 56.2 mm (11.2 16.4%) for these periods. The annual SLP increases by 0.2 hpa, 0.3 0.4 hpa and 0.4 0.6 hpa correspondingly. Changes in the seasonality of precipitation, temperature, and SLP also occur. Together these changes, although with some uncertainties, are likely to lead to major challenges for regional water resource management and planning. These results provide a reference for decision-makers when determining adaptation policies and the methods can be used for other regions in assessing the potential future climatic change for the given emission scenarios. Key words climate change; emission scenarios; GCMs; southwest Western Australia; streamflow; water resources management INTRODUCTION Since the mid-1970s the observed rainfall and streamflow across southwest Western Australia (SWWA) has declined significantly (IOCI, 2002) and there are extensive regions where rainfall is less than 80% of the long-term average (Hope et al., 2006). This decline has had a significant impact on water supply in the region, decreasing inflow into reservoirs by 40 65% (IOCI, 2002; Power et al., 2005). This decline has occurred simultaneously with changes in the large scale atmospheric circulation, currently attributed to both natural variability and the enhanced greenhouse effect (IOCI, 2002). Recently, Cai & Cowan (2006) showed that about 50% of the SWWA rainfall reduction before 2000 is attributable to anthropogenic forcing. Therefore, the future climate, including rainfall, is of utmost interest to resource management, agriculture, and water-users in the region. Given the strong spatial gradients from south to north and coast to inland with the majority of SWWA precipitation falling in a few winter months (Smith et al., 2000), and the coarseness and uncertainties of GCM rainfall projections to the extent that different models often disagree even on the sign of regional changes (Giorgi & Francisco, 2000; Murphy et al., 2004), there is more confidence in studies that have projected the future changes in synoptic systems or atmosphere variables and then Copyright 2007 IAHS Press

Multi-model climate change scenarios for southwest Western Australia 17 related these to rainfall (downscaling). For example, Hope (2006) used eight GCMs to project the future changes in synoptic systems influencing SWWA for June and July. This is a suitable approach given the relationship between observed rainfall and largescale atmospheric change has been well documented for this region (Allan & Haylock, 1993; Smith et al., 2000; Li et al., 2005) and downscaling methods for the region have been identified which quantify the linkage (Charles et al., 1999, 2004). This study extracts projected SWWA climate variables (annual and monthly temperature, rainfall, and SLP) from 19 GCMs used in the IPCC Fourth Assessment Report. The uncertainties associated with emission scenarios, and model structure and parameterization are discussed. The potential impacts of these projected changes on streamflow and water availability for SWWA are explored. METHODS AND MATERIALS Study region The study region is SWWA ranging from 115 120 E and 30 35 S. The annual rainfall for this region varies from 300 mm in the semi-arid northeast to over 1000 mm near the southwest coast. The majority of annual rainfall falls within the winter months with over 80% falling during the April to October period. The early winter (May July) rainfall has a well documented step change in the mid-1970s that resulted in a rainfall reduction of approximately 10 15% (IOCI, 2002; Hope, 2006). This region has been identified as vulnerable to climate change by the IPCC (McCarthy et al., 2001). GCMs There