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Influence of global climate model selection on runoff impact assessment
Journal of Hydrology 379 (2009) 172–180
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Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol
Influence of global climate model selection on runoff impact assessment F.H.S. Chiew *, J. Teng, J. Vaze, D.G.C. Kirono CSIRO Land and Water, GPO Box 1666, Canberra ACT 2601, Australia
a r t i c l e
i n f o
Article history: Received 24 February 2009 Received in revised form 6 July 2009 Accepted 5 October 2009 This manuscript was handled by K. Georgakakos, Editor-in-Chief, with the assistance of Günter Blöschl, Associate Editor Keywords: Global climate models Rainfall Runoff GCM assessment Australia
s u m m a r y The future rainfall series used to drive hydrological models in many climate change impact on runoff studies are informed by rainfall simulated by global climate models (GCMs). This paper assesses how the choice of GCMs based on their abilities to reproduce the observed historical rainfall can affect runoff impact assessment. The 23 GCMs used in IPCC 4AR are considered together with 1961–2000 observed rainfall data over southeast Australia. The results indicate that most of the GCMs can reproduce the observed spatial mean annual rainfall pattern, but the errors in the mean seasonal and annual rainfall amounts can be significant. The future mean annual rainfall projections averaged across southeast Australia range from 10% to +3% change per degree global warming, which is amplified as 23% to +4% change in the future mean annual runoff. There is no clear difference in the future rainfall projections between the better and poorer GCMs based on their abilities to reproduce the observed historical rainfall, therefore using only the better GCMs or weights to favour the better GCMs give similar runoff impact assessment results as the use of all the 23 GCMs. The range of future runoff in impact assessment studies is probably best determined using future rainfall projections from the majority of available archived GCM simulations. Ó 2009 Elsevier B.V. All rights reserved.
Introduction Global warming can potentially lead to changes in future regional rainfall and runoff patterns that may require a significant planning response. There are numerous studies in the literature on the modelling of climate change impact on runoff. Rainfall is the key driver in these hydrological modelling studies and a change in rainfall is amplified as a bigger percent change in runoff (Wigley and Jones, 1985; Sankarasubramaniam et al., 2001; Chiew, 2006; Fu et al., 2007). The future catchment-scale rainfall series in many of these studies is informed by the relative difference in the rainfalls simulated by global climate models (GCMs) for the current and future climates (e.g., Schaake, 1990; Lettenmaier and Gan, 1990; Xu, 1999; Chiew and McMahon, 2002; Chiew et al., 2009). This paper assesses the relative abilities of the 23 GCMs used in the IPCC 4AR (Fourth Assessment Report, IPCC, 2007) to simulate various historical rainfall characteristics in southeast Australia, compares the future rainfall projections from these GCMs, and explores how the choice of GCMs used in a study can affect runoff impact assessment. The GCM rainfall is assessed here because (i) rainfall is the main driver of runoff, (ii) the future rainfall series used to drive hydrological models in many climate change impact studies is informed by the relative difference between GCM current and future rainfalls, and (iii) GCM ability to reproduce the observed * Corresponding author. Tel.: +61 2 62465717. E-mail address: [email protected] (F.H.S. Chiew). 0022-1694/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2009.10.004
rainfall pattern is frequently discussed and presented in various forums. There have been other studies that assess GCM ability to simulate historical rainfall and atmospheric-oceanic drivers of rainfall (e.g., Van Oldenborgh et al., 2005; Overland and Wang, 2007; Perkins et al., 2007; Suppiah et al., 2007; Reichler and Kim, 2008; Koutsoyiannis et al., 2008; Watterson, 2008), but this paper uses a large data set over an important region of Australia, considers all the archived GCM simulations used in IPCC 4AR and explores the implications of GCM selection on runoff impact assessment. The paper is organised as follows. The study area and the observed and GCM data used for the study are first described. The various rainfall characteristics modelled by the GCMs over the 1961-2000 period are then assessed against the observed rainfall characteristics. The future annual rainfall projections from the GCMs are then presented. The future rainfall and runoff projections are then compared against the GCMs0 ability to reproduce the historical rainfall characteristics, followed by a discussion of the implications of GCM selection on runoff impact assessment. Study area and data The study area in southeast Australia, and the mean annual rainfall and runoff across the area, is shown in Fig. 1. The entire Murray-Darling Basin and the two biggest Australian cities, Sydney and Melbourne, are in the study area. More than half of Australia’s agricultural income is generated here and more than half of Aus-
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tralia’s population lives in the south-eastern parts of the study area. There is a clear east–west rainfall and runoff gradient and most of the runoff comes from the upland catchments in the south-east. The climate near the coast is temperate and becomes semi-arid and arid further inland towards the north-west and west. Summer runoff dominates in the northern half and winter runoff dominates in the southern half, and some of the subsequent summary results are reported separately for the northern and southern halves of the study area (the horizontal line in Fig. 1 roughly separates the summer half and winter half runoff regions). The major atmospheric-oceanic drivers of rainfall variability in southeast Australia are El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole which describes the sea surface temperature anomaly in the Indian Ocean north-west of Australia and the Southern Annular Mode (SAM) which is the dominant mode of
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the Southern Hemisphere extratropical circulation (Drosdowsky, 2002; Hendon et al., 2007; Shi et al., 2008; SEACI, 2009). The observed rainfall data come from the ‘SILO Data Drill’ (http://www.nrw.qld.gov.au/silo; Jeffrey et al., 2001) which provides daily rainfall (and other climate variables) for 0.05° grids across Australia, interpolated from point measurements made by the Australian Bureau of Meteorology. The mean annual runoff in Fig. 1 is modelled using the lumped conceptual daily rainfall-runoff model, SIMHYD (Chiew et al., 2002). The input data into the model are daily rainfall and potential evapotranspiration. The model parameters are calibrated against streamflow data from 240 relatively unimpaired catchments, and the optimised parameter values from the closest gauged catchment is used to model runoff for the 50,000 0.05° grid cells across the study area (hence the apparent discontinuity in the runoff in Fig. 1). The mean annual runoff sim-
Fig. 1. Study area (map also shows mean annual rainfall and modelled runoff (1961–2000) across the study area, and line separating the predominantly summer runoff region in the north and predominantly winter runoff region in the south).
