Australian rainfall variability and change

Australian rainfall variability and change Neville Nicholls, Wasyl Drosdowsky and Beth Lavery Bureau of Meteorology Research Centre, Melbourne, Australia Australian rainfall is more variable than could be expected from similar climates elsewhere in the world. Much of this additional variability is related to the impact of the El NifioSouthern Oscillation (ENSO) on Australia. There have been longterm variations in the influence of ENSO on Australian rainfall. Variations in rainfall are closely related to variations in the diurnal temperature range, on both interannual and decadal timescales. Only in the past decade or so has this relationship broken down, perhaps reflecting a significant shift in the operation of the climate system in this region. Mean annual rainfall in 195292 was significantly greater than in 191151, and the variability also increased, although not significantly. Rainfall variability Conrad (1941) examined the relationship between interannual rainfall variability and longterm mean annual rainfall, using data from across the globe. He defined the relative variability of annual rainfall as the mean of the absolute deviations of annual rainfalls from the longterm mean, expressed as a percentage of the longterm mean. Conrad found that the relative variability decreased, in general, as the mean precipitation increased. Over some large areas, however, the relative variability was consistently larger than would be expected from the global relationship with mean rainfall. Some of these deviations were due to the influence of the ENS0 phenomenon on rainfall. Nicholls (1988) compared the relationship between relative variability and mean rainfall in areas affected by ENS0 with the relationship elsewhere. The relative variability was typically one third to one half higher for these stations, compared with stations with the same mean rainfall in areas not affected by ENSO. Nicholls (1988), following Conrad, found that the relationship: V = 12.5 + 4569/(P+77.3), where V is the relative variability and P is the longterm mean annual rainfall (mm), fitted the global rainfall data quite well. We have calculated the ratio of the observed relative variability at Australian rainfall stations to the relative variability predicted from this global relationship. We used a new, highquality rainfall dataset of 341 Australian rainfall stations (Lavery et al. 1997). These stations all have good data from at least 1910, and cover the country adequately (although coverage inland is poorer than in the coastal regions). The results (Fig. 1) indicate where rainfall variability is high relative to the global pattern expected for the same mean rainfall. This is the case across much of the country. Only in the southwest and southeast is the variability lower than would be expected from the above relationship (i. e. the ratio of observed to predicted relative variability is less than 1.0 in these areas). Elsewhere the ratio exceeds 1.0, indicating that the rainfall is more variable than would be the general case for stations with similar mean rainfall elsewhere in the world. In some areas on the northeast coast the ratio approaches 2.0. Mean annual rainfall across Australia is shown in Fig. 2. The centre of the continent is dry, with annual rainfall less than 100mm. Rainfall on the north and east coasts can exceed 1400mm; so the spatial variability of rainfall is large, as is the temporal variability. There is no obvious simple relationship between mean rainfall and the ratio of observed to predicted relative rainfall variability (Fig. 1). Areas where the relative variability is considerably larger than that predicted from global data occur both in the dry regions and in the very wet parts of the northeast coast of the conti 66

1.00 1 26 I 60 1.76 Fk. 1 Ratio of relative rainfd variabilty (see text for dejinihn) w that expecdfimn global relationship. The 1.0 contour is thick. The white areas indicate where Australian rainfd variabiliiy is lower than that expectedfimn global patterns for areas with similar annual mean rainjd. The shaded areas indicate where Australian rainfan is more variable than could be expecredjm the global pattern. Data are for 191e92. 100 200 400 000 Boo 1000 1200 1400 Fig. 2 Mean annual rainjd (mm) using the same data as for Fk. 1 67