were 23 GCMs used for the IPCC Fourth Assessment Report (4AR). However, not all had data available. The model outputs for 19 GCMs were available and used for this study. The model name, institute and data periods are listed in Table 1. Detailed information for these models can be found at the IPCC Data Distribution Centre website (http://ipcc-ddc.cru.uea.ac.uk). Variable changes Since most GCMs could not simulate observed 20th century climate accurately at regional scales (Giorgi & Francisco, 2000; Phillips & Gleckler, 2006), we cannot relate the future model results directly with current observed data. Instead, we compare the future climate scenarios with their respective 4AR 20th century runs (20c3m, 1961 2000). The differences in climatology are treated as changes due to increasing emissions. Emission scenarios IPCC scenarios A1B, A2 and B1 are used in this paper. These are the standard emission scenarios used in 4AR, and so are available for all GCMs. However, they do

18 Guobin Fu & Stephen P. Charles Table 1 GCMs used in this study *. Model name Originating group(s) and country 20c3m A1B A2 B1 cccma_cgcm3_1 Canadian Centre for Climate Modelling & Y Y Y Y Analysis (Canada) cnrm_cm3 Météo-France / Centre National de Recherches Y Y Y Y Météorologiques (France) csiro_mk3_0 CSIRO Atmospheric Research (Australia) Y Y gfdl_cm2_0 US Dept of Commerce / NOAA / Geophysical Y Y Y Y Fluid Dynamics Laboratory (USA) gfdl_cm2_1 US Dept of Commerce / NOAA / Geophysical Y Y Y Y Fluid Dynamics Laboratory (USA) giss_aom NASA / Goddard Institute for Space Studies (USA) Y Y Y giss_model_e_h NASA / Goddard Institute for Space Studies (USA) Y Y giss_model_e_r NASA / Goddard Institute for Space Studies (USA) Y Y Y Y iap_fgoals1_0_g LASG / Institute of Atmospheric Physics (China) Y Y Y inmcm3_0 Institute for Numerical Mathematics (Russia) Y Y Y Y ipsl_cm4 Institut Pierre Simon Laplace (France) Y Y Y Y miroc3_2_hires Center for Climate System Research (The Y University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) (Japan) miroc3_2_medres Center for Climate System Research (The Y Y Y Y University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) (Japan) mpi_echam5 Max Planck Institute for Meteorology (Germany) Y Y Y Y mri_cgcm2_3_2a Meteorological Research Institute Japan) Y Y Y Y ncar_ccsm3_0 National Center for Atmospheric Research (USA) Y Y Y Y ncar_pcm1 National Center for Atmospheric Research (USA) Y Y Y Y ukmo_hadcm3 Hadley Centre for Climate Prediction and Y Y Y Y Research / Met Office (UK) ukmo_hadgem1 Hadley Centre for Climate Prediction and Research / Met Office (UK) Y * The availability of model runs is solely based on data availability at the time of the study. The unavailability for a specific GCM model does not necessarily mean the data has not become available subsequently. not represent the full range of possible climate change. A1B The A1 scenarios all describe a future world of very rapid economic growth and global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. B means balanced energy between fossil fuels and other energy sources. B1 The B1 scenario describes a convergent world with the same global population as in the A1 scenario, but with rapid changes in economic structures. The emphasis is on global solutions to economic, social and environmental sustainability, including improved equity, but without additional climate initiatives. A2 The A2 scenario describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Economic development is primarily regionally oriented and per capita economic growth and technological changes are more fragmented and slower than in other scenarios.