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Table 1 Summary statistics comparing observed rainfall characteristics with those simulated by the 23 GCMs and projected changes in future rainfall. GCMs
Number of GCM grid cells in study area
Observed
RMSE of mean annual rainfall (mm)
RMSE of mean summer rainfall (mm)
RMSE of mean winter rainfall (mm)
Spatial correlation
Cv of annual rainfall
Linear trend of annual rainfall (mm/year)
NSE of daily rainfall distribution
–
–
–
–
0.28 (0.20 to 0.49)
1.0 (1.5 to +2.9) 0.4 (1.6 to +1.0) +1.4 (1.0 to +2.6) 0.4 (1.3 to +0.6) +2.3 (1.0 to +5.8) 0.5 (3.5 to +1.6) 2.7 (3.8 to 1.8) +0.2 (1.6 to +1.1) 1.3 (2.3 to +0.3) +0.3 (0.1 to +0.8) +1.2 (+0.7 to +3.1) 0.5 (1.6 to +0.4) +0.1 (0.7 to +0.7) 0.1 (3.9 to +1.8) 0.3 (0.4 to +1.8) 2.7 (4.8 to 0.7) 0.7 (1.8 to +1.2) 0.1 (0.6 to +0.6) +0.7 (+0.2 to +2.5) +0.4 (0.3 to +0.9) +0.5 (0.5 to +1.3) 0.0 (2.0 to +1.1) 0.7 (1.3 to +1.1) +0.4 (1.1 to +2.6) 0.0
–
BCCR
29
485
196
92
0.69
0.19 (0.15 to 0.22)
CCCMA T47
19
186
89
40
0.83
0.31 (0.17 to 0.37)
CCCMA T63 CNRM CSIRO-MK3.0
29 29 53
241 196 214
88 110 78
60 78 71
0.90 0.84 0.74
0.27 (0.14 to 0.33) 0.37 (0.18 to 0.44) 0.36 (0.22 to 0.47)
CSIRO-MK3.5
53
207
82
66
0.78
0.45 (0.30 to 0.64)
GFDL 2.0
41
252
99
44
0.86
0.37 (0.26 to 0.51)
GFDL 2.1
42
184
75
51
0.93
0.48 (0.30 to 0.64)
GISS-AOM
20
326
122
60
0.91
0.27 (0.12 to 0.40)
GISS-E-H
14
487
211
68
0.13
0.09 (0.07 to 0.12)
GISS-E-R
14
238
125
29
0.63
0.16 (0.09 to 0.23)
IAP
29
251
98
64
0.86
0.12 (0.11 to 0.17)
INMCM
16
192
81
53
0.86
0.24 (0.19 to 0.28)
IPSL
25
394
153
51
0.51
0.39 (0.16 to 0.65)
MIROC-H
134
201
73
44
0.90
0.23 (0.15 to 0.29)
MIROC-M
29
255
94
43
0.86
0.16 (0.13 to 0.18)
MIUB MPI-ECHAM5 MRI NCAR-CCSM NCAR-PCM1
19 53 29 89 29
174 173 437 245 309
105 43 130 129 135
80 56 95 48 52
0.81 0.89 0.86 0.64 0.76
0.12 (0.09 to 0.15) 0.25 (0.14 to 0.31) 0.16 (0.11 to 0.30) 0.13 (0.10 to 0.16) 0.24 (0.15 to 0.30)
UKHADCM3
25
179
75
52
0.83
0.26 (0.19 to 0.34)
UKHADGEM1
78
163
71
56
0.90
0.28 (0.21 to 0.40)
Median
29
238
98
56
0.84
0.25
– 0.90 (0.70 0.89 (0.76 0.90 (0.75 0.90 (0.71 –
Percent change in future mean annual rainfall
0 1 to 0.96) +1 to 0.97) 6 to 0.97) 17 to 0.97) –10
0.93 (0.80 to 0.99) –
6
0.85 (0.65 to 0.93)
7
–
1
0.85 (0.68 to 0.98) 0.91 (0.79 to 0.98) 0.73 (0.55 to 0.98) –
2
0.83 (0.28 0.92 (0.83 0.93 (0.86 0.72 (0.51 0.88 (0.62 0.89 (0.55 –
+4
13
+3
8 5 +1
to 0.96) +4 to 0.97) 5 to 0.96) 6 to 0.86) 1 to 0.97) 1 to 0.97) 6
–
4
0.89
4
RMSE is root mean square error comparing the difference between GCM and observed mean rainfalls [annual, summer (Dec–Jan–Feb) and winter (Jun–Jul–Aug)] in the GCM grid cells in the study area. Spatial correlation is the linear correlation (R) between GCM and observed mean annual rainfalls in the GCM grid cells in the study area. Cv is the coefficient of variation of annual rainfall (standard deviation divided by mean). The median and the range of 10th to 90th percentile values from the GCM grid cells are shown. Linear trend of the 1961–2000 annual rainfall series is in mm/year. The median and the range of 10th to 90th percentile values from the GCM grid cells are shown. The observed Cv and linear trend are calculated after aggregating the SILO 0.05° rainfall data to 1.75° (similar scale as a GCM with about 30 grid cells in the study area). NSE is the Nash–Sutcliffe efficiency comparing the agreement between the GCM and observed daily rainfall distributions assessed at the GCM grid cells. A value of 1.0 indicates perfect agreement, and values above 0.6 indicate reasonable to good agreement. The median and the range of 10th to 90th percentile values from the GCM grid cells are shown. 1961–2000 data are used, except for the daily rainfall distributions where 1981–2000 data are used. The last column shows the percent change in future mean annual rainfall averaged over the study area per degree global warming (see ‘‘GCM projections of future rainfall”). Details about the GCMs can be found in IPCC (2007) and CSIRO and Bureau of Meteorology (2007).
ulations from SIMHYD and from other rainfall-runoff models that have been also applied to this region are similar, and are generally reliable particularly in the high runoff generation areas in the east and south-east where they are many gauged catchments from which to calibrate the model (Chiew et al., 2008; Zhang and Chiew, 2009).
The monthly rainfall data for the 23 GCMs (and daily rainfall data from 15 GCMs) are obtained from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) website (http:// www-pcmdi.llnl.gov). The spatial resolutions of the GCMs vary from 100 to 400 km with 14–134 GCM grid cells (Table 1) covering the 1.4 million square kilometres study area. For the coastal areas,
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175
Fig. 2. Observed and GCM mean annual rainfalls, averaged over 1961–2000 (the plots are positioned from the driest GCM (lowest mean annual rainfall averaged over the study area) to the wettest GCM).
the GCM grid cell is considered only if it has more than 40% land area. GCM simulated rainfall versus observed rainfall The seasonal and annual rainfall characteristics simulated by the 23 GCMs are compared with the observed rainfall using data
from 1961 to 2000. Fig. 2 shows the mean annual observed rainfall and the mean annual rainfall simulated by the 23 GCMs. Table 1 summarises the comparative statistics of various rainfall characteristics: (i) root mean square error (RMSE) between GCM rainfall and observed rainfall for mean annual, summer (Dec–Jan–Feb) and winter (Jun–Jul–Aug) rainfalls, (ii) spatial correlation of GCM mean annual rainfall versus observed mean annual rainfall across the
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Fig. 3. Percent change in mean annual rainfall per degree global warming from the 23 GCMs (the positions of the GCMs in Figs. 2 and 3 are the same).
study area, (iii) inter-annual rainfall variability in the GCM and observed rainfalls expressed as the coefficient of variation of annual rainfall (Cv, standard deviation divided by the mean), and (iv) linear trend in the GCM and observed 1961–2000 annual rainfall series. The comparison is carried out at the GCM spatial resolution and the calculations of the above statistics are further described in Table 1. The interpretation of the statistics for the different GCMs may be different because the GCMs have different number of grid cells covering the study area, however comparison of the statistics calculated at the 50,000 0.05° grid cells corresponding to the observed gridded rainfall data also show similar results.
