AN ASSESSMENT OF SIMULATIONS OF CLIMATE VARIABILITY OVER AUSTRALIA WITH A LIMITED AREA MODEL

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, (1997) AN ASSESSMENT OF SIMULATIONS OF CLIMATE VARIABILITY OVER AUSTRALIA WITH A LIMITED AREA MODEL KEVIN WALSH AND JOHN MCGREGOR CSIRO Division of Atmospheric Research, Aspendale, Australia kevin.walsh@dar.csiro.au Received 28 November 1995 Revised 20 March 1996 Accepted 28 May 1996 ABSTRACT Simulations with a general circulation model (GCM) and a limited area model (LAM) are presented for two contrasting years of observed rainfall over Australia, 1982 and The LAM is forced at its lateral boundaries by the GCM simulation. Both models were forced with the observed sea-surface temperatures of those years, and five realizations of each model were performed in order to reduce the effects of internal variability. The resulting simulations are compared with observed rainfall for both years. Both models simulate differences in the mean rainfall over Australia between the two years that are similar to observed differences. Although the LAM has larger positive inherent biases than the GCM, it has smaller r.m.s. errors. The LAM simulated a much better pattern of rainfall than did the GCM, and has a greater ability to simulate observed regional anomalies of precipitation. Nevertheless, neither model adequately simulates the observed detailed pattern of rainfall anomalies over the continent for the respective years. The climatology of the LAM needs to be improved before it can be used to make inferences about regional interannual variability in a changed climate. KEY WORDS: Australia; climate modelling; limited area modelling; precipitation; CSIRO Climate Change Research Program. 1. INTRODUCTION Several climate simulations have been performed using limited area models (LAMs), in which a primitive equations model is implemented over a restricted area of the globe and forced at the boundaries by lowerresolution fields using, either analyses or general circulation model (GCM) output. The work of Giorgi and his collaborators is perhaps best known in this respect (Giorgi et al., 1993, 1994). In their work, versions of the NCAR MM4 mesoscale model (Anthes et al., 1987) have been implemented over various regions of the globe and the climatology of the models assessed. The technique has also been used to simulate the climate of Australia and the surrounding regions (McGregor and Walsh, 1993, 1994; Walsh and McGregor, 1995) using the CSIRO DARLAM model (McGregor, 1987). It was demonstrated that the LAM produced a generally superior simulation (particularly of rainfall) over the continent of Australia compared with the global model within which it was nested. Although considerable work has been performed analysing the ability of various LAMs to simulate the mean climate, little work has been performed to assess their ability to simulate the observed interannual variability of climate. For global models, the work of the Atmospheric Model Intercomparison Project (AMIP; Gates, 1992) provides a method of assessing the quality of the simulation of these models when forced by observed sea-surface temperatures. A large number of GCMs have been assessed in this manner. The ability of an earlier four-level GCM (CSIRO4) to simulate observed drought conditions was evaluated by Hunt and Gordon (1991). They showed that the 1988 drought over the USA could be simulated by the GCM when forced with observed seasurface temperatures (SSTs) for 1987 and The performance of CSIRO4 has also been assessed by Smith (1995), running the model with observed sea-surface temperatures (SSTs) for the period It was concluded that although the GCM simulated many features of the observations, there were also important differences that might limit the ability of the GCM to predict the climate even if forced by an accurate forecast of CCC /97/ $17.50 # 1997 by the Royal Meteorological Society