Multi-model climate change scenarios for southwest Western Australia 19 RESULTS AND DISCUSSION Temperature projections Overall, the GCMs project annual temperature increases for SWWA of 0.76 0.91 C for 2025 (2005 2044), 1.17 1.57 C for 2050 (2030 2069), and 1.69 2.72 C for 2080 (2060 2099) for the three IPCC emission scenarios (Table 2). Although there are differences between GCMs (the minimum and maximum annual temperature increases are given as ranges in Table 2), the overall trend of increasing temperature is obvious. The effect of differing emission paths increases with time: e.g. the annual temperature difference between scenarios A2 and B1 is only 0.13 C for 2025, but increases to 1.03 C for 2080 (Table 2). For monthly output, summer (December, January, November and October) temperature increases are usually larger than those during winter (July, June and August) (Table 3). This pattern is almost consistent for all emission scenarios and for the Table 2 Projected annual climate changes for SWWA. Variables Time Emission Change in annual value Changes in percentage period scenarios Mean Range Mean Range Temperature C C 2025 A1B 0.91 0.56 to 1.34 A2 0.89 0.51 to 1.31 B1 0.76 0.45 to 1.12 2050 A1B 1.51 0.73 to 2.12 A2 1.57 1.13 to 2.17 B1 1.17 0.80 to 1.66 2080 A1B 2.25 0.98 to 3.28 A2 2.72 1.90 to 3.61 B1 1.69 1.17 to 2.44 Precipitation mm mm % % 2025 A1B 19.9 51.1 to 18.7 5.6 15.6 to 3.7 A2 17.5 64.2 to 17.3 4.9 19.8 to 5.1 B1 16.0 68.1 to 28.1 4.7 21.1 to 9.6 2050 A1B 38.4 94.9 to 3.1 11.6 29.4 to 0.6 A2 36.7 82.8 to 15.1 10.7 25.6 to 3.0 B1 31.2 80.8 to 2.1 9.1 21.4 to 1.4 2080 A1B 52.4 122.7 to 10.7 15.5 37.9 to 4.5 A2 56.2 127.2 to 8.1 16.4 38.4 to 1.6 B1 38.1 98.6 to 2.3 11.2 24.3 to 0.4 Sea-level pressure Pa Pa 2025 A1B 0.2 0.1 to 0.6 A2 0.2 0.1 to 0.8 B1 0.2 0.2 to 0.6 2050 A1B 0.3 0.0 to 1.0 A2 0.4 0.1 to 1.3 B1 0.3 0.1 to 0.8 2080 A1B 0.4 0.1 to 1.6 A2 0.6 0.0 to 1.9 B1 0.4 0.2 to 1.2

20 Guobin Fu & Stephen P. Charles 20 Table 3 Mean projected monthly climate changes for SWWA. Guobin Fu & Stephen P. Charles Variables Time period Emission scenarios Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec A1B 0.90 0.86 0.88 0.94 0.94 0.86 0.80 0.87 0.93 0.93 0.95 1.02 2025 A2 0.96 0.86 0.90 0.93 0.87 0.80 0.72 0.82 0.90 0.89 0.96 1.01 B1 0.83 0.70 0.78 0.77 0.76 0.71 0.63 0.72 0.76 0.79 0.79 0.85 A1B 1.51 1.51 1.53 1.53 1.50 1.44 1.35 1.45 1.57 1.53 1.57 1.64 Temperature ( o C) 2050 A2 1.70 1.62 1.61 1.58 1.53 1.39 1.32 1.45 1.58 1.62 1.65 1.75 B1 1.26 1.13 1.21 1.23 1.13 1.05 1.00 1.10 1.16 1.26 1.22 1.30 A1B 2.31 2.27 2.27 2.23 2.19 2.10 2.02 2.11 2.33 2.38 2.36 2.45 2080 A2 2.91 2.78 2.81 2.81 2.71 2.45 2.31 2.51 2.79 2.77 2.85 2.97 B1 1.72 1.62 1.66 1.64 1.53 1.41 1.35 1.46 1.63 1.66 1.63 2.95 A1B 1.2 0.1-0.1-0.9-2.9-2.7-4.6-5.2-2.9-1.3-1.0 0.3 2025 A2 0.5 0.3-0.1 0.5-1.5-2.3-4.9-4.9-2.5-1.3-0.6-0.7 B1 0.2 1.1-0.8 0.8-1.3-2.2-4.7-4.9-2.4-1.0-0.6-0.1 A1B 0.1-1.1-0.6-1.3-4.0-6.1-8.1-7.6-4.7-2.4-1.6-0.9 Precipitation (mm) 2050 A2 0.3-0.5-1.0-1.1-3.3-5.2-8.1-8.4-4.3-2.4-1.4-1.3 B1 0.3-0.7-1.0-0.9-2.8-3.9-7.4-6.8-3.9-2.1-1.3-0.8 A1B -0.2-1.1-0.7-1.2-6.2-8.6-10.3-10.1-6.3-3.6-2.3-1.8 2080 A2-0.1 0.1-1.5-1.1-5.5-8.9-12.2-11.6-6.7-4.3-2.6-1.8 B1 0.2-1.3-1.0-1.9-4.1-5.2-7.3-7.6-4.6-2.8-1.5-1.2 A1B 8.3 2.7 0.7-3.5-5.7-3.9-9.0-11.6-12.2-8.2-10.0 4.6 2025 A2 4.2 5.1 1.8 3.4-3.0-3.2-9.2-12.1-12.1-8.8-2.0-3.5 B1 3.6 9.3-3.6 4.0-4.1-3.0-9.2-10.9-10.0-5.5-3.0-1.1 A1B 0.5-2.5-2.4-4.3-9.7-10.9-15.4-18.6-20.4-17.8-16.4-4.8 Precipitation (%) 2050 A2 2.2 1.8-3.9-1.6-7.2-8.8-15.8-21.3-19.9-17.0-13.3-5.5 B1 3.2-0.9-2.8-2.5-6.2-6.2-14.9-15.5-16.2-12.6-9.2-7.5 A1B 0.2-2.1-2.2-3.2-14.0-15.5-20.1-25.0-26.7-27.0-23.0-12.4 2080 A2 0.0 