nent. The areas where the variability is high tend to be those areas most affected by ENSO. In the southeast and southwest (where the variability is relatively low) other factors also affect rainfall (e.g. Nicholls 1989; Drosdowsky 1993a,b). The relatively high variability of Australian rainfall has a number of implications. It makes the detection of longterm trends more difficult (because of the higher interannual noise caused by this variability). The variable rainfall also affects Australian vegetation (Nicholls 1991) and agriculture. Even averaged across the country, annual rainfall is rather variable. The mean allaustralia average annual rainfall (191092) was 471mm, with a standard deviation of 82mm. Figure 3 shows a histogram of these all Australia averages of annual rainfall. The frequency distribution deviates considerably from the normal distribution, because it is highly skewed. Even the gamma distribution, often used for rainfall (Thom 1958), does not provide a good fit. A good fit is provided by the Gumbel (or extreme ) distribution, as shown in the figure. The skewed distribution reflects the fact that occasional years (e.g. 1974) are very wet across nearly all the country. In 1974 rainfall was in the highest decile of recorded an nual falls over about 65 per cent of the country. Rainfall and ENS0 A timeseries of the annual rainfall averaged across Australia (from Lavery et al. 1996) is shown in Fig. 4. The Southern Oscillation Index (SUI the standardised difference between Tahiti and Darwin surface atmospheric pressure) is also shown in this figure. Weighted leastsquares smoothers were used to highlight the longterm variations in the two timeseries. The two series are clearly related, with high rainfall in years when the SOI is large and positive. Such years are La Niiia events. El Nino years (when the SOI is strongly negative) are usually years of drought over much of Australia. This relationship is well known (e.g. McBride and Nicholls 1983). Less well known is that there have been longterm variations in this relationship (e.g. OpokuAnkomah and Cordery 1993). One aspect of these variations is illustrated in Fig. 4. Although in general the variations from year to year are positively correlated (correlation coefficient, r = 0.50, significant at 1 per cent), in some periods there appears to be a bias in one of the variables. Before about 1970 the two series are, for most years, well separated in the figure, whereas 26. q 2 (u 24 I 22 I 16, 14. 350 (400 4501 (500 5501 (600 6501 (700 7501 > 800 (350 4001 (450 500) (550 6001 (650 7001 (750,8001 Rainfall (rnrn) Fig. 3 Awiid razizfalls averaged u~ross Australia, 191092. A Cunibel (exrrerne) dzsmbunonfimd w the data 2s also s/wzv?i (co?itiviuoub he). The brakecs (1 indicate that each range includes the upper limit but rwt the lower one. 68

900.. 25 800 700 600 d._ 500 400 300 I 20 )I0 1920 1930 1940 1950 1960 1970 1980 1990 Year Fig. 4 Timeseries of annual average Australian rainfall (continuous line) and the Southern Oscillation Index (SOI, broken line), 191&92. The thick lines are the result of applying a weighted leastsquares smoother to the annual values. since the 1970s they have been closely aligned. So, since the early 1970s rainfall appears to have been greater, relative to the SOI, than was the case in earlier years (Nicholls et al. 1996). Rainfall and diurnal temperature range The average Australian rainfall timeseries is repeated in Fig. 5, along with a timeseries of the diurnal temperature range, again averaged across the country (in the figure the negative of the diurnal range is plotted, to simplify the comparison between the series). The temperature data are from a new dataset of 149 rural stations. Each station has data since at least 1910. The data from these stations have been adjusted to correct for changes in instrumentation and exposure (Torok and Nicholls 1996). The close relationship between the variables is clear, at both short and long timescales, with high rainfall being accompanied by a reduction in the diurnal temperature range. The correlation between the two timeseries is 0.74 (significant at 1 per cent) over the 83 years. Only at the end of the record (from the early 1980s) do the timeseries diverge. Even in this part of the record, however, the interannual variations are closely related, with the diurnal range nega tively correlated with rainfall. The divergence at the end of the series may suggest a change in the nature of the climate system over Australia, or a problem with the data. Rainfall is correlated with maximum temperature, averaged across Australia (r = 0.52, significant at 1 per cent), and almost independent of minimum temperature (r = 0.13, not significant). However, maximum and minimum temperatures are closely correlated with each other (r = 0.62, significant at 1 per cent), so calculation of partial correlations is required to reveal the underlying temperaturerainfall relationships. The partial correlation between maximum temperature and rain, with the effect of the relationships of minimum temperature with the other two variables removed, is 0.77 (significant at 1 per cent), indicating a strong underlying tendency for years with heavy rains to be cool during the day. Likewise, the partial correlation of minimum temperature with rain, controlling the relationships of maximum temperature with both the other variables, is 0.68 (significant at 1 per cent). So there is a strong underlying tendency for wet years to also have high minimum temperatures. The underlying relationships between maximum and minimum temperature and rainfall 69