2 202 K. WALSH AND J. McGREGOR SST. Specifically, there are a number of differences between the simulated rainfall patterns and known ENSOrelated rainfall anomaly patterns. The response of the nine-level CSIRO9 GCM (McGregor et al., 1993; Watterson et al., 1995) to forcing by SSTs over the period has been examined by Dix and Hunt (1995) as part of the AMIP experiment. They performed three separate simulations with different atmospheric initial conditions to study the impact of deterministic chaos on the resulting simulated climate. They found a coherent response between the three simulations over the tropical oceans, but much less agreement at higher latitudes and over land. The simulated precipitation over Australia in this experiment was examined by Rocha (1993). He found that although the GCM exhibited considerable skill in the simulation of rainfall variability in eastern regions of the continent, its skill was poor in the western half. In addition, because the modelled rainfall was spread over the continent instead of being concentrated along the coast as in the observations, he placed little confidence in the ability of the model to simulate the detailed spatial variability of rainfall patterns. He attributed this result to the specification of orography in the model, which at a resolution of R21 is poorly resolved. Thus the use of a higher-resolution limited area model, which has already been shown to provide an improved simulation when nested within CSIRO9 and forced with climatological SSTs, suggests itself as a way of improving the simulation of interannual variability. In this paper, the results of DARLAM simulations nested within the CSIRO9 GCM forced with observed SSTs are shown. Simulations are performed with the observed SSTs of two different years: 1982 and These years are chosen because they represent two contrasting aspects of rainfall conditions over Australia; 1982 was predominantly dry, and was a strong El Niño year (Nicholls, 1985) characterized by a large negative anomaly of the Southern Oscillation (SO) (Troup, 1965; Rasmussen and Carpenter, 1982). On the other hand, 1988 experienced above-normal rainfall over much of the continent (Drosdowsky, pers. comm., 1993), and was a La Niña year, with a positive SO anomaly (Kousky, 1989). There are good reasons to assume that a large portion of the interannual variability of precipitation over Australia is forced by changes in SSTs. There are well-known relationships between SST anomalies in the regions surrounding Australia and observed rainfall over the continent itself; a recent compilation of such teleconnections is given by Allan (1991). There are strong links between SSTs off the coast of north-western Australia and rainfall in south-eastern Australia (Nicholls, 1989; Whetton, 1990). Streten (1981) found a clear relationship between SST anomalies off the north-east coast and total Australian rainfall. Nicholls (1989) documented a strong relation between SST anomalies in the central equatorial Pacific and winter rainfall in northeast and eastern Australia. Lough (1992) discovered some correlations between both summer and winter rainfall in north-eastern Australia and SSTs in that region. A more sceptical conclusion was reached by Cordery and Opuku-Ankomah (1994), who found that some of these correlations lacked stability over long periods of time. Correlations have been robust in the last years, however (Allan, 1993). Observed relationships between SSTs and Australian rainfall were tested in the GCM experiment of Simmonds and Rocha (1991), in which a warm SST anomaly north-west of Australia caused significant precipitation increases in a GCM over more than half of the continent. In the current experiments, because the GCM is forced with global observed SSTs it also includes the effects of the dynamical changes associated with the SO. The GCM s simulation of the SO is reasonable (Dix and Hunt, 1995); thus observed contemporaneous correlations of Australian rainfall with the SO (e.g. McBride and Nicholls, 1983) should also be simulated (at least in part) by the LAM nested within the GCM. Section 2 of this paper briefly describes the model and the methodology of this experiment. In section 3, the results are discussed, and section 4 gives some conclusions from this study. 2. MODEL AND METHODOLOGY DARLAM is a two-time-level, semi-implicit, hydrostatic primitive equations model; it uses an Arakawa staggered C-grid, and semi-lagrangian horizontal advection with bi-cubic spatial interpolation. In the present experiments, the model uses one-way nesting with lateral boundary conditions specified from a version of the CSIRO9 GCM (Mark I) which has been developed from the flux-conserving GCM of Gordon (1981). DARLAM uses a Lambert conformal projection, and in the present experiments has the same vertical level assignments as the GCM. The resolution used for these simulations was 125 km, and the model domain (as shown in Figure 1)

3 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 203 Figure 1. Model domain for DARLAM simulations. DARLAM grid-points are small dots GCM Gaussian grid-points are crosses. was the same as that of Walsh and McGregor (1995), in which the surface climatology of DARLAM is discussed and compared with that of the CSIRO9 GCM. The physical parameterizations of the CSIRO9 GCM include a modified version of the Arakawa (1972) cumulus convection scheme, the Deardorff (1977) soil moisture scheme, and the diurnally-varying GFDL parameterization for longwave and shortwave radiation (Fels and Schwarzkopf, 1975; Schwarzkopf and Fels, 1991). The model also includes a stability-dependent boundary layer, based on Monin Obukhov similarity theory (Louis, 1979). Soil temperatures were calculated using a three-layer model with a zero flux condition at the bottom. A diagnostic cloud scheme is also included, as well as a parameterization of gravity wave drag (Chouinard et al., 1986). In these simulations DARLAM incorporates similar physical parameterizations but uses a modified Kuo (1974) convection scheme and does not include gravity wave drag. Sensitivity experiments suggest that the DARLAM results were not greatly affected by this difference in convective parameterization. The models use the same method for determining surface soil moisture, denoted the a-method by Kondo et al. (1990). At each time step, the outermost boundary rows of DARLAM are relaxed towards the interpolated values provided every 6 h by the GCM, using a modified Davies (1976) scheme with exponentially decreasing weights as proposed by Giorgi et al. (1994). The contribution from the forcing boundary conditions at each time step falls below 5 per cent by five grid-points inside the boundary. In the case of surface pressure and temperature, the boundary fields are altered to compensate for any differences in height of the interpolated orography of the GCM compared with DARLAM. Sea-surface temperatures were taken from the standard SST data set used for the AMIP experiments. In the GCM experiments mentioned previously, multiple simulations were performed starting with differing initial conditions (taken from the same calendar month of different years of a long model simulation), and the

