Representation of the Australian sub-tropical ridge in the CMIP3 models

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 33: (2013) Published online 14 December 2011 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3406 Representation of the Australian sub-tropical ridge in the CMIP3 models David M. Kent, a * Dewi G. C. Kirono, a Bertrand Timbal a and Francis H. S. Chiew b a Centre for Australian Weather and Climate Research (CAWCR), a partnership between CSIRO and the Bureau of Meteorology, Australia b CSIRO Land and Water, Black Mountain, Canberra, Australia ABSTRACT: This study examines the representation of one of the key drivers of southeastern Australian rainfall, the sub-tropical ridge (STR) in mean sea level pressure, in the World Climate Research Programme s Coupled Model Intercomparison Project phase 3 multi-model dataset. In particular, the mean state and variability of the ridge s intensity and position is diagnosed and compared to observations for the 20th Century. The mean annual cycle of the STR intensity and position are found to be moderately well represented compared to two observational datasets. The models project that STR intensity will increase over the 21st Century whilst its mean position will be further south. Given the historical relationship between the STR and rainfall, this suggests that southeastern Australia will most likely be drier in the future. Most models fail to reproduce the spatial pattern of correlation between the STR intensity and precipitation but some models show a correlation (significant in some cases) between high rainfall and a weaker STR intensity. However, these correlations are much weaker than the observations. Given that the annual cycle of the STR has significant implications for rainfall in southeastern Australia, this is of some concern and leads to questions about which processes are driving rainfall in the models. Copyright 2011 Royal Meteorological Society KEY WORDS sub-tropical ridge; CMIP3; southeastern Australia; model selection; rainfall projection Received 3 September 2010; Revised 6 May 2011; Accepted 16 October Introduction The sub-tropical ridge (STR) is a key driver of rainfall in southeastern Australia (Drosdowsky, 2005; Larsen and Nicholls, 2009; Timbal et al., 2010; Timbal and Drosdowsky, (submitted)); it is strongly related to the frequency and strength of the extra-tropical storms and frontal systems that provide most of the region s rainfall (Wright, 1988, 1989). In order to inform climate projection studies for southeastern Australia, an evaluation of the performance of global circulation models (GCMs) in representing major oceanic and atmospheric climate drivers, such as the STR, is important. There have been many studies conducted of the representation of the mean state and variability of fields such as precipitation, temperature and evaporation in models on a regional and global basis (e.g. Suppiah et al., 2007; Kirono and Kent, 2010) as well as the representation of broad-scale circulation and modes of variability such as El Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM) (e.g. Cai et al., 2009; Frederiksen et al., 2009; Van Oldenborgh et al., 2005). Here we examine the STR as it has a strong association with rainfall in southeastern Australia (Pittock, 1971; Timbal and Drosdowsky, (submitted)). Correspondence to: D. M. Kent, CSIRO Marine and Atmospheric Research, Aspendale, Victoria, 3195, Australia. David.Kent@csiro.au The STR is a ridge of high pressure associated with the downward component of the Hadley cell. It has a strong seasonal cycle in both its location and its intensity. Over the austral summer months it is generally weaker and further south (around 40 S and 1013 hpa) before shifting north and strengthening during the austral winter (30 S and 1023 hpa). Generally, as the ridge moves north so too does the band of eastward-propagating, rain-bearing low pressure and frontal systems. Climatologically, this change occurs rapidly between November and January and then again between March and May. This cycle is shown in the top panel of Figure 1. The lower two panels show the correlation between the observed annual STR-I (b) and STR-P (c) with annual rainfall across Australia for the 20th Century. They show the strong relationship between a more intense