The GCM and observed daily rainfall distributions, also at the GCM spatial resolution, are also compared and summarised in Table 1 as the commonly used Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), which characterises the agreement between the two daily rainfall distributions. This comparison is carried out using data from 1981 to 2000 for 15 of the 23 GCMs, as archived daily rainfall simulations are only available for these 15 GCMs over the 1981–2000 period. The GCMs generally reproduce the observed spatial mean annual rainfall patterns, as indicated by the east–west rainfall gradients simulated by most of the GCMs (Fig. 2) and the generally
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Fig. 4. Percent changes in mean annual rainfall and runoff per degree global warming from the 23 GCMs plotted against RMSE of GCM versus observed mean annual rainfall for the entire study area and the northern and southern halves of the study area (note that the y-axis scale in the runoff plots is twice the y-axis scale in the rainfall plots).
greater than 0.7 spatial correlations between GCM rainfall and observed rainfall (Table 1). The GCMs can also generally reproduce the observed daily rainfall distribution at the GCM spatial resolution, as indicated by the relatively high NSE values comparing the GCM and observed daily rainfall distributions. Perkins et al. (2007) present a more detailed analysis of GCM simulation of daily rainfall across Australia. However, there are considerable differences between the rainfall amounts simulated by the GCMs and the observed rainfall. The median RMSE comparing the mean annual, summer and winter rainfalls simulated by the 23 GCMs and the observed rainfall are 238 mm (10th to 90th percentile range from 175 mm to 428 mm), 98 mm (range from 73 mm to 149 mm) and 56 mm (range from 43 mm to 80 mm) respectively, about one third to one half of the annual and seasonal mean rainfalls averaged across the study area. The GCMs can be ranked based on their abilities to reproduce the observed mean annual or seasonal rainfalls, but apart from several GCMs with very high RMSEs (for example, BCCR,
GISS-E-H, IPSL and MRI in the mean annual rainfall comparison), there is no clear threshold in the distribution of RMSE values to separate the better and poorer GCMs. The IPCC 4AR GCM experiments are run to simulate the climatology and are not forced with observed atmospheric-oceanic time series (except CO2). As such, the GCMs cannot simulate the actual historical rainfall time series including the long-term trend in the rainfall. The inter-annual variability of rainfall simulated by the GCMs are of similar magnitude as the observed inter-annual variability, with a bit over half of the GCMs underestimating the interannual variability and a bit under half of the GCMs overestimating the inter-annual variability (Table 1). GCM projections of future rainfall Fig. 3 and the last column of Table 1 show the future rainfall projections simulated by the 23 GCMs. The projections are expressed as the percent change in rainfall per degree global warm-
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Fig. 5. Percent changes in mean annual rainfall and runoff per degree global warming (averaged across the study area) from the 23 GCMs (22 for the second and third rows) plotted against GCM abilities to simulate observed historical climate characteristics assessed in this and two other studies.
ing. This is estimated using the pattern scaling method (Mitchell, 2003; Whetton et al., 2005), where the GCM simulated seasonal rainfall is plotted against the GCM simulated global average surface air temperature (each of the four seasons are considered separately in this paper). A linear regression is then fitted through the data points and the slope of the linear regression gives the change in seasonal rainfall per degree global warming. The slope is generally statistically significant (at a = 0.05) in more than 80% of the GCM grid cells. The absolute change in rainfall is converted against the GCM 1975–2005 modelled baseline to express the change as a percent change. The change in mean annual rainfall in Table 1 is the weighted average of the changes in the four seasons (weighted by the observed mean seasonal rainfall at 0.05°, hence the plots in Fig. 3 showing a resolution that is finer than the GCM resolution). All the data from 2001 to 2100 for the same GCM for different ensemble runs that are available in the archived data base are combined and used in the above analysis to estimate the percent
change in rainfall per degree global warming for the GCM. The entire data set can be used in the pattern scaling method to estimate the future rainfall projections because the method decouples the GCM response from the particular emission scenario used in the simulation and expresses the projections as a change per degree global warming rather than for a particular time in the future. In any case, Whetton et al. (2005) and Suppiah et al. (2007) showed that rainfall tend to scale linearly with global average surface air temperature, and the future rainfall projections estimated using this method are similar to the projections determined by comparing GCM simulation for a future time period and the GCM simulation for a historical period. Fig. 3 and Table 1 show that about three quarters of the GCMs simulate a decrease in future mean annual rainfall averaged over the study area, and almost all the GCMs simulate a decrease in future rainfall in the south and south-east where significant runoff is generated. However, the difference in the future rainfall projec-
F.H.S. Chiew et al. / Journal of Hydrology 379 (2009) 172–180 Table 2 Future runoff projections averaged across the entire study area and the northern and southern halves of the study area (expressed as percent change in runoff per degree global warming), from different methods used to select/combine GCMs based on their relative abilities to simulate the 1961–2000 rainfall (as measured by the RMSE of mean annual rainfall in Table 1 and Fig. 4). Mean
Range (10th to 90th percentile)
Entire study area Equal weight to all GCMs GCM weighted by relative RMSEs Best 10 GCMs (with lowest RMSEs) Best 15 GCMs (with lowest RMSEs)
9 10 10 11
23 31 25 28
to to to to
+4 +4 +4 +3
Northern half Equal weight to all GCMs GCM weighted by relative RMSEs Best 10 GCMs (with lowest RMSEs) Best 15 GCMs (with lowest RMSEs)
7 9 13 10
19 36 36 29
to to to to
+9 +9 +10 +8
Southern half Equal weight to all GCMs GCM weighted by relative RMSEs Best 10 GCMs (with lowest RMSEs) Best 15 GCMs (with lowest RMSEs)
11 11 12 11
26 27 22 25
to to to to
0 0 1 1
tions from the 23 GCMs is large, ranging from a 10% decrease to a 3% increase (10th to 90th percentile values) with a median of 4% decrease in mean annual rainfall per degree global warming averaged over the study area. The difference in the rainfall projections from the different GCMs is significant, particularly as the change in rainfall will be amplified as a percent change in runoff.
Future rainfall and runoff projections versus GCM performance Fig. 4 shows the projected changes in future mean annual rainfall and runoff from the 23 GCMs plotted against the RMSE of GCM versus observed mean annual rainfall for the entire study area and for the northern and southern halves of the study area. The change in mean annual runoff is estimated as 2.5 times the change in mean annual rainfall, based on the rainfall elasticity of runoff of 2–3 in this region estimated by Chiew (2006) using a rainfall-runoff model similar to the one used here as well as a non-parametric analysis of the annual rainfall and runoff series. The changes in rainfall and runoff in Fig. 4 are weighted average values across the regions, and are strongly influenced by the eastern parts where rainfall and runoff is higher (see Fig. 1). The patterns of the rainfall and runoff results are similar, but are not the same because of the much bigger east–west gradient in runoff than in rainfall. The plots again highlight the large range of future rainfall projections, which are amplified in the future runoff projections. The plots also show better agreement between the GCMs in the southern half, where practically all the results show a decline in future rainfall and runoff. The data points in the plots are randomly scattered (the highest correlation in the plots is 0.3, and is not statistically significant) which indicate no clear differences in the future rainfall projections between the better and poorer GCMs. Fig. 5 shows the same rainfall and runoff plots for results averaged across the entire study area, with the x-axis showing the GCM performance based on RMSE of GCM versus observed mean annual rainfall (the two plots on the first row of Figs. 4 and 5 are identical) and based on two other assessments. The x-axis in the second row of plots shows the demerit points from Suppiah et al.’s (2007) analysis of GCM abilities to reproduce the observed average 1961–1990 patterns of rainfall, temperature and mean sea level pressure across the whole of Australia. The x-axis in the third row of plots shows the failure rates from analysis by Smith and Chiew (2009) combining the results from 11 studies that assessed the GCM performance against different criteria (mainly GCM ability to repro-
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duce the observed rainfall, temperature and mean sea level pressure across Australia, but also includes results from studies that consider a larger range of climate variables across the world and GCM ability to simulate ENSO). In all the plots in Fig. 5, the better GCMs plot towards the left of the x-axis. The Suppiah et al. and Smith and Chiew results are only available for 22 of the 23 GCMs (all except the CSIRO-MK3.5 GCM). Like Fig. 4, the plots in Fig. 5 also indicate that there is no clear difference in the future rainfall and runoff projections between the better and poorer GCMs, assessed in three different studies. The GCM assessment against mean annual rainfall in this study is poorly correlated to the Suppiah et al. and Smith and Chiew studies which are weakly correlated to one another. To further explore the implications of GCM selection on runoff impact assessment, Table 2 shows the mean and the range of future runoff projections from different methods of GCM selection based on their abilities to reproduce the observed 1961–2000 mean annual rainfall. Again, because there is no clear difference in the future rainfall projections between the better and poorer GCMs, the use of weights to favour results from the better GCMs and the use of results only from the best 10 or 15 GCMs give future runoff projections that are similar to the use of results from all the 23 GCMs.