4 204 K. WALSH AND J. McGREGOR model response was taken to be resulting averages of the simulations. This procedure has been found necessary because of the sensitivity of lengthy model runs to initial conditions. Accordingly, five simulations of the GCM were performed with differing initial conditions and observed SSTs for each of the periods 1 July 1981 to 31 December 1982, and 1 July 1987 to 31 December Only of the years 1982 and 1988 were analysed, as the previous 6-month period was used as an equilibration period. DARLAM simulations were then performed nested within each of these GCM simulations, and the averages of the simulations taken to be the model climatology. In order to test quantitatively the ability of the models to simulate the observed anomaly patterns of precipitation, several statistical measures were used. Mean biases or anomalies were calculated over various regions of the continent for both models, to assess whether there were any systematic differences in the amount of simulated precipitation, both compared with the observed precipitation of Drosdowsky (pers. comm. 1993) for that month, and to the long-term climatology of Suppiah (pers. comm. 1992). In this way, the ability of the models to simulate observed rainfall deficits and surpluses was determined. The Drosdowsky data set comprises more than 6000 stations, although the geographical coverage is very variable, with many stations near the major cities, and few in the desert regions of inland Australia. Station coverage is adequate in the mountainous regions of south-east Australia, but no elevation-dependent interpolation from stations to grid-points was used. Pattern correlations were calculated between the monthly averages of simulated and observed precipitation fields in order to determine how well the models simulated the large-scale patterns of precipitation. The pattern correlation r p of two spatial fields is simply the correlation of a series of data points x i from one field with corresponding values y i from the other field: r p ˆ P xi ÿ x y i ÿ y q q P xi ÿ x 2 P yi ÿ y 2 1 where x and y are the means of the x and y fields respectively. In addition, anomaly correlation fields were calculated, in which the anomalies of both observed and simulated fields versus a long-term climatology were calculated for each month, and the resulting fields correlated. In this way, the ability of the models to simulate the detailed observed geographical variations from the long-term climatology was determined. The anomaly correlation r a is similar to the pattern correlation except that fields are replaced by anomalies from long-term climatology x 0 i and y 0 i r a ˆ P x 0 i ÿ x 0 y 0 i ÿ y0 q P pp x 0 i ÿ x 0 2 y 0 i ÿ y0 2 2 The output fields of both models were interpolated to the 1 by 1 grid of the Drosdowsky observations before the statistics were calculated. All statistics were calculated over only those grid squares where observations were present. 3. RESULTS 3.1. SST forcing and inherent climatological model biases The observed SST anomalies for January and July of 1982 are shown over the DARLAM model domain in Figure 2(a and b). Although the anomalies for January 1982 around Australia start out as largely positive (indicative of La Niña conditions, e.g. Allan (1988)), by July 1982 the anomalies had turned negative as the very strong El Niño observed in that year gained strength (e.g. Nicholls, 1985). The edge of the positive SST anomaly in the central equatorial Pacific associated with the El Niño event is seen in Figure 2(b) near the Equator in the eastern part of the domain. SST anomalies for January and July of 1988 (not shown) were largely positive around the continent. El Niño years are usually drought years in Australia, and 1982 was no exception (Nicholls, 1985). Figure 3(a) shows observed rainfall for 1982, and Figure 3(b) shows observed rainfall minus the long-term average. There was a large area of below-normal rainfall in the eastern half of the continent, whereas the western half experienced areas of both above- and below- normal rainfall. This is generally consistent with the gross features

5 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 205 Figure 2. SST anomaly patterns in K versus long-term climatology for (a) January 1982; and (b) July Isotherm interval is 1 K of precipitation variability expected in an El Niño year (Allan, 1988), in which negative SST anomalies near Australia are associated with lower summer rainfall in the north, lower winter rainfall in the south-east, and higher winter rainfall in the west of the continent. In contrast, 1988 (Figure 3(c and d)) was a year of largely above-normal rainfall in the south-east, and below-normal rainfall in the north. Although the pattern for 1988 is consistent in the south-east with the canonical La Niña conditions, there is less agreement in the north, where above-average rainfall should be expected. Nevertheless, the two years provide clearly contrasting cases to test the ability of the models to simulate the observed interannual variability.

6 206 K. WALSH AND J. McGREGOR Figure 3. (a) 1982 total observed precipitation, averaged to millimetres per month, from the data of Drosdowsky; (b) anomaly of this field versus the long-term climatology of Suppiah; (c) the total as (a) but for 1988; (d) the anomaly as (b) but for White squares are missing data Both models naturally exhibit precipitation biases even when forced with climatological SSTs. These biases are shown in Figure 4, in which the long-term observations for January are subtracted from a climatology composed of averages of 10 separate January simulations for each model. Ideally, we would prefer to present the annual mean long-term biases of the models, but these are at present unavailable. The results show that both the GCM and DARLAM overestimate observed rainfall throughout much of the continent, although the mean biases in Table 1 show that, on average, the GCM slightly underestimates rainfall over Australia, whereas DARLAM