STR and decreased rainfall in southeastern and southwestern Australia and between a more southerly position of the STR and decreased rainfall in similar regions. There is also a marked seasonality to the relationship between the STR s position and intensity and Australian rainfall that is masked by this annual relationship. There have been a number of studies of the dynamics, trends and impacts of the STR. Drosdowsky (2005) compared many of the early attempts and methods used to characterize the STR position including Pittock (1971, 1973) and Das (1956) before presenting an objective STR-P metric based on that of Pittock (1973). This work was built on by Larsen and Nicholls (2009) and Copyright 2011 Royal Meteorological Society

2 THE AUSTRALIAN SUB-TROPICAL RIDGE IN THE CMIP3 MODELS 49 autumn STR-P with the Larsen and Nicholls (2009) and Timbal and Drosdowsky (submitted) studies going on to identify trends in STR-I as being the dominant driver of decreases in southeast Australian rainfall. Finally, Whan et al. (submitted) identifies the joint contribution by the STR intensification and southerly shift in position on autumn and winter rainfall across southeast Australia. It is in this context that we endeavour to answer three primary questions: (1) do the GCMs comprising the World Climate Research Programme s (WCRP) Coupled Model Intercomparison Project Phase 3 (CMIP3) multimodel dataset represent the STR? (2) do they replicate its relationship with rainfall (Section 3.1)? and (3) how do they represent recent and future trends (Section 3.2)? Figure 1. The observed annual cycle (a) of the STR for the BoM (black) and NCEP (grey) datasets along with the observed correlation between the STR-I (b) and STR-P (c) with rainfall between 1900 and 2008 for the BoM dataset. This figure is available in colour online at wileyonlinelibrary.com/journal/joc Williams and Stone (2009), both of whom found that the choice of dataset used to calculate STR indices has a marked effect on the characteristics of the diagnosed STR. Both studies also identify poleward trends in 2. Method and data There have been a number of different definitions of the STR over the last 20 years, a summary of many can be found in Drosdowsky (2005); however, two parameters representing characteristics of the STR are calculated here after the method of Drosdowsky (2005). The two metrics used are the latitude (referred to as position or STR-P) and magnitude (referred to as intensity or STR-I) of the maximum mean sea level pressure (MSLP) between 9 and 45 S, and a selected longitude band (the selection of this band is discussed below and in Section 3.1). A local zonal mean is calculated across the selected longitude band, these values are then interpolated to regular 0.5 degree latitude points using a cubic spline. The STR is defined to be at the maximum value on this meridional profile. If the maximum of the interpolated pressure is found to be at the extremes of the latitudinal range (i.e. at 9 or 45 S) then a local search is performed around the location of the maximum at the previous time step. Monthly time series of both STR-I and STR-P are derived for each of the observational datasets and for the GCMs described below. Two different datasets are used to compare the GCM STR characteristics to the observed historical characteristics. The first is the monthly time series derived by Drosdowsky (2005, p.1295) (updated to 2008 by Timbal and Drosdowsky (submitted)) who used station MSLP data from 1890 to 2008 between 145 and 150 E interpolated a 1 grid (hereafter known as the BoM dataset). This dataset is based on station data, unlike the remaining datasets. With this in mind, most of the metrics presented here make use of the second observation-based dataset with the BoM dataset used to ground truth the results. The second observed time series is derived from MSLP fields from the National Centers for Environmental Prediction (NCEP) reanalysis (Kalnay et al., 1996) from 1948 to A longitudinal band of E is used as this is the range used by the BoM time-series. This NCEP STR time series is derived in the same way as each of the GCM time series; therefore, it is used as the reference dataset for the metrics presented in the following analysis. The annual cycle of both datasets is shown in Figure 1(a).