8.3-3.5-6.2-12.1-15.4-24.5-29.1-29.2-32.1-25.7-15.7 B1 1.3-4.5-5.0-8.0-8.9-8.8-14.6-17.9-19.3-18.3-11.8-8.7 A1B 0.0 0.0-0.1 0.0 0.3 0.4 0.5 0.5 0.3 0.2 0.2 0.1 2025 A2-0.1 0.0 0.0 0.1 0.3 0.4 0.6 0.7 0.3 0.3 0.1 0.1 B1 0.1 0.0 0.0 0.0 0.3 0.3 0.6 0.6 0.2 0.2 0.1 0.1 A1B 0.0 0.0 0.0 0.1 0.5 0.7 0.9 0.7 0.5 0.3 0.2 0.0 Sea-level pressure (hpa) 2050 A2-0.1-0.1-0.1 0.1 0.6 0.8 1.0 1.1 0.6 0.5 0.1 0.1 B1 0.1 0.0 0.0 0.0 0.5 0.6 0.8 0.7 0.4 0.2 0.1 0.1 A1B 0.0 0.0-0.1 0.1 0.8 1.0 1.1 0.9 0.7 0.5 0.2 0.1 2080 A2 0.0-0.1-0.1 0.1 0.9 1.2 1.5 1.4 0.8 0.6 0.2 0.1 B1 0.1 0.0 0.0 0.1 0.6 0.8 0.9 0.8 0.5 0.2 0.2 0.1

Multi-model climate change scenarios for southwest Western Australia 21 different time periods (2025, 2050 and 2080). However, the range of monthly increase among GCMs is usually larger than that of annual increase. For example, under the A1B scenario and across the GCMs, January temperature increases about 0.90 C at 2025 (Table 3), similar to the annual temperature increase of 0.91 C. But ranges among GCMs vary from 0.37 C to 1.52 C, which is larger than the annual temperature range of 0.56 to 1.34 C (Table 2). This is true for almost all months, emission scenarios and time periods. Precipitation projections Although averaging across the GCMs and emission scenarios projects that annual precipitation for SWWA will decrease by 16.0 19.9 mm (4.7 5.6%) for 2025, 31.2 38.4 mm (9.1 11.6%) for 2050, and 38.1 56.2 mm (11.2 16.4%) for 2080 (Table 2), comparing individual GCMs shows disagreement on even the sign of the projected precipitation changes. Except for the A1B scenario at 2050, there is always at least one GCM projecting an annual precipitation increase (Table 2). This makes the GCM precipitation results more uncertain and more challenging for decision makers to use. Addressing this problem, various downscaling techniques have been developed to predict regional precipitation conditional on projected atmospheric variables, such as SLP (Charles et al., 2007). Overall, the GCMs project JJA (June, July and August) to have the largest absolute reduction in precipitation amount (Table 3). The precipitation in SON (September, October and November) has the largest percentage decrease, but the absolute amount in SON is smaller than that in JJA. As with temperature, the effect of emission scenarios on precipitation increases with time and the ranges of monthly precipitation are usually larger than that of the annual total. For example, the difference in annual precipitation change between scenarios A1B and B1 is only 3.9 mm (0.9%) for 2025, but it increases to 14.3 mm (4.3%) for 2080 (Table 2). For the A1B scenario, across GCMs, precipitation for May decreases by about 5.7% for 2025, which is almost the same as the annual decrease ( 5.6%). But the ranges among GCMs vary from 25.5% to 32.4%, which is much larger than the projected annual range of 15.6 to 3.7%. Furthermore, the differences between ranges of monthly and annual precipitation produced by different GCMs are much larger than those for temperature. Sea-level pressure projections Averaging across GCMs projects an annual SLP increase of 0.2 hpa for 2025, 0.3 0.4 hpa for 2050, and 0.4 0.6 hpa for 2080 across the scenarios (Table 2). As with precipitation, different GCMs may disagree even on the sign of the annual SLP changes expected over SWWA (Table 2). This is a challenging issue for statistical downscaling requiring further validation of individual GCM for current climate. As was the case with precipitation, the largest change of SLP occurs in JJA. As higher SLP is associated with less precipitation (Li et al., 2005) the changes of SLP are consistent with projected precipitation changes.