Discussion and conclusions This paper assesses the relative abilities of the 23 GCMs used in the IPCC 4AR to simulate various 1961–2000 observed rainfall characteristics, compares the future rainfall projections from these GCMs and explores how the choice of GCMs used in a study can influence runoff impact assessments, using data from a 1.4 million square kilometres region across southeast Australia. The results indicate that most GCMs can reproduce the observed spatial mean annual rainfall pattern across southeast Australia and the observed daily rainfall distribution. However, the difference between the GCMs and observed mean seasonal and annual rainfalls can be significant, with RMSEs of almost half the mean rainfall averaged over the study area. The future mean annual rainfall projections from the 23 GCMs averaged across the study area range from 10% to +3% change per degree global warming, which is amplified as 23% to +4% change per degree global warming in the future mean annual runoff. About three quarters of the GCMs simulate a decrease in future rainfall averaged over the study area, and almost all the GCMs simulate a decrease in future rainfall in the south and south-east where significant runoff is generated. The future runoff impact assessment will be more reliable if it is based on future rainfall projections from the better GCMs. However, it is difficult to determine which GCMs give more reliable future rainfall projections, with some studies selecting the better GCMs based on their abilities to reproduce the historical rainfall characteristics (Perkins et al., 2007; Suppiah et al., 2007; Watterson, 2008) and others considering the GCMs based on their abilities to reproduce the large scale atmospheric-oceanic drivers of rainfall (van Oldenborgh et al., 2005; Overland and Wang, 2007; Shi et al., 2008). In this study, where the GCMs are assessed against their abilities to reproduce the historical mean seasonal and annual rainfalls, there is no clear difference in the future rainfall projections between the better and poorer GCMs. For this reason, using only the better GCMs or using weights to favour the better GCMs give similar runoff impact assessment results for southeast Australia as the use of all the 23 GCMs. It is possible that the selection of GCMs based on other criteria may give different results but it is difficult to decide objectively on what might be the appropriate crite-
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ria. The interpretation of results from multi-model ensembles may also be clouded by the same biases in groups of GCMs. It is more likely that more reliable results will be obtained as global and regional climate modelling improves and the GCMs show more consistency in the future projections. For now, the uncertainty and range of future runoff in impact assessment studies are probably best determined using future climate projections from the majority of available archived GCM simulations. Acknowledgements This study was part of the Murray-Darling Basin Sustainable Yields Project and the South Eastern Australian Climate Initiative carried out in the CSIRO Water for a Healthy Country National Research Flagship. We would like to thank Steve Charles and David Post for reviewing this paper internally, and to the three Journal of Hydrology reviewers and associated editor whose suggestions helped improve the paper. References Chiew, F.H.S., 2006. Estimation of rainfall elasticity of streamflow in Australia. Hydrological Sciences Journal 51, 613–625. Chiew, F.H.S., McMahon, T.A., 2002. Modelling the impacts of climate change on Australian streamflow. Hydrological Processes 16, 1235–1245. Chiew, F.H.S., Peel, M.C., Western, A.W., 2002. Application and testing of the simple rainfall-runoff model SIMHYD. In: Singh, V.P., Frevert, D.K. (Eds.), Mathematical Models of Small Watershed Hydrology and Applications. Water Resources Publication, Littleton, Colorado, pp. 335–367. Chiew, F.H.S., Vaze J., Viney, N.R., Perraud, J.M., Teng, J., Jordan, P.W., Kirono, D., 2008. Estimation of runoff and the impact of climate change and development on runoff across the Murray-Darling Basin. In: Proceedings of the Water Down Under 2008, Adelaide, April 2008, Engineers Australia, CDROM, pp. 1957–1968. ISBN: 0-858-25735-1. Chiew, F.H.S., Teng, J., Vaze, J., Post, D.A., Perraud, J.M., Kirono, D.G.C., Viney, N.R., 2009. Estimating climate change impact on runoff across south-east Australia: method, results and implications of modelling method. Water Resources Research 45, W10414. doi:10.1029/2008WR007338. CSIRO and Australian Bureau of Meteorology, 2007. Climate Change in Australia. Australian Commonwealth Scientific and Industrial Research Organisation and Australian Bureau of Meteorology, Technical Report, www. climatechangeinaustralia.gov.au. Drosdowsky, W., 2002. SST phases and Australia rainfall. Australian Meteorological Magazine 51, 1–12. Fu, G., Charles, S.P., Chiew, F.H.S., 2007. A two-parameter climate elasticity of streamflow index to assess climate change effects on annual streamflow. Water Resources Research 43, W11419. doi:10.1029/2007WR005890. Hendon, H.H., Thompson, D.W.J., Wheeler, M.C., 2007. Australian rainfall and temperature variations associated with the Southern Hemisphere Annular Model. Journal of Climate 20, 2452–2467.
IPCC, 2007. Climate Change 2007: The Physical Basis. Contributions of Working Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.. Jeffrey, S.J., Carter, J.O., Moodie, K.B., Beswick, A.R., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling and Software 16, 309–330. Koutsoyiannis, D., Efstratiadis, A., Mamassis, N., Christofides, A., 2008. On the credibility of climate projections. Hydrological Sciences Journal 53, 671–684. Lettenmaier, D.P., Gan, T.Y., 1990. Hydrologic sensitivity of the Sacramento – San Jaoquin river basin, California, to global warming. Water Resources Research 26, 69–86. Mitchell, T.D., 2003. Pattern scaling. Climatic Change 60, 217–242. Nash, J.E., Sutcliffe, J.V., 1970. River forecasting through conceptual models, 1. A discussion of principles. Journal of Hydrology 10, 282–290. Overland, J.E., Wang, M., 2007. Future climate of the North Pacific Ocean. Eos 88 (16). Perkins, S.E., Pitman, A.J., Holbrook, N.J., McAneney, J., 2007. Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions. Journal of Climate 20, 4356–4376. Reichler, T., Kim, J., 2008. How well do coupled models simulate today’s climate? Bulletin of the American Meteorological Society 89, 303–311. Sankarasubramaniam, A., Vogel, R.M., Limburner, J.F., 2001. Climate elasticity of streamflow in the United States. Water Resources Research 37, 1771–1781. Schaake, J.C., 1990. From climate to flow. In: Waggoner, P.E. (Ed.), Climate Change and US Water Resources. John Wiley, New York, pp. 177–206 (Chapter 8). SEACI, 2009. South Eastern Australian climate initiative factsheets. . Shi, G., Rible, J., Cai, W., Cowan, T., 2008. An interpretation of Australian rainfall projections. Geophysical Research Letters 35, L02702. Smith I.N., Chiew, F.H.S., 2009. GCM Selection for Regional Studies. Final Report for the South Eastern Australian Climate Initiative Project 2.2.5P. . Suppiah, R., Hennessy, K.J., Whetton, P.H., McInnes, K., Macadam, I., Bathols, J., Ricketts, J., Page, C.M., 2007. Australian climate change projections derived from simulations performed for the IPCC 4th assessment report. Australian Meteorological Magazine 56, 131–152. Van Oldenborgh, G.J., Philip, Y.S., Collins, M., 2005. El Nino in a changing climate: a multi-model study. Ocean Science 1, 81–95. Watterson, I.G., 2008. Calculation of probability density functions for temperature and precipitation change under global warming. Journal of Geophysical Research 113, D12106. Whetton, P., McInnes, K.L., Jones, R.J., Hennessy, K.J., Suppiah, R., Page, C.M., Durack, P.J., 2005. Australian Climate Change Projections for Impact Assessment and Policy Application: A Review. CSIRO Marine and Atmospheric Research Paper 001. . Wigley, T.M.L., Jones, P.D., 1985. Influence of precipitation changes and direct CO2 effects on streamflow. Nature 314, 149R–152R. Xu, C.Y., 1999. Climate change and hydrologic models: a review of existing gaps and recent research developments. Water Resources Management 13, 369–382. Zhang, Y.Q., Chiew, F.H.S., 2009. Relative merits of different methods for runoff predictions in ungauged catchments. Water Resources Research 45, W07412. doi:10.1029/2008WR007504.