7 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 207 Figure 4. Bias of model simulations for January versus the long-term climatology of Suppiah, in millimetres per month, for (a) GCM and (b) DARLAM overestimates it. The GCM has biases that tend to cancel each other out, whereas those of DARLAM are more consistent. The r.m.s. errors in Table I show this clearly, with January r.m.s. errors being substantially larger in the GCM than in DARLAM. In January, the GCM (Figure 4(a)) has largest positive biases in an eyeglass pattern over the east of Australia, whereas biases are largely negative over the far north and west. The influence of orography was found in Walsh and McGregor (1995) to be crucial, with DARLAM having a tendency to overestimate rainfall in regions of steep orography while at the same time simulating a better pattern of rainfall

8 208 K. WALSH AND J. McGREGOR Table I. Bias and r.m.s. error of GCM and DARLAM precipitation long-term climatologies over all Australia versus observations (millimetres per month) January July Bias R.m.s. error Bias R.m.s. error GCM ÿ DARLAM Table II. Mean annual anomalies for 1982 and 1988 of GCM, DARLAM and observations over all Australia with respect to long-term climatology (millimetres per month) GCM DARLAM Observed ÿ8.2 ÿ2.3 Table III. Mean difference between 1988 and 1982 rainfall over Australia (millimetres per month), for the model simulations and observations Difference Correlation with observations GCM 6.9 ÿ0.12 DARLAM Observed 7.4 than the GCM. In contrast, DARLAM s largest positive biases (Figure 4(b)) are over the eastern coastal region; this may be because DARLAM resolves the eastern highlands, whereas the GCM does not. Both models largely overestimate rainfall over the continent in July (not shown; but see Table I), and have comparable r.m.s. errors. Although the GCM exhibits regions along the east and south coasts where rainfall is underestimated, these are smaller in DARLAM, again because of the influence of orography. Largest positive biases occur in DARLAM in the far north of areas of substantial orography. Previous experiments showed that some differences in the two model simulations were caused by the higher resolution of DARLAM and the use of different convection schemes in the two models. Both of these effects were found to be considerably smaller than the differences caused by the specification of orography, however. Because of the influence of orography, it might be expected that the biases seen in the simulations with climatological average SSTs would also be present in simulations with observed SSTs for our selected years. Table II shows the mean annual anomalies between the GCM, DARLAM and the Drosdowsky observations and the long-term observed climatology, averaged over all of Australia. The observations show that 1982 was a year of below-average rainfall over the continent as a whole, whereas 1988 was a year of more average rainfall; despite the fact that much of Australia experienced above-average rainfall in this year, the high rainfall regions of the north did not, thus reducing the average over the entire continent. Both models overestimate rainfall, DARLAM particularly so; however, both models also simulate precipitation differences between the two years that are of the right order and sign (see also Table III). It would be preferable to present results of simulations for each year with the long-term annual mean climatologies for each model removed. In this way, a better estimate of the sensitivity of the models to the imposition of the SST anomalies characteristic of each year could be obtained. In the absence of these climatologies, another way to look at the results is to plot the differences between each model s simulation of the

9 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 209 two years, and compare the results to the observed differences in precipitation between the two years. This comparison removes the necessity for annual mean model climatologies. The results are show in Figure 5. The observations (Figure 5(a)) show that rainfall was higher in 1988 than in 1982 over the eastern half of the continent, and predominantly lower in the north-west portion. The GCM (Figure 5(b)) simulated generally higher rainfall everywhere over Australia in 1988, except for two regions in the south-east and far west. DARLAM (Figure 5(c)) simulated generally higher rainfall in the east and lower in the west and towards the north, although Figure 5. Difference in annual mean precipitation, 1988 minus 1982, for (a) observations; (b) GCM; and (c) DARLAM. Units are millimetres per month

10 210 K. WALSH AND J. McGREGOR like the GCM there was a region of lower rainfall in the south-east. The mean differences are compared in Table III, which shows that there is quite good agreement in both models regarding the observed sign and magnitude of the changes in precipitation between the years, as was also shown in Table II (slight differences between the results of Table III and similar differences that could be calculated from Table II are the result of differences between the two years in the locations of missing data). The anomaly correlations between the model differences and those observed shown in Table III are small, however. Although the models capture the general difference in rainfall observed between 1982 and 1988, the specific geographical detail of these differences is not well simulated at the fine scale of the observations (1 grid), although we show later in this paper that the simulation is better at larger, regional scales Significance of model response and regional anomalies In order to determine the statistical significance of the model response, the signal-to-noise ratio, R, is calculated from the model results and compared with observations, where R S ˆ 3 s S is the signal, in this case the difference in precipitation between 1988 and 1982 (either simulated or observed), and s is the standard deviation of such differences. For the observations, the standard deviation is calculated from observed differences from 1982 rainfall for the years (but not including 1982). For DARLAM, standard deviations are calculated from the five realizations that comprise this simulation. The statistical significance of the signal-to-noise ratio is evaluated using Student s t test. The results are shown in Figure 6. Shading correspond to regions statistically significant at the 95 per cent and 99 per cent levels (two-tailed), whereas dotted regions are not statistically significant. The observations (Figure 6(a)) show that precipitation differences between the two years are highly significant over most of the eastern coast and the south-east of the continent, with most signal-to-noise ratios significant at the 99 per cent level. In contrast, the DARLAM simulation (Figure 6(b)) shows only a few statistically significant regions of increased rainfall in the eastern half of the continent. Thus, although the simulated change in rainfall between 1988 and 1982 is mostly of the correct sign in this region, it is much less statistically significant than that seen in the observations. There are two possible causes for this result. The first is that an insufficient number of realizations have been run to provide enough statistics for reliable results. The second is that the GCM and=or DARLAM are unable to simulate the observed precipitation changes when forced solely by SSTs. Although both models have biases, they also both have some skill in simulating the observed precipitation pattern when forced with climatological SSTs for 10 realizations (Walsh and McGregor, 1995). This suggests that the number of realizations presented in this paper should be increased in similar experiments performed in the future. A similar comparison was made for those months where long-term DARLAM and GCM climatologies are available (January and July) to see whether meaningful results could be obtained by subtracting these model climatologies from the simulations of the corresponding months of 1982 and 1988, and then comparing the resulting signal-to-noise ratios to the observed ratios in these months. Unfortunately it was found that the model fields of these individual months were noisy. In addition to the interannual variability between realizations, there is also variability between simulated months, which makes the comparison of the signal-to-noise for individual months less meaningful than that for entire years. Rather than displaying maps of signal-to-noise ratios, the best way of showing the ability of the models to simulate observed anomalies for individual months is to calculate regional anomalies of precipitation for these months (in other words, model simulations minus the long-term model climatologies). These regional anomalies are shown in Tables IV and V. The averages over all of Australia (Table IV) show that DARLAM may be picking up the interannual variations in the observations slightly better than the GCM, but this table does not show that either model has great skill in this respect. Table V shows the same anomalies broken down into several regions. For analysis purposes, we define here six regions (shown in Figure 7: Northwest (north of the tropic of Capricorn, and west of 130 E); North-central (north of the tropic, 130 E 140 E); North-east (north of the tropic, east of 140 E); South-west (south of the tropic, west of 130 E); South-central

11 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 211 Figure 6. Signal-to-noise ratio of differences between 1988 and 1982 precipitation, for (a) observations; and (b) DARLAM. Statistically significant areas are shaded (south of the tropic, 130 E 140 E); and South-east (south of the tropic, east of 140 E)). These regions were chosen largely on the basis of the observed differences in rainfall response to SST and SO anomalies, based upon some of the teleconnection studies referred to in the introduction (see also Drosdowsky, 1993). Of the 24 events (6 regions times 4 months) shown in Table V, DARLAM has the correct sign of the anomaly in 18 of them; if one assumes that the events are binomially distributed, this result has a probability of being random of less than 1 per cent. This probability analysis ignores the effect of spatial autocorrelation, however.

12 212 K. WALSH AND J. McGREGOR Table IV. Model simulations of precipitation over all Australia (in millimetres per month) with long-term model climatology removed compared with observed monthly anomalies (from long-term observed climatology) January July January July GCM ÿ ÿ DARLAM ÿ7.5 ÿ8.6 ÿ Observations 9.5 ÿ20.3 ÿ Table V. The same as Table IV but for various regions January July Year NW NC NE SW SC SE NW NC NE SW SC SE 1982 GCM ÿ15.7 ÿ ÿ2.6 ÿ ÿ10.2 ÿ ÿ DARLAM ÿ ÿ9.5 ÿ0.8 ÿ2.1 ÿ28.3 ÿ8.1 ÿ18.8 ÿ ÿ2.0 ÿ5.0 Observations ÿ ÿ13.4 ÿ7.0 ÿ11.0 ÿ7.0 ÿ14.3 ÿ21.4 ÿ18.7 ÿ GCM ÿ15.6 ÿ0.4 ÿ10.4 ÿ27.0 ÿ8.0 ÿ ÿ DARLAM ÿ12.3 ÿ53.8 ÿ55.2 ÿ10.4 ÿ23.3 ÿ ÿ4.3 ÿ3.7 ÿ1.2 ÿ5.1 ÿ1.2 Observations ÿ62.5 ÿ73.0 ÿ151.5 ÿ13.2 ÿ18.2 ÿ22.2 ÿ10.5 ÿ ÿ0.6 ÿ The GCM is less good at capturing regional variations than DARLAM, with only 14 events having the correct sign; this result has a probability of being random of about 10 per cent (also ignoring the effects of spatial autocorrelation). Table VI shows the same analysis for the differences of the model simulations of annual mean precipitation for 1988 and 1982 compared with the observed differences between these two years. The models perform best in the eastern part of the continent, as can also be seen in Table V; agreement between the models and the observations is poor in the north-west, north-central, and south-west regions. This may be partly because of the dominance of Figure 7. Regions of Australia for the calculation of regional statistics, and other locations mentioned in the text

13 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 213 Table VI. Model simulations of 1988 minus 1982 precipitation compared with observed 1988 minus 1982 for various regions (in millimetres per month) NW NC NE SW SC SE GCM ÿ DARLAM ÿ Observations ÿ20.9 ÿ the rainfall pattern in these two years by phases of the SO, whose observed correlations with rainfall are greatest in the eastern half of the continent (McBride and Nicholls, 1983). The interannual variability of the models may also be compared by examining the coefficient of variation, which is simply the standard deviation of the five realizations divided by the mean. Thus the coefficient of variation is a normalized measure that enables the variability of different locations and models to be compared. Those for the GCM and DARLAM are shown in Figure 8(a and b), for the five realizations of In general, the LAM has a greater inherent variability than the GCM. There may be several reasons for this result, but it is likely that the higher horizontal resolution and more heterogeneous orography of the LAM are significant factors. The LAM shows greater variability than the GCM even when the LAM results are interpolated to the GCM grid before the statistics are calculated. Such a clear increase in variability is not seen in the mean sea-level pressure fields (not shown), which suggests that increases in the precipitation variability in the LAM are locally forced. The results for 1988 (not shown) are qualitatively similar. One could compare these results to observations, but the model results shown in Figure 8 and similarly-derived observations would not be strictly comparable: the coefficient of variation as derived for the models represents the internal variability of the atmospheric system without SST variations, whereas a similar field derived from several years of observations would contain the effects of SST variations. 3.3 Month-to-month variability of anomalies Although it is useful to subtract the inherent long-time biases of the models from the simulations of individual months or years, this approach has some limitations. Most importantly, it assumes that whatever biases are present in the model climatology are also present in the simulations of individual months. This assumption may not be entirely correct in regions subject to temporal SST variations because of the non-linearity of the model response to these variations. Accordingly, we have also examined the ability of the models to simulate the observed month-to-month variability in these years, without the subtraction of any long-term model climatologies. In this way, we evaluate the ability of the models to simulate individual months directly, without any correction. Figure 9 shows the average monthly anomalies over all of Australia versus the long-term observed climatology for the GCM, DARLAM, and the observed rainfall. Both 1982 and 1988 are shown. In general, DARLAM exhibits larger positive anomalies than the GCM, and both tend to overestimate the observed rainfall in most months. Neither model shows great skill in the simulation of the observed signal. One difficulty is that the observed anomalies for 1982 have little variation. In 1988, when the observed anomalies show a strong trend from negative to positive anomalies, both models show increases in rainfall with time. It would, however, be unreasonable to expect the models to give excellent results given their known inherent biases and the fact that even variations in observed rainfall over Australia have maximum correlations with variations in SSTs or with the SO of the order of 016 (thus representing only about 40 per cent of the variance). Figure 10 shows the same anomalies for various regions of the continent. Figure 10(a) shows that for 1982, several regional differences are evident in the observed anomalies. The observed positive anomaly for the Northwest region for 1982, as shown in Figure 3(a), is largely caused by a strong positive anomaly at the start of the year in this year, which both models fail to simulate. In the eastern third of the continent, rainfall begins the year normal, but deficits grow as the year progresses, particularly in the South-east region. Neither model simulates a convincing drought in this region; this may be a result of the inherent positive model biases in this location (Figure 4). In the North-east region, the GCM shows some ability to simulate the observations, whereas

14 214 K. WALSH AND J. McGREGOR Figure 8. Coefficient of variation for 1982 realizations of (a) GCM; and (b) DARLAM DARLAM substantially overestimates summer rainfall. The large peak in the DARLAM simulation towards the end of the year (as opposed to a smaller peak at the beginning) is probably caused by an anomalously strong onshore flow in the GCM simulation of this month compared with the observations, thus forcing DARLAM to respond similarly. In the South-west and South-central regions, both models overestimate the observed winter rainfall. In the 1988 simulation (Figure 10(b)), the models appear to show more skill. There is good agreement between simulated and observed rainfalls over the North-west, North-central and South-west regions. Note, however, that

15 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 215 Figure 9. Monthly average anomalies over all Australian points for the GCM and DARLAM simulations and the observations of Drosdowsky versus the long-term climatology of Suppiah, for (a) 1982 and (b) Units are millimetres per month good agreement is expected in the usually dry winter months in tropical regions, because both model and observations should converge to low values. The observed trend of rainfall through the year in the North-east region is captured by both models, with the aforementioned biases of DARLAM superimposed on the trend of the GCM. The results are less good for the South-central and South-east regions, where large intermonthly variations are generally not captured by either model Pattern correlations The annual mean pattern correlations are shown in Table VII. They show that, as detailed in Walsh and McGregor (1995), the pattern of precipitation simulated by DARLAM is overall much superior to that of the GCM. The monthly correlations for both models are shown in Figure 11. On a month-by-month basis, DARLAM exhibits generally superior pattern correlations over the continent to the GCM. The regional breakdown of this statistic (Figure 12) shows that this is particularly so for the North-east and North-central regions during the wetter half of the year (austral summer), where the nested model has a much better pattern of simulated rainfall than the GCM. This is attributed to the higher resolution of DARLAM, and in particular the better specification of orography in the nested model. An exception is the South-central extratropical region, which encompasses South Australia and portions of the New South Wales and Victoria, which is only consistently better in the winter portion of 1982 and the second half of One reason for this may be that orography is generally less Table VII. Pattern correlations of precipitation over all of Australia (for annual-mean GCM and DARLAM versus observations) GCM DARLAM

16 216 K. WALSH AND J. McGREGOR Figure 10. (a) 1982 monthly average anomalies as in Figure 9(a) but for the various regions

17 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 217 Figure 10. (b) 1988 monthly average anomalies as in Figure 9(a) but for the various regions

18 218 K. WALSH AND J. McGREGOR Table VIII. Anomaly correlations of precipitation over all of Australia (for annual-mean GCM and DARLAM versus observations) GCM DARLAM 0.03 ÿ0.27 significant in this region. The figures show that DARLAM tends to perform best in seasons with high climatological rainfall: for example, in summer in the tropical regions, and in winter and spring in south-western and south-central Australia. As in the anomaly plots of Figure 10, there is considerable variation in skill, both between months, among the different regions, and between years in the same regions. The response of the models to SST perturbations is clearly influenced by many factors, not simply their inherent climatological biases Anomaly correlations The anomaly correlation of precipitation is a particularly rigorous test of a model s ability to simulate interannual variability, particularly if calculated over a subregion of the continent. In order for the model to perform well by this measure, there must be very good agreement in most respects between observations and the model simulation. This is particularly true of the method that we have presented here, where the long-term biases of the models are not subtracted from the model simulations of each year. Annual mean anomaly correlations are shown in Table VIII. These tend to show that neither model demonstrates skill. Monthly anomaly correlations over all of Australia are shown in Figure 13, and reinforce this conclusion. The explanation for this unexpected result is related to DARLAM have larger biases than the GCM in some regions of higher rainfall; despite that fact that it simulates a better mean precipitation pattern than the global model, its ability to simulate the correct pattern of the anomalies is seriously affected. The results over all of Australia show that neither model exhibits consistent skill in simulating the anomaly patterns over the entire year. Best results are obtained in 1982 in winter Figure 11. The same method as Figure 9 but for pattern correlations

19 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 219 Figure 12. (a) The same method as Figure 10(a) but for pattern correlations

20 220 K. WALSH AND J. McGREGOR Figure 12. (b) The same method as Figure 10(b) but for pattern correlations

21 SIMULATED CLIMATE VARIABILITY OVER AUSTRALIA 221 Figure 13. The same method as Figure 9 but for anomaly correlations by the GCM. The GCM is, however, not always better than DARLAM in this measure of skill, as shown towards the end of 1988, where DARLAM has superior results. The geographical variation of anomaly correlation (not shown) indicates that the overall skill level of the models in picking out the precise regional variations of precipitation patterns is not high. If we take a correlation of 015 as presenting some skill, correlations only exceed this criterion in a few regions and seasons. Moreover there appears little consistency in skill for the same seasons between the two simulated years. This is perhaps a highly quantitative way of saying that the regional simulation of climate variability of both models needs to be improved Discussion McBride and Nicholls (1983) give a summary of correlations between precipitation over Australia and indices of the SO, partitioned by season. It is instructive to compare the ability of the models in seasons and locations when good observed correlations occur with those when they do not. Anomalies are most relevant in this context, because observed correlations between the SO are calculated from regional or point deviations of rainfall from the long-term mean. For the annual mean rainfall, best correlations with the SO are observed in the South-east region, and to a lesser extent in the North-east and South-central regions. In Table VI, both models show some ability to simulate the correct sign of the differences between 1988 and 1982 rainfall in these regions. Less skill is seen in the other regions, where observed correlations are much lower. This result is in general agreement with the conclusions of Rocha (1993). For the individual monthly anomalies shown in Table V, the same is still true, although to a lesser degree, and DARLAM is generally performing better than the GCM at capturing the correct sign of the anomalies. When the actual values of the biases are examined, the two models appear to be of about equal quality, reflecting the generally greater biases of DARLAM. For the seasonal variations, best observed correlations are seen in southern spring (September November) in the central and eastern portion of the country. Examining the anomalies shown in Figure 10(a and b) for these regions, it is difficult to detect any genuine systematic improvement in these months and regions. The model results do not appear to be clearly better in spring than in other seasons. The same can be said about the anomalies averaged over the entire Australian region (Figure 9). We conclude that although the models have an ability to simulate the very gross annual changes in rainfall observed for different phases of the SO, in their present

22 222 K. WALSH AND J. McGREGOR configuration they lack an ability to simulate the seasonal differences and only have some ability to simulate the regional variability inherent in the observations. 4. CONCLUSION The ability of both a global model and a limited area model to simulate the observed interannual variability of precipitation over Australia was examined. Two contrasting years of precipitation, 1982 and 1988, were chosen, and the models forced with the observed SSTs for those two years. Both models give larger average precipitation in 1988 than in 1982, as observed, although DARLAM overestimates precipitation more than the GCM. At the regional scale (if Australia is divided into six regions), the models have some ability to simulate the observed regional differences in precipitation between the two years, but only in the eastern half of the continent. Both models exhibit some skill in simulating the observed regional anomalies for individual months, although such skill is not consistent for all months or over all regions of Australia, and is highest in the eastern half of the continent. Skill is improved in both models, particularly for DARLAM, if the long-term mean biases of the models are removed. The ability of both models to reproduce the observed patterns of precipitation anomalies at the subregional scale of the observations is largely inadequate. The GCM is of insufficient resolution to give an adequate simulation of regional climate. The nested model has higher resolution, and simulates a substantially better pattern of observed precipitation, but it suffers from some biases, particularly over regions of high orography. The result of this defect in DARLAM is that its ability to simulate the correct sign of observed anomaly patterns in these regions is substantially reduced. Even when model biases are removed, the DARLAM simulation of anomaly patterns at the subregional scale remains unsatisfactory. This result may be a result of inadequate representation of convection over orography. This has been shown previously to be a problem in long limited area model simulations (Giorgi, 1990; McGregor and Walsh, 1994). Several techniques have been proposed to deal with this problem, but the solution may lie in a combination of higher vertical resolution and improved parameterization of convection in DARLAM. There may be a more fundamental problem, however. It may be that no such simulation will ever be able to reproduce exactly the observed pattern of precipitation anomalies for a particular month because of the limits of predictability. This field remains an active area of research. ACKNOWLEDGEMENTS The authors would like to thank Ian Smith and Hal Gordon for useful discussions. Martin Dix, Rob Allan, Barrie Hunt, and Brian Sawford made a number of useful comments on an earlier draft of this work. We also gratefully acknowledge the financial support of the CSIRO Climate Change Research Program, which is partly funded by the Australian Department of Environment, Sport and Territories. REFERENCES Allan, R. J El-Niño Southern Oscillation influences in the Australasian region, Prog. Phys. Geogr., 12, Allan, R. J Regional case studies of teleconnections: physical aspects (Australia), in Teleconnections Linking Worldwide Climate Anomalies, Glantz, M. H., Katz, R. W. and Nicholls, N. (eds), Cambridge University Press, Cambridge, pp Allan, R. J Historical fluctuations in ENSO and teleconnection structure since 1879: near-global patterns, Quat. Aus., 11, Anthes, R. A., Hsie, E. Y. and Kuo, Y. H Description of the Penn State=NCAR Mesoscale Model Version 4 (MM4), Technical Note NCAR=TN-282 STR, National Center for Atmospheric Research, Boulder, Co., 66 pp. Arakawa, A Design of the UCLA General Circulation Model. Numerical Simulation of Weather and Climate, Technical Report No. 7, Department of Meteorology, University of California, Los Angeles. Chouinard, C., Béland, M. and McFarlane, N A simple gravity wave drag parameterization of use in medium-range weather forecast models, Atmos. Ocean., 24, Cordery, I. and Opuku-Ankomah, Y Temporal variation of relations between tropical sea-surface temperatures and New South Wales rainfall, Aust. Meteorol. Mag., 43, Davies, H. C A lateral boundary formulation for multi-level prediction models, Qt. J. R. Meteorol. Soc., 102, Deardorff, J. W A parameterization of ground-surface moisture content for use in atmospheric prediction models, J. Appl. Meteorol., 16, Dix, M. R. and Hunt, B. G Chaotic influences and the problem of deterministic seasonal predictions, Int. J. Climatol., 15, Drosdowsky, W An analysis of Australian seasonal rainfall anomalies: I: spatial patterns, Int. J. Climatol., 13, 1 30.

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