The Relationship between ENSO/IOD and Rainfall Extremes in Australia Karin Senior Meteorologist, Bureau of Meteorology, Melbourne, Australia E-mail: K.@bom.gov.au Dörte Jakob Manager Hydrometeorological Advisory Service, Bureau of Meteorology, Melbourne, Australia E-mail: D.Jakob@bom.gov.au Abstract The impacts of ENSO and IOD on extreme daily rainfall are explored. The effect of ENSO on the frequency of occurrence of rainfall events is found to extend over most of Australia with very high/low extreme rainfall more likely in La Niña/El Niño years. In the southeast coastal zone of Australia, the impact of ENSO on the daily extremes is not apparent. The links between the IOD and extreme rainfall in northern Victoria are also investigated. It is found that low daily rainfall extremes tend to occur more often in a positive phase of the IOD, however this also includes the effect from El Niño due to the interdependence of the IOD and ENSO. For northern Victoria, it is found that very high extremes in La Niña years tend to occur most often in the warmer months while very low extremes in El Niño years tend to occur most often in the cooler months. The IOD has a lesser impact on the very low and very high extremes than ENSO. In both phases of the IOD, the occurrence of very low and very high extremes occurs more often in the cooler months which is when the IOD influence is known to occur. The effect of IOD positive on the very low extremes is somewhat stronger than the effect of IOD negative on the very high extremes. The association of extreme rainfall events with ENSO and IOD can be used by decision-makers in planning and mitigating the impact of adverse weather conditions on their operations. The Bureau of Meteorology s seasonal forecasts use both ENSO and IOD to predict the likelihood of above or below average rainfall and can be used to assess the risk of extreme rainfall. 1. INTRODUCTION The variability of rainfall in Australia at inter-annual time scales and longer, impacts on many human activities, particularly those involving agriculture and engineering. The association between rainfall variability and large-scale drivers such as ENSO has been used successfully to provide seasonal forecasts of rainfall (Drosdowsky and Chambers, 2001, Cottrill et al 2013). Very heavy rainfall over shorter time-scales, such as one day, can cause severe disruption to operations and activities, as well as damage to infrastructure and landscape. Knowledge of whether there is an increased or decreased likelihood of extreme rainfall occurring would be useful in planning and assessing risk. This paper explores the relationship between daily rainfall extremes and large-scale drivers of circulation changes. In Australia, rainfall variability over inter-annual time scales is linked to large-scale drivers such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). ENSO and IOD are atmosphere-ocean coupling phenomena active in the Pacific and Indian Oceans respectively while the SAM is a band of westerly winds around Antarctica that expands and contracts. These large-scale drivers result in changes in the weather patterns which result in positive or negative anomalies in seasonal average rainfall over eastern Australia (Pittock, 1975, McBride & Nicholls, 1983, Risbey et al, 2009). The effect of large-scale climate drivers on Australian winter/spring rainfall at daily and subdaily time-
scales has been explored by Pui et al, 2012, who found that ENSO, IOD and SAM have more of an impact on the number of wet days in eastern Australia than the rainfall intensity. King et al (2013) found that the duration and intensity of extreme five-day rainfall in eastern Australia is affected more by a La Niña phase than an El Niño phase. In this study, the links between ENSO and IOD and daily extreme rainfall are explored. The definition of extreme rainfall in this study is the annual maximum series. Annual maxima are grouped into percentile ranges and the frequency of occurrence of annual maxima in the phases of ENSO and IOD is calculated. From this, we can infer the effect of the different phases of ENSO and IOD on the very high and very low extreme values. While the SAM would be expected to have an impact on extreme rainfall, the effect would be expected to be relatively small, therefore in this study we focus on the ENSO and IOD only. In this paper, the data used is discussed in section 2. In section 3 the probability distributions of the annual maxima in eastern Australia are presented and the influences of ENSO and IOD on the annual maxima in inland Victoria are studied. A conclusion is presented in section 4. 2. DATA A quality controlled daily rainfall dataset from Bureau of Meteorology daily rainfall gauges over Australia was used. The definition of year used to extract the annual maxima was chosen to match the ENSO year and spans the months between April of one year and March of the next year. ENSO usually establishes itself in the Austral autumn around April/May and breaks down in the Austral summer. From all available daily rainfall gauges, suitable sites were then selected based on having long records. Some sites had missing or incomplete data in some years. Criteria set for acceptance of an annual maximum for a year were defined such that each year had to have at least 10 months with each of those months having least 75% of data. In addition, if rejected annual maxima happened to be in the top 90 th percentile of all the annual maxima, then they were added back in because it would be highly likely that the true annual maximum for that year had been sampled. After taking into account missing data, stations were accepted only if they had at least 100 AMS values in the series. This requirement was relaxed slightly to 80 years for western Tasmania due to a lack of sites with long records. This resulted in a total of 1261 sites over all of Australia. For most sites, the earliest annual maximum occurs at around 1900. 3. STATISTICS OF THE DAILY EXTREME VALUES UNDER ENSO 3.1. Probability distributions of daily extreme values The effect of ENSO on seasonal rainfall is strongest in eastern Australia (Risbey et al, 2009). In this section, a subset of the 1261 sites that are located east of 130 degrees longitude was used to examine some of the general statistics of the annual maxima in the phases of ENSO. The annual maxima for each site were separated into three groups according to whether they occurred in a La Niña year, an El Niño year or a neutral year with the year spanning the ENSO year. Years were assigned to a La Niña or an El Niño year using the classification at http://www.bom.gov.au/climate/enso/lnlist/ and http://www.bom.gov.au/climate/enso/enlist/. For years prior to 1902, the Southern Oscillation Index (SOI) was used and El Niño events were doublechecked against Ortlieb, 2000 who used historical documented reports to list El Niño events since the sixteenth century. The region was then divided into two zones: an Eastern Australia Zone and a Coastal Southeast Zone (Figure 1) in recognition of the fact that the response of rainfall in these regions to ENSO may be different (Risbey et al, 2009). The Great Dividing Range was used to define the two zones. (Figure 1 also shows stations in other zones used later in Section 3.2.) HWRS 2014,Jakob 2 of 8
The probability density functions (PDFs) for the annual maxima (standardised with respect to the mean) in each category of the states of ENSO were calculated for the two zones and they are shown in Figure 2. The other regions are discussed in Section 3.2. The PDFs show that the mean of the annual maxima in the Eastern Australia Zone in La Niña years is shifted slightly towards higher values than in Neutral or El Niño years. Further, the tail of the distribution in La Niña years is more skewed towards higher values than El Niño or neutral years. In contrast, the PDFs for the Coastal Southeast Zone annual maxima do not show such dissimilarities in the different states of ENSO. Figure 1 Stations located in zones Figure 2 Probability density functions for annual maxima standardised with respect to the mean for the South East Coastal (left) and Eastern Australia (right) zones. Bold lines are composites of all sites in each zone. The Kolmogorov-Smirnov (KS) test was applied to ascertain whether the cumulative density functions (CDFs) under El Niño and La Niña were from the same distribution. The null hypothesis was rejected in 35% of the cases at the 95% level in the Eastern Australia Zone and 6% in the Southeast Coastal Zone. Therefore the annual maxima experience a shift in the probability distributions in the Eastern Australia Zone which is significant in just over a third of the stations, but in the South East Coastal Zone such a shift is very weak. Even in the Eastern Australia Zone, the implication is that the distributions for the annual maxima for El Niño and La Niña are not significantly different at the 95% confidence level. The PDFs for the South East Coastal Zone show a greater amount of overlap between those from the three states of ENSO. 3.2. The highest and lowest of the extremes and ENSO For most sites, the KS test did not show that the distributions of the annual maxima are different in El Niño and La Niña years. However, Figure 2 for the Eastern Australia Zone shows that the La Niña HWRS 2014,Jakob 3 of 8
distribution is highly skewed towards very high values and the PDFs for El Niño events tend to be shifted towards lower values. In this section, we address the question: Is it more likely that the highest of the annual maxima occur in La Niña years and the lowest of the annual maxima in El Niño years? The annual maxima for each site are ranked from low to high and the 20 th, 40 th, 60 th, 80 th percentiles are evaluated. The annual maxima are then split into five groups, with each group corresponding to a percentile range (0% 20%, 20% to 40%, 40% to 60%, 60% to 80% and 80% to 100%). The annual maxima in each of the five groups are then standardised with the mean of all the annual maxima. Figure 3 shows boxplots of the standardised annual maxima in each of the five groups as well as boxplots of all groups together for the three phases of ENSO. The annual maxima in the highest percentile range account for a large amount of the spread in the annual maxima, however there is not much variation between the phases of ENSO. The mean is shifted slightly to lower values for El Niño taking all the annual maxima together, however, there is not such a variation when the annual maxima are split into the five groups. The same result was obtained by selecting a subset of stations that are located in Victoria north of the Divide and the other zones shown in Figure 1. From this result, it appears that ENSO has little effect on the magnitude of the annual maxima. Even the very high extremes appear to have similar magnitudes in the three phases of ENSO. Figure 3 Standardised annual maxima in the Eastern Australia Zone for five percentile ranges and all percentile ranges (right column). The relative frequency of occurrence of annual maxima in the three phases of ENSO (El Niño, La Niña and Neutral) is then calculated. This involves counting the number of events that occurred in each of the three phases of ENSO in each of the groups and expressing these counts as a percentage of the total number of events in the three phases of ENSO. If the daily rainfall extremes are independent of ENSO, then it would be expected that the relative frequency would be 20% in each group since there are five groups. The CDF for a large number of samples was simulated and for a random process the 95% confidence intervals would be expected to have relative frequencies of 17.5 and 22.5%. Furthermore, a relative frequency of 30% would be exceedingly rare. The sites that had many very high annual maxima in the top percentile range (80% to 100%) and very few annual maxima in the bottom percentile range (0% to 20%) with relative frequencies of 30% more in each of these percentile ranges are shown in Figure 4. These sites represent those that had relatively many very high annual maxima in La Niña years and relatively few low annual maxima in El Niño years. There are several such sites in the Eastern Australia Zone, fewer in the rest of Australia but relatively few in the Southeast Coastal Zone. The opposite situation, with relatively many high annual maxima in El Niño years and relatively few low annual maxima in La Niña years did not occur at all. This suggests that the relatively higher frequency of extremely high annual maxima in La Niña years and very low annual maxima in El Niño years could not have occurred by chance alone. In the Southeast Coastal Zone, however, the effect of ENSO on the frequency of the extremes of the extremes is very weak. The relative frequencies of annual maxima in each of the percentile groups for the different phases of HWRS 2014,Jakob 4 of 8
ENSO were averaged over a number of sites to smooth out local variations. The sites were grouped together into zones that showed similar patterns in the relative frequencies. The barplots in Figure 5 show the relative frequencies for all the zones. For the Eastern Australia Zone, the average relative frequency in neutral years lies within the 95% confidence intervals, but the far ends of the percentile ranges show an increased likelihood of very high annual maxima in La Niña years and a decreased likelihood of very low annual maxima in El Niño years, which is significant. For the Southeast Coastal Zone the average relative frequencies are barely significant, reminiscent of the white strip (Timbal, 2010, Risbey et al, 2009), the coastal region in the Eastern Seaboard of Australia with weak and statistically insignificant (at the 95% level) correlation between rainfall and ENSO and IOD indices. The high extremes in the Northern Territory are more affected by La Niña while for Tasmania the low extremes are more affected by El Niño. The plots for Western Australia and South Australia are similar to those for the Eastern Australia Zone except the effect of ENSO on the high and low extremes, is weaker though still significant. The procedure was first attempted using smaller zones. These were combined for simplicity when the effects of ENSO were found to be similar in each of the smaller zones. Figure 4 Sites that are wettest under La Niña and driest under El Niño (green stars) (relative frequency of 30% or more in the highest percentile range for La Niña and the lowest percentile range for El Niño). Other stations are shown as black dots. The results from Figures 3, 4 andfigure 5 show that ENSO affects the frequency of extreme rainfall events with very high extremes occurring more frequently in the La Niña phase and very low extremes more likely in the El Niño phase. The very high/low extremes can still occur in an El Niño/La Niña but with a decreased frequency of occurrence. Figure 3 suggests the magnitudes of annual maxima are not markedly different in the different phases of ENSO, though the averages are affected somewhat. This is similar to the result obtained by Pui et al, 2012 even though they were considering rainfall events in general not just extreme rainfall. 3.3. The effect of ENSO and the IOD on extreme rainfall The IOD has three phases: positive, negative and neutral. When the IOD is positive/negative, seasonal rainfall tends to be below/above average. The phases of IOD and ENSO can enhance seasonal rainfall, though the IOD and ENSO are not independent of each other. The effects of ENSO and the IOD on seasonal rainfall vary in intensity depending on geographic location. ENSO affects large parts eastern Australia while the IOD affects the southern part of Australia extending from Western Australia into Victoria (Risbey et al, 2009). One region that is affected by both IOD and ENSO is northern Victoria. This region is defined as that part of Victoria that lies north of the ranges and which does lie in the Southeast Coastal Zone. In this section, the effects of both ENSO and IOD on the daily extreme rainfall values are studied. The annual maxima were classified into groups according to which phase of ENSO they occurred in as was described in section 3.1. Then they were also classified according to the IOD phase (positive, negative and neutral IOD). The IOD years were classified using the IOD positive and negative years from the Bureau of Meteorology website at http://www.bom.gov.au/climate/iod/negative/ and http://www.bom.gov.au/climate/iod/positive/ for the years from 1958 onwards and Meyers et al 2007 for the years prior to 1958. There is greater certainty about the IOD events for 1958 onwards because HWRS 2014,Jakob 5 of 8
they have been well-documented. There is less data available to verify the events prior to 1958. There are some differences in IOD years from the two sources. The analysis was carried out for IOD classifications using Meyers et al, 2007 only and the combined Meyers et al, 2007 and Bureau of Meteorology classifications. The results were very similar. Combinations of ENSO and IOD phases were also attempted however there were insufficient numbers of years in each category available for this analysis. Figure 5 Barplots showing the relative frequency of annual maxima in the three phases of ENSO in five percentile ranges for six zones in Australia. Solid light blue line shows the expected relative frequency. Dotted blue lines show approximate 95% confidence intervals. (Percentile ranges number 1 to 5 refer to 0-20, 20-40, 40-60, 60-80 and 80-100 percentile ranges). Figure 6 Barplots showing relative frequency of annual maxima for (a) ENSO, (b) IOD for inland Victoria. Solid black line shows the expected relative frequency. Dotted lines show approximate 95% confidence intervals. Figure 6 shows the relative frequency of the annual maxima in the five percentile ranges averaged over all sites in northern Victoria for (a) ENSO years and (b) IOD years. These plots show that the frequency of very high and very low annual maxima is affected by ENSO, however with the IOD it is only the frequency of the low annual maxima that is affected significantly. Since the IOD and ENSO are not independent some of the effect of the IOD on the low annual maxima could also be due to ENSO events. But the effect on the frequency of very high annual maxima is more apparent in La Niña years than in negative IOD years. This is likely to be an effect of seasonality, because the IOD affects HWRS 2014,Jakob 6 of 8
rainfall mainly in winter and spring when northwest cloud bands interact with fronts extending northward from the Southern Ocean. Strong ENSO events can still be evident in the summer months when higher temperatures can contribute towards higher moisture availability. The effect of seasonality on the annual maxima is discussed in the next section. 3.4. Seasonality The frequency of occurrence of annual maxima for northern Victoria in the different months of the year was explored for each of the phases of ENSO and IOD where the annual maxima were either very high or very low. Figure 7(a) shows the total number of occurrences of annual maxima for each month scaled with the highest (February) and represents an overall long-term seasonal cycle. Figures 7(b) to (e) show the number of occurrences of annual maxima as a percentage of all years. This means the normal seasonal cycle has been removed somewhat. In spite of that, there is still a strong seasonal signal that shows that in La Niña years, the very high annual maxima tend to occur in the warmer months of the year, with a peak in February and very few in the winter months. This is probably because in La Niña years, the monsoon is more active and extends further southwards. In contrast, in El Niño years, the monsoon is suppressed due to cooler waters to the north of Australia and the annual maxima tend to more often occur around May/June, probably from cut-off lows and frontal systems. Figure 7 Barplots showing the number of annual maxima in each month for (a) all years scaled with the maximum, (b) El Niño years with very low annual maxima, (c) La Niña years with very high annual maxima, (d) IOD positive years with very low annual maxima and (e) IOD negative years with very high annual maxima, expressed as a percentage of the total number of annual maxima for each ENSO/IOD phase. Dashed lines indicate the average across all months. The effect of IOD positive on very low annual maxima is somewhat stronger than for IOD negative with very high annual maxima. Overall, the effect of IOD on the very low and very high extremes is weaker than that from ENSO. For both phases of IOD the very low and very high extremes tend to occur more in the cooler months, which is to be expected since its influence extends from Autumn until Spring. HWRS 2014,Jakob 7 of 8
4. CONCLUSION The effects of the phases of ENSO and IOD on extreme daily rainfall have been explored. It is found that the CDFs of annual maxima for the El Niño and La Niña phases of ENSO are significantly different using the Kolmogorov-Smirnov test in about a third of stations in Eastern Australia. ENSO affects the frequency of occurrence of extreme rainfall. In the La Niña phase of ENSO extreme daily rainfall occurs more often than in El Niño years. In northern Victoria, the positive phase of the IOD increases the likelihood of low annual maxima though El Niño is likely to be contributing because of the inter-dependence of IOD and ENSO. The effect of seasonality on the very low (0-20 percentile range) and very high (80-100 percentile range) annual maxima was explored. In northern Victoria, the very low annual maxima in El Niño years tend to occur more often in May/June. During La Niña years few of the very high annual maxima occur during the months June to August. Most often they tend to occur in the warmer months, with February experiencing the largest number of very high annual maxima. The effect of IOD on the very low and very high extremes is weaker than that for ENSO. With IOD the effect on the annual maxima is somewhat stronger in the cooler months. The relatively frequent occurrence of annual maxima in the months when the IOD has broken down, suggests that the IOD does not impact heavily on the very high and very low annual maxima. The effect of ENSO in increasing/decreasing the likelihood of extreme daily rainfall during the La Niña/El Niño phase of ENSO is stronger. This information can be used together with the Bureau s seasonal forecasts to assess the risk of occurrence of extreme daily rainfall. 5. REFERENCES Cottrill, A., Hendon, H.H., Lim Eun-Pa, Langford, S., Shelton, K., Charles, A., McClymont, D., Jones, D. and Kuleshov, Y. (2013), Seasonal Forecasting in the Pacific using the Coupled Model POAMA-2, Weather and Forecasting, 28, pp 668-680. Drosdowsky, W., and Chambers, L.E., (2001), Near Global Sea Surface Temperature Anomalies as Predictors of Australian Seasonal Rainfall. Journal of Climate, 14, pp 1677-1687. King, A.D., Alexander L.V. and Donat, M.G. (2013), Asymmetry in the response of eastern Australia extreme rainfall to low-frequency Pacific variability, Geophysical Research Letters, Vol 40, 1-7. McBride, J. and Nicholls, N. (1983), Seasonal Relationships between Australian Rainfall and the Southern Oscillation, Monthly Weather Review, 111, pp 1998-2004. Ortlieb, L. (2000). The documented historical record of El Niño events in Peru: An Update of the Quinn record (sixteenth through nineteenth centuries. El Niño and the Southern Oscillation Multiscale Variability and Global and Regional Impacts, Henry F. Diaz and Vera Markgraf, Eds., Cambridge University Press, PP. 207-295. Pittock, A. (1975). Climatic Change and the Patterns of Variation in Australian Rainfall, Search, 6, pp 498-504. Pui, A., Sharma, A., Santoso, A. and Westra, S. (2012), Impact of the El Niño Southern Oscillation, Indian Ocean Dipole, Southern Annular Mode on Daily to Subdaily Rainfall Characteristics in East Australia. Monthly Weather Review, 140, pp 1665-1682. Risbey, J.S., Pook, M.J., McIntosh, P.C., Wheeler, M.C. and Hendon, H.H. (2009), On the Remote Drivers of Rainfall Variability in Australia, Monthly Weather Review, 137, pp 3233-3253. Timbal, B., (2010), The Climate of the Eastern Seaboard of Australia: A Challenging Entity Now and for Future Projections. 17 th National Conference of the Australian Meteorological and Oceanographic Society, IOP Conf. Series: Earth and Environmental Science (2010) 012013. HWRS 2014,Jakob 8 of 8