900 800 700 $ 600 3 E 500 400.11.25 1 1.50 1 1.75 12.00 U 12.25 5 12.50._ Y 12.75 $ P 13.00 2 13.25 8 E 13.50 I" 13.75 3 c 300 1910 1920 1930 1940 1950 1960 1970 1980 1990 14.00 14.25 Fig. 5 Timesenis of unnuul uveruge Australian rainjall (continuous line) and the diurnal temperature range, also averaged across Australia (broken line), 191092. T%e thick lines are the result of applying a wtzghted leastsquares smoother to the annual values. Note that the scale for the diurnal temperature range has been reversed, w emphasire the negative relationship with the min fnll 16.5 I t 350 400 450 500 550 600 650 above Rainfall (mm) 27.0 27.5 28.0 28.5 29.0 29.5 Maximum temperature ("C) Fig. 6 Contour analysis of annual rainfall plotted against annual maximum and minimum temperatures, with all variables averaged across Australia. The white dots indicate locations of the data (years). 77ze shading s h s the empirical variatzon of rainfall with muximuni and minimum temperatures. Data are for 191092. are illustrated in Fig. 6, which plots rainfall against the two temperature variables. The figure illustrates the links between maximum and minimum temperatures and rainfall. The strong positive relationship between the two temperatures is clear. Also evident is that at any value of the maximum temperature, an increase in minimum temperature is associated with increased rainfall. At any value of minimum temperature, rainfall decreases as maximum temperature increases. In summary, rainfall is strongly correlated 70

14 l3 t " <= 350 (400,4501 (500,5501 (600,6501 (350,4001 (450,5001 (550,6001 > 650 Rainfall (rnrn) 191151 195292 Fig. 7 Annual average Australian rainfall fm 191151 and 195292. Brackets as in F& 3. with both maximum temperature (negative correlation) and minimum temperature (positive correlation), although the correlation between minimum and maximum temperatures masks these relationships. These relationships may simply reflect the effect of cloud cover on radiation, with increased cloud leading to cool days and warm nights. Lough (1995) found that relationships between Queensland rainfall and maximum and minimum temperatures varied with the season, although the correlation of rainfall and diurnal temperature range was the same (0.77, significant at 1 per cent) in both summer and winter. Trends in rainfall Histograms of the annual average Australian rainfall are shown in Fig. 7, divided into two equal subsets: 191151 and 195292. The mean rainfall is slightly, but significantly (at the 5 per cent level), larger in the more recent period (489mm compared with 451mm). The standard deviation has increased by a larger ratio (from 69.2mm to 90.7mm), reflecting the influence of a few recent years (197375, see Fig. 4) with heavy rains. This increase is not statistically significant, at the 5 per cent level. Nicholls and Lavery (1992) describe regional trends across Australia. The small trend in annual average Australian rainfall reflects the combination of a strong increase in summer rainfall in much of the east of the country and a decrease in winter rainfall in the southwest corner. Lough (1991) found that both the mean and the variability of Queensland summer rainfall increased significantly in the second half of the twentieth century. Concluding remarks This paper has discussed several aspects of Australian rainfall variability which have received little attention in the past, viz. the variability of Australian rainfall relative to the rest of the world, how the influence of ENS0 has varied with time, how rainfall variations are related to temperatures, and how the variability of rainfall has changed through the twentieth century. These, and other, aspects of Australian rainfall deserve increased attention, as they can have important messages for the sustainable management of the country. For instance, knowledge that the rainfall is more variable in Australia than is the general situation elsewhere should affect landmanagement practices. The availability of a set of highquality rainfall data (Lavery et al. 1997) should assist in the more complete documentation of this crucial variable. 71

~ (1993b) References Conrad, V. (1941) The variability of precipitation. MOX Wea. Rev., 69, pp. 511 Drosdowsky, W. (1993a) An analysis of Australian seasonal rainfall anomalies: 19501987. 11: temporal variability and teleconnection patterns. Itif. J. Clirnatol., 13, pp. 111149 Potential predictability of winter rainfall over southern and eastem Australia using Indian Ocean sea surface temperature anomalies. Am. Mereorol. Mag., 43, pp. 16 Lavery, B. M., Joung, G. and Nicholls, N. (1997) An extended high quality historical rainfall data set for Australia. Awt. Meteorol. Mag. (in Press) Lough, J. M. (1991) Rainfall variations in Queensland, Australia 18911986. Int. J. Climawl., 11, pp. 745 768 (1995) Temperature variations in a tropical subtropical environment: Queensland, Australia 191@1987. hit. J. Climatol., 15, pp. 77~95 McBride, J. L. and Nicholls, N. (1983) Seasonal relationships between Australian rainfall and the Southem Oscillation. Mon. Weu. Rev., 111, pp. 19982004 Nicholls, N. (1988) El NixieSouthern Oscillation and rainfall variability. J. Clzm., 1, pp. 418421 (1989) Sea surface temperatures and Australian winter rainfall. J. Cliwi., 2, pp. 965973 (1991) The El NifioSouthern Oscillation and Australian vegetation. Vegetation, 91, pp. 2336 Nicholls, N. and Lavery, B. (1992) Australian rainfall trends during the twentieth century. Int. 3. Cliwiutol., 12, pp. 153163 Nicholls, N., Lavery, B., Frederiksen, C., Drosdowsky, W. and Torok, S. (1996) Recent apparent changes in relationships between the El NiiioSouthern Oscillation and Australian rainfall and temperature. Geophys. Res. Len., 23, pp. 33573360 OpokuAnkomah, Y. and Cordery, I. (1993) Temporal variation of relations between New South Wales rainfall and the Southern Oscillation. Irit. J. Cliwiatol., 13, pp. 5164 Thom, H. C. S. (1958) A note on the gamma distribution. Mon. Weu. Rev., 86, pp. 117121 Torok, S. and Nicholls, N. (1996) An historical annual temperature data set for Australia. Aust. &Jereorol. Mag., 45, pp.251260 Back to basics: Light in the atmosphere: Part 1 Why the sky is blue Grant R. Bigg University of East Anglia, Norwich When we look into the sky we see different colours all around us: the blue of the sky, white clouds, black clouds, red sunsets and dawns, rainbows. Even the night sky is not wholly black. Stars and planets, some of which can be seen by the naked eye to be coloured, twinkle, and the moon shows shades of grey, and some Table 1 Relative sizes of molecules, particles, water droplets, and wavelengths of light Particle Typical dimensions (m) Oxygen molecule 10l0 Sulphatc particle 1 o ~ Salt aerosol 10.: Cloud droplet 105 Raindrop 10 Bluelight wavelength 4.5 x 10.~ Redlight wavelength 6.5 x 10.~ times can be orange, red or even blue. Our visual perception of these phenomena arises from a number of different physical effects scattering, reflection, refraction. In this article we will consider those due to interaction of light with the basic molecules and particles of the air while Part 2 will examine the impact of water and ice particles on light transmission. Light and air The radiant energy from the sun consists of waves that are spread across a wide part of the electromagnetic spectrum; the peak energy flux is in the socalled visible part of the spectrum that part which our eyes detect (wavelengths between 4.3 x and 6.9 x 107m) and which, in combination, gives the colour white. These 72