3 50 D. M. KENT et al. Monthly MSLP data from 23 of the GCMs submitted to CMIP3 are used to calculate monthly and annual time series representing the two characteristics of the STR. Data for the climate of the 20th Century experiment is used along with a combination of SRESA2 and SRESA1B 21st Century runs (Nakicenovic et al., 2000). The former represents a simulation forced by observed 20th Century greenhouse gas concentrations and radiation while the two SRES simulations are forced by enhanced greenhouse gas concentrations. Before the STR calculation is made, all data is interpolated to a regular 1 1 grid using a bi-linear interpolation. Before settling on the use of interpolated 1 resolution data, a comparison was made between the STR time series calculated using each model s native grid and the interpolated grid. The results are not shown here but the differences were negligible with only a slight change in the mean annual intensity and position for some of the models, therefore, the 1 grid was used. The selection of the longitude band used to calculate the indices is discussed in Section 3.1. Monthly precipitation data for each of the 23 models was also used along with the BoM s gridded (0.25 ) highquality rainfall observations (Jones and Beard, 1998). The mean Euclidean distance between a model s STR cycle and the observed STR is used as a metric throughout this study to compare the annual cycle of both the STR-I and -P to their observed cycle as a single value. The values presented are the mean of the distance (d) between the data to be assessed and the observed data for each month. The monthly value of each component (the STR intensity and position) is standardized before the distance calculation in order to allow comparison of values with very different units. Mathematically: d = i=1 (P o,i P i ) 2 σ P,o,i + (I o,i I i ) 2 σ I,o,i (1) Where P is the STR-P, I the STR-I, a subscript o represents the observed value and o the standard deviation. This results in a distance index where values closer to zero indicate a better fit to the observed cycle. 3. Results 3.1. Evaluation Four tests are applied to the CMIP3 results in order to answer the questions posed above. The first test assesses the ability of the models to produce a realistic STR in the Australian region and determines the longitudinal breadth and location of the region over which the STR is calculated. The second and third tests examine the similarity of any identified STR to the observed STR, using the mean annual cycle and the interannual variability as metrics. The fourth test assesses the model s STR-I relationship with rainfall Test 1: STR calculation location and longitudinal breadth Many of the models represent the historical mean annual cycle (calculated over the period ) of the STR, however, the similarity of the model s STR to the observations is heavily dependent on the longitude band used. Figure 2 shows the annual cycle of the STR for the CSIRO-Mk3.5 model (black) diagnosed using a series of different longitude bands along with the cycles derived from the BoM data (grey). The bands become narrower down the page and move further east from left to right. It can be seen that the cycle is best represented in the bands around E. At least three bands appear to perform equally well ( E, E and E); however, in order to objectively assess each band, the Euclidean distance from the BoM cycle is calculated. The Euclidean distance metric separates the three giving a better (i.e. lower) score to the E band (0.55 compared to 0.43 and 0.49 for the other two). Figure 3. The figure shows that most of the models use a longitude band very Figure 2. The CSIRO-Mk3.5 STR annual cycle, STR-I (x-axis) and STR-P (y-axis), for a range of longitude bands (black, range in degrees east shown above each panel). The observed cycle for the BoM (grey) dataset is also shown.

4 THE AUSTRALIAN SUB-TROPICAL RIDGE IN THE CMIP3 MODELS 51 Table I. Summary of test results. F indicates that the model fails a test. Models that pass test 1 exhibit a good annual-cycle over a band that touches 140 E and is narrower than 40 in longitude. Test 2 examines the Euclidean distance from the observed annual cycle (pass if <0.6, the distance of the NCEP cycle from the BoM cycle). Test 3 (STR-I left, STR-P right) fails if a models inter-annual variability does not resemble the observed, models must fail both STR-I and -P to fail test 3 overall. Test 4 assesses the spatial correlation of the modelled and observed STR intensity/rainfall correlation (pass if r>0.3, significant at = 0.9). The models that pass all four tests are highlighted in bold. Model Test 1 Test 2 Test 3 Test 4 I P Figure 3. The longitude bands where the best STR is found for each model as per the lowest Euclidean distance. CSIRO-Mk3.5 (shaded) is shown in Figure 2. similar to the band used for the observations. A number of models achieve their optimal STR representation over a broader band in the same region (INM-CM-3.0, GISS- ER, CSIRO-Mk3.0, BCCR-BCM2.0) while a number of other models use bands much further west (e.g. both CGCM models). Five of the models use bands that are either far too broad or far too westerly to really be considered representative of an Australian STR. These are CGCM3.1(T47), CGCM3.1(T63), FGOALSg1.0, GISS-EH and IPSL-CM4; these models are listed as failed in Table I. The best STR band for each of them does not touch 140 E and is greater than 40 wide resulting in a ridge that is not centred over Australia and is