Contents lists available at ScienceDirect
Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol
Influence of global climate model selection on runoff impact assessment F.H.S. Chiew *, J. Teng, J. Vaze, D.G.C. Kirono CSIRO Land and Water, GPO Box 1666, Canberra ACT 2601, Australia
a r t i c l e
i n f o
Article history: Received 24 February 2009 Received in revised form 6 July 2009 Accepted 5 October 2009 This manuscript was handled by K. Georgakakos, Editor-in-Chief, with the assistance of Günter Blöschl, Associate Editor Keywords: Global climate models Rainfall Runoff GCM assessment Australia
s u m m a r y The future rainfall series used to drive hydrological models in many climate change impact on runoff studies are informed by rainfall simulated by global climate models (GCMs). This paper assesses how the choice of GCMs based on their abilities to reproduce the observed historical rainfall can affect runoff impact assessment. The 23 GCMs used in IPCC 4AR are considered together with 1961–2000 observed rainfall data over southeast Australia. The results indicate that most of the GCMs can reproduce the observed spatial mean annual rainfall pattern, but the errors in the mean seasonal and annual rainfall amounts can be significant. The future mean annual rainfall projections averaged across southeast Australia range from 10% to +3% change per degree global warming, which is amplified as 23% to +4% change in the future mean annual runoff. There is no clear difference in the future rainfall projections between the better and poorer GCMs based on their abilities to reproduce the observed historical rainfall, therefore using only the better GCMs or weights to favour the better GCMs give similar runoff impact assessment results as the use of all the 23 GCMs. The range of future runoff in impact assessment studies is probably best determined using future rainfall projections from the majority of available archived GCM simulations. Ó 2009 Elsevier B.V. All rights reserved.
Introduction Global warming can potentially lead to changes in future regional rainfall and runoff patterns that may require a significant planning response. There are numerous studies in the literature on the modelling of climate change impact on runoff. Rainfall is the key driver in these hydrological modelling studies and a change in rainfall is amplified as a bigger percent change in runoff (Wigley and Jones, 1985; Sankarasubramaniam et al., 2001; Chiew, 2006; Fu et al., 2007). The future catchment-scale rainfall series in many of these studies is informed by the relative difference in the rainfalls simulated by global climate models (GCMs) for the current and future climates (e.g., Schaake, 1990; Lettenmaier and Gan, 1990; Xu, 1999; Chiew and McMahon, 2002; Chiew et al., 2009). This paper assesses the relative abilities of the 23 GCMs used in the IPCC 4AR (Fourth Assessment Report, IPCC, 2007) to simulate various historical rainfall characteristics in southeast Australia, compares the future rainfall projections from these GCMs, and explores how the choice of GCMs used in a study can affect runoff impact assessment. The GCM rainfall is assessed here because (i) rainfall is the main driver of runoff, (ii) the future rainfall series used to drive hydrological models in many climate change impact studies is informed by the relative difference between GCM current and future rainfalls, and (iii) GCM ability to reproduce the observed * Corresponding author. Tel.: +61 2 62465717. E-mail address: [email protected] (F.H.S. Chiew). 0022-1694/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2009.10.004
rainfall pattern is frequently discussed and presented in various forums. There have been other studies that assess GCM ability to simulate historical rainfall and atmospheric-oceanic drivers of rainfall (e.g., Van Oldenborgh et al., 2005; Overland and Wang, 2007; Perkins et al., 2007; Suppiah et al., 2007; Reichler and Kim, 2008; Koutsoyiannis et al., 2008; Watterson, 2008), but this paper uses a large data set over an important region of Australia, considers all the archived GCM simulations used in IPCC 4AR and explores the implications of GCM selection on runoff impact assessment. The paper is organised as follows. The study area and the observed and GCM data used for the study are first described. The various rainfall characteristics modelled by the GCMs over the 1961-2000 period are then assessed against the observed rainfall characteristics. The future annual rainfall projections from the GCMs are then presented. The future rainfall and runoff projections are then compared against the GCMs0 ability to reproduce the historical rainfall characteristics, followed by a discussion of the implications of GCM selection on runoff impact assessment. Study area and data The study area in southeast Australia, and the mean annual rainfall and runoff across the area, is shown in Fig. 1. The entire Murray-Darling Basin and the two biggest Australian cities, Sydney and Melbourne, are in the study area. More than half of Australia’s agricultural income is generated here and more than half of Aus-
F.H.S. Chiew et al. / Journal of Hydrology 379 (2009) 172–180
tralia’s population lives in the south-eastern parts of the study area. There is a clear east–west rainfall and runoff gradient and most of the runoff comes from the upland catchments in the south-east. The climate near the coast is temperate and becomes semi-arid and arid further inland towards the north-west and west. Summer runoff dominates in the northern half and winter runoff dominates in the southern half, and some of the subsequent summary results are reported separately for the northern and southern halves of the study area (the horizontal line in Fig. 1 roughly separates the summer half and winter half runoff regions). The major atmospheric-oceanic drivers of rainfall variability in southeast Australia are El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole which describes the sea surface temperature anomaly in the Indian Ocean north-west of Australia and the Southern Annular Mode (SAM) which is the dominant mode of
173
the Southern Hemisphere extratropical circulation (Drosdowsky, 2002; Hendon et al., 2007; Shi et al., 2008; SEACI, 2009). The observed rainfall data come from the ‘SILO Data Drill’ (http://www.nrw.qld.gov.au/silo; Jeffrey et al., 2001) which provides daily rainfall (and other climate variables) for 0.05° grids across Australia, interpolated from point measurements made by the Australian Bureau of Meteorology. The mean annual runoff in Fig. 1 is modelled using the lumped conceptual daily rainfall-runoff model, SIMHYD (Chiew et al., 2002). The input data into the model are daily rainfall and potential evapotranspiration. The model parameters are calibrated against streamflow data from 240 relatively unimpaired catchments, and the optimised parameter values from the closest gauged catchment is used to model runoff for the 50,000 0.05° grid cells across the study area (hence the apparent discontinuity in the runoff in Fig. 1). The mean annual runoff sim-
Fig. 1. Study area (map also shows mean annual rainfall and modelled runoff (1961–2000) across the study area, and line separating the predominantly summer runoff region in the north and predominantly winter runoff region in the south).