22 Guobin Fu & Stephen P. Charles As with temperature and precipitation, the range of monthly SLP change is usually larger than annual. For example, under the A1B scenario, across GCMs, monthly SLP for October and November increase by about 0.2 hpa for 2025, the same as the annual SLP increase. But between GCMs, it ranges from 0.6 to 1.3 hpa and 0.4 to 1.4 hpa, larger than the annual SLP range of 0.1 to 0.6 hpa. Uncertainties Clearly, climatic change projections have large uncertainties, especially on regional scales (Trenberth, 1997; Giorgi & Francisco, 2000; Murphy et al., 2004). These uncertainties stem from a hierarchy of sources (Giorgi & Francisco, 2000): (1) uncertainty related to the forcing scenarios; (2) uncertainty related to the use of different GCMs; (3) uncertainty related to predictions by different realizations of a given scenario with a given GCM; and (4) uncertainty related to sub-gcm grid scale forcing and processes. This study does not assess sub-gcm grid scale forcing and processes. For the uncertainties (1) (3), our results indicate that (2) is the primary sources of uncertainty in the projection of SWWA climatic change. Emission scenario might be important for 50 100 year ahead projections, but is less important for 20-year ahead projection. Different realizations, which are quantified by comparing different model runs for a given scenario and GCM, is also an important contributor to uncertainty, although it is less varied than different GCMs. For example, there are five and six A1B runs for the mri_cgcm2_3_2a and ncar_ccsm3_0 models, respectively. Their annual precipitation changes in 2025 vary from 6.1 to 1.9% and 7.6 to 2.3%, respectively. These are smaller than the across-gcms range, 15.6 to 3.7% in the 2025 A1B scenario (Table 2), but much larger than the different scenario ranges, 5.6 to 4.7% in 2025, 11.6 to 9.1% in 2050, and 16.4 to 11.2% in 2080 (Table 2). Impacts on streamflow and water supply In the last 30 40 years, SWWA precipitation has decreased by 10 15%, which has resulted in a 40% or greater reduction in runoff to reservoirs supplying the Perth metropolitan area (Berti et al., 2004). If future precipitation continues to decrease as most GCMs project, then it will produce a serious challenge for water resources management in the region. The projected magnitude of streamflow decrease has been investigated in other studies; for example, Charles et al. (2007) found that an 11% precipitation decrease in the future would result in a streamflow decrease of 31% for a catchment in SWWA. A significant issue is that most GCMs project precipitation in JJA to have the largest decrease. These three months are the wet season when the reservoirs in SWWA receive the majority of their inflows. CONCLUSIONS Although uncertainties remain due to emission scenarios and GCMs, the climate in SWWA is consistently projected to become drier and hotter in the future based on 19

Multi-model climate change scenarios for southwest Western Australia 23 GCMs. The projected winter rainfall decline is especially worrying. This will have significant impacts on water resources management and, potentially, regional economic development if adaptive management strategies are not successfully implemented. These results should be used to inform regional water resources managers in planning such adaptation strategies. Future research will better quantify the link between projected climate change and SWWA water resource reliability. Acknowledgements This research was part funded by the Australian Greenhouse Office Climate Change Science Program and the Indian Ocean Climate Initiative. REFERENCE Allan, R. J. & Haylock, M. R. (1993) Circulation features associated with the winter rainfall decrease in southwestern Australia. J. Climate 6, 1356 1367. Berti, M. L., Bari, M. A., Charles, S. P. & Hauck, E. J. (2004) Climatic change, catchment runoff and risks to water supplies in the South-West of Western Australia. Department of Environment, Perth, Australia. Cai, W. & Cowan, T. (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. Charles, S. P., Bates, B. C., Whetton, P. H. & Hughes, J. P. (1999) Validation of downscaling models for changed climate conditions: case study of southwestern Australia. Climate Res. 12, 1 14. Charles, S. P., Bates, B. C., Smith, I. N. & Hughes, J. P. (2004) Statistical downscaling of daily precipitation from observed and modelled atmospheric fields. Hydrol. Processes 18, 1373 1394. Charles, S. P., Bari, M. A., Kitsios, A. & Bates, B. C. (2007) GCM downscaling for precipitation and runoff projection in the Serpentine Catchment, Western Australia. Int. J. Climate (in press). Giorgi, F. & Francisco, R. (2000) Evaluating uncertainties in the prediction of regional climate change. Geophys. Res. Lett. 27, 1295 1298. Hope, P. K. (2006) Projected future changes in synoptic systems influencing southwest Western Australia. Climate Dynamics 26, 765 780. Hope, P. K., Drosdowsky, W. & Nicholls, N. (2006) Shifts in the synoptic systems influencing southwest Western Australia. Climate Dynamics 26, 751 764. IOCI (2002) Climate variability and change in south west Western Australia. Indian Ocean Climate Initiative Panel, Australia. Li, F., Chambers, L. E. & Nicholls, N. (2005) Relationships between rainfall in the southwest of Western Australia and near-global patterns of sea-surface temperature and mean sea-level pressure variability. Austral. Met. Mag. 54, 23 33. McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J. & White, K. S. (2001) Climate Change 2001: Working Group II: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK. Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M. & Stainforth, D. A. (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768 772. Phillips, T. J. & Gleckler, P. (2006) Evaluation of continental precipitation in 20th century climate simulations: the utility of multimodel statistics. Water Resour. Res. 42, W03202, doi:10.1029/2005wr004313. Power, S., Sadler, B. & Nicholls, N. (2005) The influence of climate science on water management in Western Australia. Bull. Am. Met. Soc. 86, 839 844. Smith, I. N., McIntosh, P., Ansell, T. J., Reason, C. J. C. & McInnes, K. (2000) Southwest western Australian winter rainfall and its association with Indian Ocean climate variability. Int. J. Climate 20, 1913 1930. Trenberth, K. E. (1997) The use and abuse of climate models. Nature 386, 131 133.