unlikely to have the same impact on Australian weather as the observed pressure maxima. In order to avoid the imposition of an arbitrary choice of longitude band on the results, the best band for each model is used for all subsequent analyses Test 2: STR annual cycle Figure 4 shows the annual cycle of the STR derived using the best longitude band for each model while the upper left panel shows the multi-model mean. Each panel shows the annual cycle of the BoM STR (grey) as well as one of the models (black). The multi-model mean closely resembles the observational dataset, with a mean Euclidean distance of only 0.32 from the BoM data and 0.58 from the NCEP data (compared to a range of for the individual models and a difference of 0.6 between the two observational sets). Such a good multi-model result has also been observed for other atmospheric-oceanic drivers such as ENSO (Cai et al., 2009). However, there are significant differences between many of the models. Once again, to objectively assesses each model s performance a simple pass or fail test is used. In this case, all of CGCM3.1(T47), CGCM3.1(T63), CNRM- CM3, ECHO-G, GFDL-CM2.0, GISS-AOM, GISS-EH, BCCR-BCM F 0.50 F CGCM3.1(T47) F 0.82 F F 0.86 F CGCM3.1(T63) F 0.87 F F 0.06 F CNRM-CM F F F 0.50 CSIRO-Mk CSIRO-Mk ECHAM5 MPI-OM F ECHO-G 0.66 F 0.63 FGOALS-g1.0 F 0.54 F 0.81 GFDL-CM F F 0.15 F GFDL-CM F 0.77 F GISS-AOM 0.62 F F 0.90 GISS-EH F 1.66 F F F 0.83 F GISS-ER 0.68 F 0.82 INM-CM F 0.24 F IPSL-CM4 F 0.99 F F 0.94 MIROC3.2(hires) 0.55 F 0.94 MIROC3.2(medres) 0.47 F 0.97 MRI-CGCM F 0.88 CCSM PCM 0.52 F 0.79 UKMO-HadCM F F F 0.82 UKMO-HadGEM F 0.82 GISS-ER, IPSL-CM4 and UKMO-HadCM3 (all with a bold border in Figure 4) are deemed to have failed the test as each of them produces a mean Euclidean distance from the BoM observations greater than the difference between the two observational sets (0.6). The second column of Table I summarizes this result. A number of the remaining models do simulate the cycle well, particularly CCSM3, CSIRO-Mk3.0 and INM-CM3.0 which all have Euclidean distances from the observations of less than 0.4 (Table I). Figure 5 shows the Euclidean distance from the BoM observations for each model s mean (black) as well as its standard deviation (grey). The representation of the annual cycle of the standard deviation is much poorer compared to the mean, and it is interesting to note that the models that achieve a smaller Euclidean distance for the mean do not necessarily perform well for the representation of their variability. This leads to an evaluation of the inter-annual variability of the two STR characteristics in the CMIP3 models.

5 52 D. M. KENT et al. Figure 4. The annual cycle of STR-I (x-axis) and STR-P (y-axis) for the multi-model mean (black, top-left) and for each model (black). The BoM cycle is shown in grey in all plots. The figures outlined in bold pass the Euclidean distance test (test 2) Test 3: STR inter-annual variability In addition to the seasonal cycle of the mean STR-I and - P, the inter-annual variability of both has implications for natural climate variability in southeast Australia. Figure 6 shows box plots of the annual anomaly values for STR-I (a) and STR-P (b) over the period (all data detrended). The box indicates the width of the interquartile range, the line inside the box is the median value, and the extents of the whiskers the extreme data points (within 1.5 times the inter-quartile range). The edge of the shaded areas represent variability values ±1/3 the difference between the two observed datasets at both the 25th and 75th percentiles (the box-ends). The two dashed lines depict variability ±2/3 the difference between the observed values for both the extreme points. Once again, a simple objective test is utilized to identify models that do not realistically replicate the inter-annual variability expected of the STR. A model passes this particular test if both ends of its inter-quartile range fall within the bounds of the observed ranges and the extremes of the distribution fall within the observed extremes. A tolerance of 30% is allowed for the inter-quartile values and 60% for the extremes. The models that fails each test are listed in Table I. Almost all models appear to slightly underestimate the inter-annual variability of the STR-I (bottom) with only CNRM-CM3, GISS-EH and the two UKMO models not passing the test. However, the STR-P distributions vary more across the multi-model dataset. CSIRO-Mk3.0, CSIRO-Mk3.5, ECHAM5/MPI- OM, ECHO-G, GISS-ER, and CCSM3 and UKMO- HadGEM1 all have inter-annual variability similar to the observed datasets (both box ends fall within the shaded areas and both whiskers fall within the dotted lines) while most of the remaining models underestimate the STR-P s variability. As expected, the variability of the multi-model signal is nearly completely damped out Test 4: Relationship of STR-I with rainfall An understanding of model performance in the representation of key hydro-climatic drivers is important for improving hydro-climate projections. In this context, the relationship or teleconnection between each model s simulated STR-I and precipitation is examined. The relationship with STR-I is the focus here as its trends are suggested to be closely related to the observed trends in Figure 5. The Euclidean distance from the BoM observed STR annual cycle of the mean climatology (black bars) and standard deviation (grey bars) for each model. The mean values are summarized in Table I, column 2.