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Table 1 Summary statistics comparing observed rainfall characteristics with those simulated by the 23 GCMs and projected changes in future rainfall. GCMs
Number of GCM grid cells in study area
Observed
RMSE of mean annual rainfall (mm)
RMSE of mean summer rainfall (mm)
RMSE of mean winter rainfall (mm)
Spatial correlation
Cv of annual rainfall
Linear trend of annual rainfall (mm/year)
NSE of daily rainfall distribution
–
–
–
–
0.28 (0.20 to 0.49)
1.0 (1.5 to +2.9) 0.4 (1.6 to +1.0) +1.4 (1.0 to +2.6) 0.4 (1.3 to +0.6) +2.3 (1.0 to +5.8) 0.5 (3.5 to +1.6) 2.7 (3.8 to 1.8) +0.2 (1.6 to +1.1) 1.3 (2.3 to +0.3) +0.3 (0.1 to +0.8) +1.2 (+0.7 to +3.1) 0.5 (1.6 to +0.4) +0.1 (0.7 to +0.7) 0.1 (3.9 to +1.8) 0.3 (0.4 to +1.8) 2.7 (4.8 to 0.7) 0.7 (1.8 to +1.2) 0.1 (0.6 to +0.6) +0.7 (+0.2 to +2.5) +0.4 (0.3 to +0.9) +0.5 (0.5 to +1.3) 0.0 (2.0 to +1.1) 0.7 (1.3 to +1.1) +0.4 (1.1 to +2.6) 0.0
–
BCCR
29
485
196
92
0.69
0.19 (0.15 to 0.22)
CCCMA T47
19
186
89
40
0.83
0.31 (0.17 to 0.37)
CCCMA T63 CNRM CSIRO-MK3.0
29 29 53
241 196 214
88 110 78
60 78 71
0.90 0.84 0.74
0.27 (0.14 to 0.33) 0.37 (0.18 to 0.44) 0.36 (0.22 to 0.47)
CSIRO-MK3.5
53
207
82
66
0.78
0.45 (0.30 to 0.64)
GFDL 2.0
41
252
99
44
0.86
0.37 (0.26 to 0.51)
GFDL 2.1
42
184
75
51
0.93
0.48 (0.30 to 0.64)
GISS-AOM
20
326
122
60
0.91
0.27 (0.12 to 0.40)
GISS-E-H
14
487
211
68
0.13
0.09 (0.07 to 0.12)
GISS-E-R
14
238
125
29
0.63
0.16 (0.09 to 0.23)
IAP
29
251
98
64
0.86
0.12 (0.11 to 0.17)
INMCM
16
192
81
53
0.86
0.24 (0.19 to 0.28)
IPSL
25
394
153
51
0.51
0.39 (0.16 to 0.65)
MIROC-H
134
201
73
44
0.90
0.23 (0.15 to 0.29)
MIROC-M
29
255
94
43
0.86
0.16 (0.13 to 0.18)
MIUB MPI-ECHAM5 MRI NCAR-CCSM NCAR-PCM1
19 53 29 89 29
174 173 437 245 309
105 43 130 129 135
80 56 95 48 52
0.81 0.89 0.86 0.64 0.76
0.12 (0.09 to 0.15) 0.25 (0.14 to 0.31) 0.16 (0.11 to 0.30) 0.13 (0.10 to 0.16) 0.24 (0.15 to 0.30)
UKHADCM3
25
179
75
52
0.83
0.26 (0.19 to 0.34)
UKHADGEM1
78
163
71
56
0.90
0.28 (0.21 to 0.40)
Median
29
238
98
56
0.84
0.25
– 0.90 (0.70 0.89 (0.76 0.90 (0.75 0.90 (0.71 –
Percent change in future mean annual rainfall
0 1 to 0.96) +1 to 0.97) 6 to 0.97) 17 to 0.97) –10
0.93 (0.80 to 0.99) –
6
0.85 (0.65 to 0.93)
7
–
1
0.85 (0.68 to 0.98) 0.91 (0.79 to 0.98) 0.73 (0.55 to 0.98) –
2
0.83 (0.28 0.92 (0.83 0.93 (0.86 0.72 (0.51 0.88 (0.62 0.89 (0.55 –
+4
13
+3
8 5 +1
to 0.96) +4 to 0.97) 5 to 0.96) 6 to 0.86) 1 to 0.97) 1 to 0.97) 6
–
4
0.89
4
RMSE is root mean square error comparing the difference between GCM and observed mean rainfalls [annual, summer (Dec–Jan–Feb) and winter (Jun–Jul–Aug)] in the GCM grid cells in the study area. Spatial correlation is the linear correlation (R) between GCM and observed mean annual rainfalls in the GCM grid cells in the study area. Cv is the coefficient of variation of annual rainfall (standard deviation divided by mean). The median and the range of 10th to 90th percentile values from the GCM grid cells are shown. Linear trend of the 1961–2000 annual rainfall series is in mm/year. The median and the range of 10th to 90th percentile values from the GCM grid cells are shown. The observed Cv and linear trend are calculated after aggregating the SILO 0.05° rainfall data to 1.75° (similar scale as a GCM with about 30 grid cells in the study area). NSE is the Nash–Sutcliffe efficiency comparing the agreement between the GCM and observed daily rainfall distributions assessed at the GCM grid cells. A value of 1.0 indicates perfect agreement, and values above 0.6 indicate reasonable to good agreement. The median and the range of 10th to 90th percentile values from the GCM grid cells are shown. 1961–2000 data are used, except for the daily rainfall distributions where 1981–2000 data are used. The last column shows the percent change in future mean annual rainfall averaged over the study area per degree global warming (see ‘‘GCM projections of future rainfall”). Details about the GCMs can be found in IPCC (2007) and CSIRO and Bureau of Meteorology (2007).
ulations from SIMHYD and from other rainfall-runoff models that have been also applied to this region are similar, and are generally reliable particularly in the high runoff generation areas in the east and south-east where they are many gauged catchments from which to calibrate the model (Chiew et al., 2008; Zhang and Chiew, 2009).
The monthly rainfall data for the 23 GCMs (and daily rainfall data from 15 GCMs) are obtained from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) website (http:// www-pcmdi.llnl.gov). The spatial resolutions of the GCMs vary from 100 to 400 km with 14–134 GCM grid cells (Table 1) covering the 1.4 million square kilometres study area. For the coastal areas,
F.H.S. Chiew et al. / Journal of Hydrology 379 (2009) 172–180
175
Fig. 2. Observed and GCM mean annual rainfalls, averaged over 1961–2000 (the plots are positioned from the driest GCM (lowest mean annual rainfall averaged over the study area) to the wettest GCM).
the GCM grid cell is considered only if it has more than 40% land area. GCM simulated rainfall versus observed rainfall The seasonal and annual rainfall characteristics simulated by the 23 GCMs are compared with the observed rainfall using data
from 1961 to 2000. Fig. 2 shows the mean annual observed rainfall and the mean annual rainfall simulated by the 23 GCMs. Table 1 summarises the comparative statistics of various rainfall characteristics: (i) root mean square error (RMSE) between GCM rainfall and observed rainfall for mean annual, summer (Dec–Jan–Feb) and winter (Jun–Jul–Aug) rainfalls, (ii) spatial correlation of GCM mean annual rainfall versus observed mean annual rainfall across the
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Fig. 3. Percent change in mean annual rainfall per degree global warming from the 23 GCMs (the positions of the GCMs in Figs. 2 and 3 are the same).
study area, (iii) inter-annual rainfall variability in the GCM and observed rainfalls expressed as the coefficient of variation of annual rainfall (Cv, standard deviation divided by the mean), and (iv) linear trend in the GCM and observed 1961–2000 annual rainfall series. The comparison is carried out at the GCM spatial resolution and the calculations of the above statistics are further described in Table 1. The interpretation of the statistics for the different GCMs may be different because the GCMs have different number of grid cells covering the study area, however comparison of the statistics calculated at the 50,000 0.05° grid cells corresponding to the observed gridded rainfall data also show similar results.