6 THE AUSTRALIAN SUB-TROPICAL RIDGE IN THE CMIP3 MODELS 53 column of Table I shows the linear spatial correlations of each model s STR-I/rainfall teleconnection compared to the observed pattern. This is calculated as the Pearson correlation coefficient of the all spatial points in the data presented in Figure 7. Once again, a simple pass/fail test is applied to the results. Only models with a significant (α >0.9) correlation of the right sign are deemed to have passed this test. All the models either achieve high positive values or give very weak positive and negative values. Figure 6. Boxplots depicting the distribution of annual STR-I (a) and STR-P (b) values for both observational datasets, the multi-model mean and each model. The boxes represent the inter-quartile range of the detrended annual anomalies. The median is shown as a line through the box and the whiskers represent the most extreme values limited to 1.5 times the inter-quartile range. The shaded areas depict the bounds of the observed 25th and 75th percentile values and the dashed lines the observed bounds of the whiskers. rainfall (Larsen and Nicholls, 2009; Timbal and Drosdowsky, submitted; Timbal et al., 2010; Whan et al., (submitted)). Figure 7 shows the spatial distribution of the linear correlation between annual STR-I and annual precipitation for each model and for the two observational sets. The simulated precipitation fields are used for each model, and the BoM s high-quality gridded precipitation dataset is used for the observational analysis. There are no large differences between the two observed rainfall patterns apart from NCEP showing a slightly stronger correlation than the BoM data in the northwest of Victoria. As expected, the observations show a strong correlation between increased southeastern Australian rainfall and a weaker STR-I. This relationship is captured by some of the models but most models greatly underestimate its strength or do not represent the relationship at all. None of BCCR-BCM2.0, CGCM3.1(T47), CGCM3.1(T63), ECHAM5/MPI-OM, GFDL-CM2.0, GFDL-CM2.1, GISS-EH and INM- CM3.0 exhibit the correct spatial pattern, while only IPSL-CM4, MIROC3.2(hires) and MIROC3.2(medres) show anything like the right strength in the correlation. There is a very clear distinction between the models that capture the spatial pattern and those that do not. The third Summary of test Table I summarizes all four tests outlined above. One model, GISS-EH, fails all four tests while CGCM3.1 (T47) and CGCM3.1(T63) only pass half the inter-annual variability test (test 3). Four more models are identified as poorer performers, failing at least two tests (CNRM-CM3, GFDL-CM2.0, IPSL-CM4 and UKMO- HasCM3). There appears to be no correlation between the models that reproduce the annual STR cycle and those that reproduce the relationship with annual rainfall. For example UKMO-HadGEM1 and IPSL-CM4 both have a very different annual cycle to the observations but both show a very similar pattern of correlations with rainfall. CSIRO-Mk3.0, CSIRO-Mk3.5, MIROC3.2(hires), MIROC3.2(medres), MRI-CGCM2.3.2, CCSM3, PCM and UKMO-HadGEM1 pass all four tests (note that to fail test 3 a model must fail on both characteristics). Of these, CSIRO-Mk3.0, CSIRO-Mk3.5 and CCSM3 stand out as they completely pass both components of test Projections for the STR To further examine the changes in STR-I and -P, the change in both variables per degree of global mean surface air temperature increase is calculated for each model. This involves calculating the linear regression of each parameter against the annual global mean temperature for each model (specific to the scenario used). This process gives a more objective indication of the scale of change predicted by a model without most of the model temperature biases and without reference to any particular forcing scenario. All models give an increase in intensity with a median value of 0.21 hpa per degree of warming and all but one of the models project a more southerly position (median value of 0.25 latitude per degree of warming). This method is, of course, limited by the assumption that changes in the STR are linearly related to changes in global mean surface temperature, which is considered further in Section 4. Figure 8 demonstrates the effect of applying the results of the four tests developed in Section 3.1 to the multi-model mean trend in both STR intensity and position. The multi-model mean is calculated for different ensembles of models, each determined by those models that pass or fail a combination of the four tests. It shows that there is very little change from the multi-model mean of all 23 models, of either metric, when the ensemble is selected depending on the evaluation of the models, i.e. eliminating the models that were

7 54 D. M. KENT et al. Figure 7. Linear correlation of annual STR intensity and annual rainfall. Values outside of 0.3 are significant at the LGR = 90% level. The plots with a bold border indicate that the model fails test 4 (Table I for a summary). This figure is available in colour online at wileyonlinelibrary.com/journal/joc Figure 8. The multi-model mean trend per degree warming for selected groups of of models. They are (from top to bottom), all 23 models, all models that pass test 1, all models that pass test 2, all models that pass test 3, all models that pass test 4, all models that pass tests 2 and 4, all models that pass test 2 or test 4, the set of models that pass any three of the four tests and the set of models that pass all four tests. Note that changes in position are not shown for any combination including test 4 as this is only relevant to intensity changes. deemed not to perform well on the four tests does not change the mean result. This suggests that the projection is not altered by excluding the poorer models. Figure 9 shows the projected mean trend in rainfall (percent change per degree of warming relative to a 30- year period centred on 1990 over the 21st Century) for each model for the southeast Australian region (south of the line marked in Figure 1, as derived in Timbal et al., 2010) against the projected change in both STR intensity (a) and position (b). If the models were able to accurately reflect the historical relationship between the STR and rainfall in southeast Australia a strong relationship

8 THE AUSTRALIAN SUB-TROPICAL RIDGE IN THE CMIP3 MODELS 55 Figure 9. Projected percentage change per degree global warming in rainfall against the change in STR intensity (a) and position (b). All changes calculated over the entire 21st Century relative to a 30-year base-period centred on between these two metrics would be expected. This is not the case. There is no apparent relationship between rainfall responses to global temperature and the response of either of the STR indices. This suggests that the models either never represented this relationship well or that they project a future under which rainfall and the STR do not respond to global temperature changes in the same manner. Even eliminating those models that fail any of the four tests or using the autumn or winter rainfall trends (when the STR influence is strongest) does not improve the relationship. An interesting point to note is that the relationship between projected changes in STR-P and in rainfall is much stronger than that between STR-I and rainfall. Studies of the historical STR-I and -P suggest that, using linear statistics,... in none of the autumn months is latitude an important factor contributing to the observed decline in rainfall... (Larsen and Nicholls, 2009, p.5). However, Whan et al. (submitted) find that, using nonlinear statistics, the relationship between rainfall, an intensification of the STR and the contribution of a southward shift in its location is more complex. With this in mind, if the models are now projecting a stronger relationship between STR-P and rainfall changes in future climate then this difference must suggest one of two things: a change in the mechanisms driving southeast Australian rainfall or, that the models do not adequately represent such existing mechanisms. Given the evaluation presented in Section 3.1 the second option may well be likely. Given the earlier results that the multi-model mean gives a good representation of the latter 20th Century annual cycle of the STR, and that the mean position and intensity are better represented than their variability and trends, it is worth examining the multi-model 21st Figure 10. Change in annual cycle of multi-model mean intensity and pressure. Century representation. Figure 10 shows the annual cycle of the multi-model mean STR position (vertical axis) and intensity (horizontal axis) for two 20th Century time periods (centred on the 1960s and the 1980s) and three 21st Century periods (centred on the 2030s, 2060s and 2080s). The trend toward a more southerly and more intense STR is clearly seen and appears quite consistent across all months. It is interesting to note that the autumn months (historically responsible for much of southeastern Australia s rainfall (CSIRO, 2010; Timbal et al. (2010)) show far less change in intensity than the remainder of the year they only appear to exhibit a southerly change in STR position. In contrast, winter and early spring show strong changes in intensity and position. If the multimodel mean annual cycle is believed to be robust then