The GCM and observed daily rainfall distributions, also at the GCM spatial resolution, are also compared and summarised in Table 1 as the commonly used Nash–Sutcliffe efficiency (NSE) (Nash and Sutcliffe, 1970), which characterises the agreement between the two daily rainfall distributions. This comparison is carried out using data from 1981 to 2000 for 15 of the 23 GCMs, as archived daily rainfall simulations are only available for these 15 GCMs over the 1981–2000 period. The GCMs generally reproduce the observed spatial mean annual rainfall patterns, as indicated by the east–west rainfall gradients simulated by most of the GCMs (Fig. 2) and the generally
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177
Fig. 4. Percent changes in mean annual rainfall and runoff per degree global warming from the 23 GCMs plotted against RMSE of GCM versus observed mean annual rainfall for the entire study area and the northern and southern halves of the study area (note that the y-axis scale in the runoff plots is twice the y-axis scale in the rainfall plots).
greater than 0.7 spatial correlations between GCM rainfall and observed rainfall (Table 1). The GCMs can also generally reproduce the observed daily rainfall distribution at the GCM spatial resolution, as indicated by the relatively high NSE values comparing the GCM and observed daily rainfall distributions. Perkins et al. (2007) present a more detailed analysis of GCM simulation of daily rainfall across Australia. However, there are considerable differences between the rainfall amounts simulated by the GCMs and the observed rainfall. The median RMSE comparing the mean annual, summer and winter rainfalls simulated by the 23 GCMs and the observed rainfall are 238 mm (10th to 90th percentile range from 175 mm to 428 mm), 98 mm (range from 73 mm to 149 mm) and 56 mm (range from 43 mm to 80 mm) respectively, about one third to one half of the annual and seasonal mean rainfalls averaged across the study area. The GCMs can be ranked based on their abilities to reproduce the observed mean annual or seasonal rainfalls, but apart from several GCMs with very high RMSEs (for example, BCCR,
GISS-E-H, IPSL and MRI in the mean annual rainfall comparison), there is no clear threshold in the distribution of RMSE values to separate the better and poorer GCMs. The IPCC 4AR GCM experiments are run to simulate the climatology and are not forced with observed atmospheric-oceanic time series (except CO2). As such, the GCMs cannot simulate the actual historical rainfall time series including the long-term trend in the rainfall. The inter-annual variability of rainfall simulated by the GCMs are of similar magnitude as the observed inter-annual variability, with a bit over half of the GCMs underestimating the interannual variability and a bit under half of the GCMs overestimating the inter-annual variability (Table 1). GCM projections of future rainfall Fig. 3 and the last column of Table 1 show the future rainfall projections simulated by the 23 GCMs. The projections are expressed as the percent change in rainfall per degree global warm-
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Fig. 5. Percent changes in mean annual rainfall and runoff per degree global warming (averaged across the study area) from the 23 GCMs (22 for the second and third rows) plotted against GCM abilities to simulate observed historical climate characteristics assessed in this and two other studies.
ing. This is estimated using the pattern scaling method (Mitchell, 2003; Whetton et al., 2005), where the GCM simulated seasonal rainfall is plotted against the GCM simulated global average surface air temperature (each of the four seasons are considered separately in this paper). A linear regression is then fitted through the data points and the slope of the linear regression gives the change in seasonal rainfall per degree global warming. The slope is generally statistically significant (at a = 0.05) in more than 80% of the GCM grid cells. The absolute change in rainfall is converted against the GCM 1975–2005 modelled baseline to express the change as a percent change. The change in mean annual rainfall in Table 1 is the weighted average of the changes in the four seasons (weighted by the observed mean seasonal rainfall at 0.05°, hence the plots in Fig. 3 showing a resolution that is finer than the GCM resolution). All the data from 2001 to 2100 for the same GCM for different ensemble runs that are available in the archived data base are combined and used in the above analysis to estimate the percent
change in rainfall per degree global warming for the GCM. The entire data set can be used in the pattern scaling method to estimate the future rainfall projections because the method decouples the GCM response from the particular emission scenario used in the simulation and expresses the projections as a change per degree global warming rather than for a particular time in the future. In any case, Whetton et al. (2005) and Suppiah et al. (2007) showed that rainfall tend to scale linearly with global average surface air temperature, and the future rainfall projections estimated using this method are similar to the projections determined by comparing GCM simulation for a future time period and the GCM simulation for a historical period. Fig. 3 and Table 1 show that about three quarters of the GCMs simulate a decrease in future mean annual rainfall averaged over the study area, and almost all the GCMs simulate a decrease in future rainfall in the south and south-east where significant runoff is generated. However, the difference in the future rainfall projec-
F.H.S. Chiew et al. / Journal of Hydrology 379 (2009) 172–180 Table 2 Future runoff projections averaged across the entire study area and the northern and southern halves of the study area (expressed as percent change in runoff per degree global warming), from different methods used to select/combine GCMs based on their relative abilities to simulate the 1961–2000 rainfall (as measured by the RMSE of mean annual rainfall in Table 1 and Fig. 4). Mean
Range (10th to 90th percentile)
Entire study area Equal weight to all GCMs GCM weighted by relative RMSEs Best 10 GCMs (with lowest RMSEs) Best 15 GCMs (with lowest RMSEs)
9 10 10 11
23 31 25 28
to to to to
+4 +4 +4 +3
Northern half Equal weight to all GCMs GCM weighted by relative RMSEs Best 10 GCMs (with lowest RMSEs) Best 15 GCMs (with lowest RMSEs)
7 9 13 10
19 36 36 29
to to to to
+9 +9 +10 +8
Southern half Equal weight to all GCMs GCM weighted by relative RMSEs Best 10 GCMs (with lowest RMSEs) Best 15 GCMs (with lowest RMSEs)
11 11 12 11
26 27 22 25
to to to to
0 0 1 1
tions from the 23 GCMs is large, ranging from a 10% decrease to a 3% increase (10th to 90th percentile values) with a median of 4% decrease in mean annual rainfall per degree global warming averaged over the study area. The difference in the rainfall projections from the different GCMs is significant, particularly as the change in rainfall will be amplified as a percent change in runoff.
Future rainfall and runoff projections versus GCM performance Fig. 4 shows the projected changes in future mean annual rainfall and runoff from the 23 GCMs plotted against the RMSE of GCM versus observed mean annual rainfall for the entire study area and for the northern and southern halves of the study area. The change in mean annual runoff is estimated as 2.5 times the change in mean annual rainfall, based on the rainfall elasticity of runoff of 2–3 in this region estimated by Chiew (2006) using a rainfall-runoff model similar to the one used here as well as a non-parametric analysis of the annual rainfall and runoff series. The changes in rainfall and runoff in Fig. 4 are weighted average values across the regions, and are strongly influenced by the eastern parts where rainfall and runoff is higher (see Fig. 1). The patterns of the rainfall and runoff results are similar, but are not the same because of the much bigger east–west gradient in runoff than in rainfall. The plots again highlight the large range of future rainfall projections, which are amplified in the future runoff projections. The plots also show better agreement between the GCMs in the southern half, where practically all the results show a decline in future rainfall and runoff. The data points in the plots are randomly scattered (the highest correlation in the plots is 0.3, and is not statistically significant) which indicate no clear differences in the future rainfall projections between the better and poorer GCMs. Fig. 5 shows the same rainfall and runoff plots for results averaged across the entire study area, with the x-axis showing the GCM performance based on RMSE of GCM versus observed mean annual rainfall (the two plots on the first row of Figs. 4 and 5 are identical) and based on two other assessments. The x-axis in the second row of plots shows the demerit points from Suppiah et al.’s (2007) analysis of GCM abilities to reproduce the observed average 1961–1990 patterns of rainfall, temperature and mean sea level pressure across the whole of Australia. The x-axis in the third row of plots shows the failure rates from analysis by Smith and Chiew (2009) combining the results from 11 studies that assessed the GCM performance against different criteria (mainly GCM ability to repro-
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duce the observed rainfall, temperature and mean sea level pressure across Australia, but also includes results from studies that consider a larger range of climate variables across the world and GCM ability to simulate ENSO). In all the plots in Fig. 5, the better GCMs plot towards the left of the x-axis. The Suppiah et al. and Smith and Chiew results are only available for 22 of the 23 GCMs (all except the CSIRO-MK3.5 GCM). Like Fig. 4, the plots in Fig. 5 also indicate that there is no clear difference in the future rainfall and runoff projections between the better and poorer GCMs, assessed in three different studies. The GCM assessment against mean annual rainfall in this study is poorly correlated to the Suppiah et al. and Smith and Chiew studies which are weakly correlated to one another. To further explore the implications of GCM selection on runoff impact assessment, Table 2 shows the mean and the range of future runoff projections from different methods of GCM selection based on their abilities to reproduce the observed 1961–2000 mean annual rainfall. Again, because there is no clear difference in the future rainfall projections between the better and poorer GCMs, the use of weights to favour results from the better GCMs and the use of results only from the best 10 or 15 GCMs give future runoff projections that are similar to the use of results from all the 23 GCMs.
Discussion and conclusions This paper assesses the relative abilities of the 23 GCMs used in the IPCC 4AR to simulate various 1961–2000 observed rainfall characteristics, compares the future rainfall projections from these GCMs and explores how the choice of GCMs used in a study can influence runoff impact assessments, using data from a 1.4 million square kilometres region across southeast Australia. The results indicate that most GCMs can reproduce the observed spatial mean annual rainfall pattern across southeast Australia and the observed daily rainfall distribution. However, the difference between the GCMs and observed mean seasonal and annual rainfalls can be significant, with RMSEs of almost half the mean rainfall averaged over the study area. The future mean annual rainfall projections from the 23 GCMs averaged across the study area range from 10% to +3% change per degree global warming, which is amplified as 23% to +4% change per degree global warming in the future mean annual runoff. About three quarters of the GCMs simulate a decrease in future rainfall averaged over the study area, and almost all the GCMs simulate a decrease in future rainfall in the south and south-east where significant runoff is generated. The future runoff impact assessment will be more reliable if it is based on future rainfall projections from the better GCMs. However, it is difficult to determine which GCMs give more reliable future rainfall projections, with some studies selecting the better GCMs based on their abilities to reproduce the historical rainfall characteristics (Perkins et al., 2007; Suppiah et al., 2007; Watterson, 2008) and others considering the GCMs based on their abilities to reproduce the large scale atmospheric-oceanic drivers of rainfall (van Oldenborgh et al., 2005; Overland and Wang, 2007; Shi et al., 2008). In this study, where the GCMs are assessed against their abilities to reproduce the historical mean seasonal and annual rainfalls, there is no clear difference in the future rainfall projections between the better and poorer GCMs. For this reason, using only the better GCMs or using weights to favour the better GCMs give similar runoff impact assessment results for southeast Australia as the use of all the 23 GCMs. It is possible that the selection of GCMs based on other criteria may give different results but it is difficult to decide objectively on what might be the appropriate crite-
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ria. The interpretation of results from multi-model ensembles may also be clouded by the same biases in groups of GCMs. It is more likely that more reliable results will be obtained as global and regional climate modelling improves and the GCMs show more consistency in the future projections. For now, the uncertainty and range of future runoff in impact assessment studies are probably best determined using future climate projections from the majority of available archived GCM simulations. Acknowledgements This study was part of the Murray-Darling Basin Sustainable Yields Project and the South Eastern Australian Climate Initiative carried out in the CSIRO Water for a Healthy Country National Research Flagship. We would like to thank Steve Charles and David Post for reviewing this paper internally, and to the three Journal of Hydrology reviewers and associated editor whose suggestions helped improve the paper. References Chiew, F.H.S., 2006. Estimation of rainfall elasticity of streamflow in Australia. Hydrological Sciences Journal 51, 613–625. Chiew, F.H.S., McMahon, T.A., 2002. Modelling the impacts of climate change on Australian streamflow. Hydrological Processes 16, 1235–1245. Chiew, F.H.S., Peel, M.C., Western, A.W., 2002. Application and testing of the simple rainfall-runoff model SIMHYD. In: Singh, V.P., Frevert, D.K. (Eds.), Mathematical Models of Small Watershed Hydrology and Applications. Water Resources Publication, Littleton, Colorado, pp. 335–367. Chiew, F.H.S., Vaze J., Viney, N.R., Perraud, J.M., Teng, J., Jordan, P.W., Kirono, D., 2008. Estimation of runoff and the impact of climate change and development on runoff across the Murray-Darling Basin. In: Proceedings of the Water Down Under 2008, Adelaide, April 2008, Engineers Australia, CDROM, pp. 1957–1968. ISBN: 0-858-25735-1. Chiew, F.H.S., Teng, J., Vaze, J., Post, D.A., Perraud, J.M., Kirono, D.G.C., Viney, N.R., 2009. Estimating climate change impact on runoff across south-east Australia: method, results and implications of modelling method. Water Resources Research 45, W10414. doi:10.1029/2008WR007338. CSIRO and Australian Bureau of Meteorology, 2007. Climate Change in Australia. Australian Commonwealth Scientific and Industrial Research Organisation and Australian Bureau of Meteorology, Technical Report, www. climatechangeinaustralia.gov.au. Drosdowsky, W., 2002. SST phases and Australia rainfall. Australian Meteorological Magazine 51, 1–12. Fu, G., Charles, S.P., Chiew, F.H.S., 2007. A two-parameter climate elasticity of streamflow index to assess climate change effects on annual streamflow. Water Resources Research 43, W11419. doi:10.1029/2007WR005890. Hendon, H.H., Thompson, D.W.J., Wheeler, M.C., 2007. Australian rainfall and temperature variations associated with the Southern Hemisphere Annular Model. Journal of Climate 20, 2452–2467.
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