9 56 D. M. KENT et al. this has implications for southeastern Australian rainfall and raises some interesting questions about the relative impact of STR position versus STR intensity. Such issues have been considered elsewhere (e.g. Drosdowsky, 2005; Larsen and Nicholls, 2009; Timbal et al., 2010). 4. Discussion and conclusions Previous studies of historical STR characteristics (particularly STR-P, e.g. Williams and Stone (2009); Larsen and Nicholls (2009)) have commented on the impact of the choice of observational dataset. Williams and Stone (2009, p.693) suggest that the choice of dataset is more important than the method used to calculate the index. In our case, the NCEP derived STR-P is up to one degree further south than the BoM dataset. It also exhibits a lower intensity for the entire year and shows a much quicker movement to the north at the onset of winter. These differences may be accounted for by differences in the calculation method used, as well as the longitudinal band defined for the calculation. The BoM dataset relies on station data and, as such, has a different longitudinal distribution to the NCEP data Drosdowsky (2005); Timbal and Drosdowsky (submitted). However, these differences also provide a reference point for the evaluation of the GCM STR characteristics. The first question this study attempted to address was whether the CMIP3 models produced a believable STR. Comparing metrics of the two STR characteristics, which are known to be sensitive to the dataset used, to two different observed datasets allows a clearer evaluation of whether a GCM s STR lies within the bounds of what the observed datasets diagnose as a realistic Australian STR. With reference to the difference between the annual cycles of the two observed STRs a number of the CMIP3 models represent the STR-I and -P annual cycles in a realistic manner. They have the correct range of position and intensity at the right time of year. Following this approach it is already easy to identify a number of models that do not, in any meaningful way, exhibit a ridge of high pressure over the Australian region with a seasonal influence that the STR has (regardless of the observational dataset used). The argument then follows that care should be taken using these models in studies related to southeast Australian rainfall. It is interesting to note that the multi-model mean annual cycle compares very well with the observations, while the variability of the multi-model mean is very low. The process of averaging the ensemble of signals together completely damps out the inter-annual variability. This must be taken into account if using the multi-model result in any kind of impact assessment. About 60% of the CMIP3 models capture some of the inter-annual relationship between the STR-I and annual precipitation across southeastern Australia. Quite a number of the models do not capture this relationship and there seems to be little correlation between those that represent the annual cycle and those that have an appropriate rainfall and STR relationship. Larsen and Nicholls (2009) and Timbal and Drosdowsky (submitted) identified strong seasonality of the relationship between the STR-I and southeast Australian precipitation, with the intensity having much greater influence over rainfall during the winter months. Such seasonality is not examined here. However, given the generally poor replication of the annual STR-I/rainfall relationship it would be surprising if the seasonal characteristics were realistic. In addition, both Drosdowsky (2005) and Timbal and Drosdowsky (submitted) outline the non-stationarity of the relationship both STR-I and -P have with rainfall. This suggests that the GCM results could be different if a slightly different time period was considered. This possibility was not investigated here. There is also an issue in that some of the models (IPSL-CM4 and FGOALS-g1.0 in particular) that have high spatial correlation results fail the first test on their annual cycle. This suggests that there is a strong relationship with rainfall and a ridge of high pressure in these models, but that the characteristics of that highpressure ridge are not those of an Australian STR. Given that the annual cycle of the STR has significant implications for rainfall in southeastern Australia, the inability of the models to reproduce the correlation between the STR and precipitation is of some concern and leads to questions about what processes are driving rainfall in the models. It is assumed that if a model is capable of representing the historical relationship between rainfall and the STR then some conclusions can be made about future rainfall using projections of MSLP and the STR s position and intensity. In the previous section, we considered mean trends in both the STR-I and STR-P with respect to modelled changes in global temperature. It was in these values that the broadest agreement was found across the models. All project a more intense, further south STR. There was little to no pattern in the strength of these changes relative to the assessment made of model s performance. One of the potential issues with this type of analysis (the pattern scaling approach of Mitchell (2003) and used extensively in CSIRO and Australian Bureau of Meteorology (2007)) is the assumption of a linear relationship between the STR indices and global mean temperature. A detailed treatment of this topic is beyond the scope of this study, but in this case, the longterm annual temperature and STR indices do correlate well. Timbal et al. (2010) found that for the CCSM3 model the... position of the STR is unrelated to the global temperature..., however, that result was sensitive to the length of time used to examine the relationship. Indeed, they find that on a centennial time-scale (such as the values presented here) the relationships are much stronger. The multi-model values of 0.21 hpa increase in annual STR-I per degree of warming, and a 0.25 S change in STR-P are weaker than those derived from a single model by Timbal et al. (2010) but the range of model results does agree. The historical relationship between the STR and rainfall suggests that the projections of a more intense, further

10 THE AUSTRALIAN SUB-TROPICAL RIDGE IN THE CMIP3 MODELS 57 south STR would result in a decrease in rainfall in southeastern Australia. This is consistent with previous studies (CSIRO and Australian Bureau of Meteorology, 2007) and the model precipitation fields which suggest that rainfall reductions are likely. Acknowledgements This study is supported by the South-Eastern Australia Climate Initiative (SEACI). We extend our thanks to Tim Cowan, Sarah Perkins and the two anonymous reviewers for their constructive comments which lead to significant improvements to the manuscript. We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the WCRP s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. References Cai W, Sullivan A, Cowan T Rainfall teleconnections with Indo-Pacific variability in the WCRP CMIP3 models. Journal of Climate 22(19): , DOI: /2009JCLI CSIRO Climate variability and change in south eastern Australia A synthesis of findings from Phase 1 of the South Eastern Australian Climate Initiative (SEACI). Technical Report, CSIRO. CSIRO, and Australian Bureau of Meteorology Climate Change in Australia. Technical Report, CSIRO. Das S Statistical analysis of Australian pressure data. Australian Journal of Physics 9: Drosdowsky W The latitude of the subtropical ridge over eastern Australia: The L index revisited. International Journal of Climatology 25: , DOI: /joc Frederiksen JS, Frederiksen CS, Osbrough SL Modelling of changes in Southern Hemisphere weather systems during the 20th century. In 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, Anderssen R, Braddock R, Newham L (eds). Cairns, Australia Jones DA, Beard G Verification of Australian monthly district rainfall totals using high resolution gridded analyses. Australian Meteorological Magazine 47: Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77: Kirono DGC, Kent DM Assessment of rainfall and potential evaporation from global climate models and its implications for Australian regional drought projection International Journal of Climatology DOI: /joc Larsen SH, Nicholls N Southern Australian rainfall and the subtropical ridge: Variations, interrelationships, and trends. Geophysical Research Letters 36: L08,708, /2009GL Mitchell TD Pattern scaling: An examination of the accuracy of the technique for describing climates. Climatic Change 60: , DOI: /A: Nakicenovic NJ, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, Rovere ELL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger PSM, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z IPCC Special Report on Emissions Scenarios. Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA. Pittock A Rainfall and the general circulation. In Proceedings of the International Conference on Weather Modification, Canberra. Pittock A Global meridional interactions in strosphere and troposphere. Quarterly Journal of the Royal Meteorological Society 99: Suppiah R, Hennessy K, Whetton P, McInnes K, Macadam I, Bathols J, Ricketts J, Page C Australian climate change projections derived from simulations performed for the IPCC 4th Assessment Report. Australian Meteorological Magazine 56: Timbal B,Drosdowsky W. The relationship between the decline of South Eastern Australian rainfall and the strengthening of the subtropical ridge. International Journal of Climatology (submitted). Timbal B, Arblaster J, Braganza K, Fernandez E, Hendon H, Murphy B, Raupach M, Rakich C, Smith I, Whan K, Wheeler M Understanding the anthropogenic nature of the observed rainfall decline across South Eastern Australia. Technical Report. CAWCR Research report. Van Oldenborgh GJ, Philip YS, Collins M El-Nino in a changing climate: a multi-model study. Ocean Science 1: Whan K, Timbal B, Lindesay J. Linear and non-linear statistical analysis of the impact of sub-tropical ridge intensity and position on south-east Australian rainfall. International Journal of Climatology (submitted). Williams AAJ, Stone RC An assessment of relationships between the Australian subtropical ridge, rainfall variability, and high-latitude circulation patterns. International Journal of Climatology 29: , DOI: /joc Wright WJ The low latitude influence on winter rainfall in Victoria, south-eastern Australia I. climatological aspects. Journal of Climatology 8: Wright WJ A synoptic climatological classification of winter precipitation in Victoria. Australian Meteorological Magazine 37: