Impact of Climate Modes such as El Nino on Australian Rainfall


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The relationship between seasonal aggregate rainfall and large scale climate modes, particularly the El Niño-Southern Oscillation (ENSO), has been the subject of a significant and on-going research effort. However, relatively little is known about how the character of individual rainfall events varies as a function of each of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storm inter-event time at both daily and sub-daily timescales in East Australia, as a function of indices for ENSO, the Indian Ocean Dipole (IOD), and the Southern Annular Mode (SAM), with a focus on the cool season months. Long record data sets have been used to sample large variety of climate events for better statistical significance. Results using both the daily and sub-daily rainfall data sets consistently show that it is the occurrence of rainfall events, rather than the average intensity of rainfall during the events, which is most strongly influenced by each of the climate modes. This is shown to be most likely associated with changes to the time between wet spells. Furthermore, it is found that despite the recent attention in the research literature on other climate modes, ENSO remains the leading driver of rainfall variability over East Australia, particularly further inland during the winter and spring seasons.

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Impact of Climate Modes such as El Nino on Australian Rainfall

  1. 1. Impact of the El Nin˜o–Southern Oscillation, Indian Ocean Dipole, andSouthern Annular Mode on Daily to Subdaily Rainfall Characteristicsin East AustraliaALEXANDER PUI AND ASHISH SHARMASchool of Civil and Environmental Engineering, University of New South Wales, Sydney, AustraliaAGUS SANTOSOClimate Change Research Centre, University of New South Wales, Sydney, AustraliaSETH WESTRASchool of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia(Manuscript received 22 July 2011, in final form 10 December 2011)ABSTRACTThe relationship between seasonal aggregate rainfall and large-scale climate modes, particularly theEl Nin˜o–Southern Oscillation (ENSO), has been the subject of a significant and ongoing research effort.However, relatively little is known about how the character of individual rainfall events varies as a function ofeach of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storminterevent time at both daily and subdaily time scales in east Australia, as a function of indices for ENSO, theIndian Ocean dipole (IOD), and the southern annular mode (SAM), with a focus on the cool season months.Long-record datasets have been used to sample a large variety of climate events for better statistical signif-icance. Results using both the daily and subdaily rainfall datasets consistently show that it is the occurrence ofrainfall events, rather than the average intensity of rainfall during the events, which is most strongly influ-enced by each of the climate modes. This is shown to be most likely associated with changes to the timebetween wet spells. Furthermore, it is found that despite the recent attention in the research literature onother climate modes, ENSO remains the leading driver of rainfall variability over east Australia, particularlyfarther inland during the winter and spring seasons. These results have important implications for how waterresources are managed, as well as how the implications of large-scale climate modes are included in rainfallmodels to best capture interannual and longer-scale variability.1. IntroductionUnderstanding rainfall variability at interannual andlonger time scales is an important area of study withmany practical implications for how we manage ourwater resources. This variability, which is often linkedto both internally and externally forced fluctuations inthe global sea surface temperature (SST) field (Bigget al. 2003; Westra and Sharma, 2010), can often affectthe weather patterns of entire continents via a set oflarge-scale pressure and circulation anomalies knownas teleconnections that transmit these anomalies acrosslarge distances (e.g., Barnston and Livezey 1987).In Australia, much of the early research into climatevariability focused on the El Nin˜o–Southern Oscillation(ENSO) phenomenon, which has been linked to pre-cipitation anomalies across Australia, particularly overthe eastern third of the continent (Pittock 1975; McBrideand Nicholls 1983; Drosdowsky 1993; Chiew et al. 1998).This phenomenon is usually described in terms of cou-pled oceanic and atmospheric variability centered onthe central and eastern tropical Pacific Ocean, with theextreme phases of El Nin˜o and La Nin˜a typically re-sulting in below- and above-average rainfall throughoutlarge portions of Australia. Importantly, ENSO cyclesbetween its extreme phases with a period of betweenapproximately 3 and 7 years (McBride and Nicholls 1983;Corresponding author address: Ashish Sharma, School of Civiland Environmental Engineering, University of New South Wales,Sydney 2032, Australia.E-mail: 2012 P U I E T A L . 1665DOI: 10.1175/MWR-D-11-00238.1Ó 2012 American Meteorological Society
  2. 2. Drosdowsky 1993), although the predictability of in-dividual ENSO events generally does not extend muchbeyond 1 year (Barnston et al. 1999).The vast majority of the research described above hasfocused either on aggregate rainfall at seasonal timescales (e.g., McBride and Nicholls 1983; Drosdowsky1993; Ropelewski and Halpert 1987; Chiew et al. 1998;Risbey et al. 2009a), or on extremes (e.g., Cayan et al.1999; Gershunov and Barnett 1998; Liebmann et al. 2001,Pui et al. 2011). However, use of aggregated rainfall datacan result in significant loss of information about the in-dividual processes that make up interannual variability.For example, is an increase in spring average rainfallduring La Nin˜a events due to an increase in the numberof discrete precipitation events occurring, or is it due tomore intense rainfall occurring during each precipitationevent? Such information is not only useful for developinga better mechanistic understanding on the nature of in-terannual variability, but also has significant practicalimplications in areas such as agriculture (e.g., the numberof wet days per crop growing season, as well as the timebetween consecutive wet days) and flood estimation.ENSO attribution on daily time scales has alreadybeen the subject of several studies, which capture vari-ous facets of the multiscale rainfall variability issue. Forexample, Ropelewski and Bell (2008) focused on shiftsin fine temporal-scale rainfall statistics conditional toENSO in the South American continent, whereas Samueland Sivapalan (2008) analyzed the variability of inter- andintra-storm properties at three locations in Australia(Perth, Newcastle, and Darwin), which experience dif-ferent climatic regimes. Finally, Nicholls and Kariko(1993) investigated only daily rainfall characteristicssuch as intensities and number of wet days, at five lo-cations in east Australia that were influenced by theSouthern Oscillation.In this present study, we will build upon this earlierresearch, and address the following questions: 1) towhat extent are rainfall occurrences and the intensity ofrainfall per occurrence affected by these climate modes?2) Are these changes also reflected when consideringindividual storm events using the subdaily rain gaugedataset? 3) Do these changes fully account for observedvariability at the seasonal and longer time scale? To thisend we will use a much larger dataset of both daily andsubdaily precipitation sequences across east Australia.This region is selected because of the abundance of datacompared with other locations within Australia, togetherwith the strong association between rainfall and cli-mate modes as documented in earlier studies (e.g.,Nicholls 1997; Chiew et al. 1998).While ENSO is recognized as the leading source ofclimate variability in Australia, other climate modes areknown to have significant impact on regional rainfall(e.g., Risbey et al. 2009a; Hill et al. 2009). The influenceof various climate modes increases the complexity inthe attribution and thus prediction of seasonal rainfall,which is further exacerbated by complexities withinENSO itself (e.g., Westra and Sharma 2006). Thus,a second goal of this present study is to evaluate theimpact of other major climate modes on daily and sub-daily rainfall statistics. To this extent, we focus on theIndian Ocean dipole (IOD), which is of tropical origin,and the southern annular mode (SAM), of extratropicalorigin, both of which have significant impact over re-gional east Australia during austral winter and spring(see, e.g., Risbey et al. 2009a). The study presented heretherefore adds to the body of research in this area byextending the analysis to the finest subdaily time scales,encompassing ENSO and these large-scale climatemodes, using the long record of daily and subdailyrainfall datasets as the basis for the analysis.The remainder of the paper is structured as follows.In section 2, the data and methodology used to conductour analyses in this study are described. Section 3 is re-served for presenting results on the impact of ENSO ondaily and subdaily rainfall statistics. The effects of theIOD and the SAM, and their potential influence on theENSO–rainfall teleconnection, are explored in section4, and conclusions are presented in section 5.2. Data and methodologya. Rainfall dataRainfall data for this study comprises daily rainfalland subdaily (hourly) rainfall records. The daily rain-fall data is based on a set of high-quality rain gaugesthroughout Australia that were identified by Laveryet al. (1997). For the purpose of this study, only locationswith near-complete records (defined as less than 1%of days containing missing data) over the period from1920 and 1999 were used. A total of 88 high-qualitycontinuous daily rainfall records across Australia metthis criterion, and that the locations of the rainfall stationsprovide reasonable spatial coverage for east Australia.For subdaily rainfall data, a total of 34 rainfall stationsmaintained by the Australian Bureau of Meteorologywere used. The number of the stations used is less thanthat for the daily rainfall because of missing data ata number of stations. Our constraint in this case was toretain the longest possible records while ensuring lessthan 15% of the data was missing, with seasons wheredata was missing not considered in the analysis. As aresult, these available stations are sparsely distributedand concentrated over the southeastern region. Thesestations have an average record length spanning 54 yr1666 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  3. 3. from 1952 to 2005, with each record containing at least40 yr of data.b. Climate indicesTo examine the effect of the climate modes on thedaily and subdaily rainfall variables, we used a singleindex representative of the evolution of each mode. TheSouthern Oscillation index (SOI), defined as the anoma-lous pressure difference between Tahiti and Darwin, isused as an indicator for ENSO activity. As demonstratedby Risbey et al. (2009a), rainfall correlations across Aus-tralia are not sensitive to whether SST-based indices orSOI are used to represent ENSO variability. This is be-cause the SOI is highly correlated to the SST-based in-dices (Barnston et al. 1997). Indeed, our results remainlargely unaltered when the Nin˜o-3.4 index was used. Thedipole mode index (DMI; Saji et al. 1999), which is definedas the difference in SST anomaly between the western andeastern tropical Indian Ocean regions, is used to representthe evolution of the IOD (Rayner et al. 2003). The index ofFogt et al. (2009) based on the difference between nor-malized monthly zonal mean sea level pressure at 408 and658S is used to describe the evolution of the SAM.c. Multiscale rainfall variablesRainfall variables derived from the daily and subdailydata were selected to explain variations occurring atseasonal time scales. An ENSO event typically initiatesin late austral autumn, peaks in summer, and dissipatesin the autumn of the following year. While the timing ofENSO impacts on rainfall may vary spatially even in eastAustralia, we have primarily focused on variables thatwere computed from the winter [June–August (JJA)]and spring [September–November (SON)] seasons asthey coincide with the maximum impact period for themajority of stations in east Australia (Chiew et al. 1998).These seasons also coincide with the period of signifi-cant correlation between east Australian rainfall andthe IOD, as well as the SAM (e.g., Risbey et al. 2009a;Hendon et al. 2007), and are thus relevant for assessingthe interaction of impacts among the climate modes.Here, we derived three variables computed from thedaily rainfall data for each season:1) Rainfall total (from aggregated daily rainfall),2) Number of wet days, and3) Rainfall per wet day.From the subdaily rainfall data, we derived the followingvariables:4) Number of wet spells,5) Average wet spell length, and6) Rainfall intensity per wet spell.A wet day occurs when daily precipitation is equal toor exceeds 1 mm as defined by the Australian Bureauof Meteorology (BOM; available online at It isnoted that seasonal rainfall total is a function of bothnumber of wet days and rainfall intensity of each wet daywithin the season. The wet days are in turn attributedby the characteristics of a ‘‘wet spell’’ within each day. Acentral assumption pertaining to the subdaily variablesis the definition of a wet spell. Rainfall is often reportedas falling in ‘‘events’’ or ‘‘storms’’ whose beginning andend are defined by rainless intervals (dry spells) of afixed duration minimum interevent time (MIT), witha wide range of values previously adopted for the MITfrom 15 min to 24 h (Dunkerley 2008). In line withTsubo et al. (2005), a 3-h MIT was used here to dis-tinguish between separate rain events or wet spells toallow for a compromise between the independence ofrain events and intraevent variability in rain rates. Weconsidered this MIT to be appropriate in this casesince the focus of this study is not to obtain the mostaccurate storm separation threshold, but to perform acomparative analysis across a vast region with varyingclimatic regimes.d. Impact of climate modes on rainfall variablesTo evaluate the impact of the climate modes on thedaily and subdaily rainfall variables, a linear regressionanalysis is implemented. The regressed rainfall variablesonto the climate indices are expressed as percentagechange from the corresponding mean values in responseto one standard deviation of the climate index. The im-pact of interactions between the different climate modeson rainfall variables is evaluated using multiple linearregression. Furthermore, to isolate the influence of a cer-tain climate mode, a partial regression analysis is usedif two climate indices are found to be significantly cor-related with each other for a particular season. Thiswidely used technique (see, e.g., Risbey et al. 2009a; Caiet al. 2011) involves first removing the linear compo-nent of a given time series (e.g., SOI) from a time seriesof interest (e.g., DMI) and a rainfall variable beforeperforming regression between the two. While manyprevious studies involved the use of standard correla-tion methods, we have elected to apply linear regressionas it enables the ranking of each rainfall variable basedon sensitivity to rainfall through magnitude of percent-age change. This was used in preference to criteria basedon statistical significance, because practically useful orimportant relationships may only be marginally statis-tically significant, for instance due to a limited samplesize. Conversely, in the case of hydroclimatology re-search in particular, sensitivities or impacts arising fromMAY 2012 P U I E T A L . 1667
  4. 4. statistically significant relationships can be very smallsuch that they may be of limited practical value (seeClarke 2010). Nonetheless, statistical significance is stillreported to provide an indication of a statistically sig-nificant relationship.While the linear regression approach is useful, aspreviously adopted in various related studies (e.g.,Nicholls and Kariko 1993; Chiew et al. 1998; Risbeyet al. 2009a), it relies on the assumption of linearity inthe relationships between variables. This assumptionhas been found to be reasonable for aggregate sea-sonal rainfall (e.g., Westra and Sharma 2010). Further-more, since a comparison of correlations computed basedon the Pearson product moment agrees well with thenonparametric rank-based correlation performed inthis study, this suggests that our assumption of linearityis reasonable. As such, for succinctness, much of thediscussions in the next sections will be based with ref-erence to the La Nin˜a condition, since the linearity en-sures that changes as a function of El Nin˜o or La Nin˜a aresymmetric.To specifically examine the nonlinear nature of theENSO-driven rainfall impact, a composite analysis wasalso conducted. Meyers et al. (2007) conducted a care-ful study in the classification of years of El Nin˜o andLa Nin˜ a using a lagged empirical orthogonal functionanalysis. These ENSO years as identified by Meyerset al. (2007) have been adopted as a basis for our com-posite analysis. Here, the nonparametric Mann WhitneyU (MWU; Mann and Whitney 1947; Xu et al. 2003) andKolmogorov–Smirnov (K–S) tests (Smirnov 1948) areused. In this case, we use a one-tailed MWU test to inferwhether there is a significant shift in the probabilitydistribution of each rainfall statistic in a particular di-rection, at the 95% significance level. The K–S test onthe other hand uses the maximum absolute differencebetween the cumulative distribution curves from twosamples to test if the samples come from differentpopulations. Here, we use the K–S test to ascertain ifrainfall variables from the El Nin˜ o and La Nin˜ a dis-tributions are significantly different from each otherat the 95% level. In addition to hypothesis tests, wealso present density estimates of the rainfall variablessuch that notable differences, where present, can beidentified via visual inspection. As our study essentiallyconcerns interannual characteristics of daily/subdailyrainfall variables arising from the climate modes, lineartrends, although generally small over the entire periodof analysis, were removed from all variables prior to theabove analysis to avoid introducing biases to the corre-lations between variables. The best-fit estimation of lin-ear trends from all time series was done via the leastsquares method.3. Daily and subdaily rainfall variability linkedto ENSOResults from linear regression of the SOI onto thedaily rainfall variables show a significant increase inwinter and spring rainfall totals at nearly all stationsacross east Australia as associated with a positive changein SOI (i.e., a La Nin˜a condition; Figs. 1a,d). This find-ing is in agreement with previous studies (McBride andNicholls 1983; Kane 1997). However, it is particularlyinteresting that these changes in rainfall totals, on thecontinent scale, are found here to arise more predomi-nantly from changes in the number of wet days ratherthan rainfall intensity per wet day, as revealed in Figs.1b,c,e,f. Furthermore, regression analysis for the sub-daily variables reveals that changes in the number of wetspells are significant across many stations (Figs. 2a,d),compared to those for the duration and intensity of thewet spells that, on the other hand, show insignificantchanges (Figs. 2b,c,e,f). This suggests that most of thechanges to the rainfall totals appear to stem from changesin the number of occurring wet spells.Note that the direction of changes at nearly all of thestations is uniform across most of the domain beinganalyzed, with the exception of the eastern coastal fringewhere the changes are of a lower magnitude and gen-erally not statistically significant, although they are ofthe same sign (Figs. 1 and 2). This was also found inearlier work by Timbal (2010), who used this to high-light the additional difficulty in characterizing variabilityacross the east Australian coastline. Nevertheless, thisregion is only a small part of the study domain, andgiven the spatial homogeneity elsewhere, we presentthe region wide average percentage change for eachrainfall statistic in Table 1 to aid with interpretation ofthe results. The corresponding proportion of number ofstations that are statistically significant is also shownin Table 1 as an indication of field significance. Forcompleteness, we also present the results for australsummer and autumn. With respect to daily rainfall vari-ables, Table 1 shows a progressively stronger rainfallimpact into the spring season, with a 17% increase inrainfall per positive standard deviation change in SOI.It also reveals that changes to the seasonal rainfall totalare in general more apparently driven by changes to thenumber of wet days (11% increase) with a much higherproportion of stations returning statistical significance,as compared to the less pronounced changes and lowerfield significance in the rainfall per wet day statistic.Table 1 further shows that the only subdaily rainfallvariable to exhibit any significant change is the numberof wet spells across east Australia. It should be noted thatchanges to aggregated statistics such as total rainfall1668 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  5. 5. amount, number of wet days, and rainfall per wet dayderived using the subdaily rainfall data for the eastAustralian region were consistent with the same vari-ables computed using daily rainfall. This represents avalidation of the quality of the subdaily rainfall data,and suggests that the lower proportion of statistical sig-nificance may be explained simply by the shorter recordlengths and increased missing data.Based on these results, we suggest that the implica-tions of ENSO on multiscale rainfall change averagedover the entire east Australian region are in generaldriven by changes in the number of wet spells occurringwithin a day, rather than their length or intensity. Thus,these changes in turn explain the changes in the numberof wet days rather than rainfall per wet day. Regionalvariations exist whereby either or both the number ofFIG. 1. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation changein normalized seasonal SOI. Blue and red circles denote an increase and a decrease in daily rainfall, respectively; while filled circles denotethe statistical significance at the 95% significance level.MAY 2012 P U I E T A L . 1669
  6. 6. wet days and intensity appear important at a few sta-tions, possibly due to processes other than ENSO.Nonetheless, our results appear consistent with thoseof Nicholls and Kariko (1993) who found, using onlyfive daily-rainfall stations, that the Southern Oscilla-tion primarily influences the number of rain events,and to a lesser extent their intensity. Examination ofthe physical processes behind this differing impact onthe number and intensity of rain events is beyond thescope of our study. It is likely that, among other things,stochastic behavior of the air trajectory associated withrain-producing synoptic systems can play a role, by influ-encing available moisture relevant for rainfall formation(Brown et al. 2009).For the remainder of this section, we further examinethe ENSO-driven rainfall characteristics by focusing onFIG. 2. Changes for subdaily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviationchange in normalized seasonal SOI. Blue and red circles denote an increase and a decrease in daily rainfall, respectively; while filled circlesdenote the statistical significance at the 95% significance level.1670 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  7. 7. the composite analysis for the spring season (SON), dur-ing which the ENSO impact is maximum (see Table 1).As such, any notable changes as discussed above shouldemerge clearly in probability density plots and pass theMWU and/or K–S statistical tests (see section 2). Prob-ability density function (PDF) plots of mean-removed(centered) daily rainfall variables at each station con-ditional to El Nin˜o and La Nin˜a are presented in Fig. 3,with the corresponding bold curves indicating the eastAustralia–wide averaged response. It can be seen thatthe seasonal rainfall total during La Nin˜a years is highercompared to El Nin˜o years at most stations (Fig. 3a).These changes can be attributed more apparently to thenumber of wet days (Fig. 3b), as noted by the clearerseparation between the two clusters of probability dis-tributions for the El Nin˜o and La Nin˜a phases, comparedto those for the rainfall per wet day statistic (Fig. 3c). Asa validation statistic, we have introduced a new variable,the dry spell length defined as number of intervening drydays between distinct wet days. If there is an increase inthe number of wet days for a fixed time frame (season),it should logically follow that there will be a correspond-ing decrease in the length of dry periods, unless these wetdays are consecutive rather than interspersed. Figure 3dshows that while dry spell lengths are shorter duringLa Nin˜ a periods, the extent of the separation in thePDFs between the El Nin˜ o and La Nin˜ a phases is notas distinct as that of the number of wet days. This sug-gests that a fraction of the increase in the number ofwet days at some stations during La Nin˜ a is associatedwith episodes of consecutive wet days (see also Fig. 5).The contrasting probability distribution in the num-ber of wet days is also seen at the subdaily time scale.As shown in Fig. 4, the subdaily PDF plots show theclearest separation in the PDFs of number of wet spells,with an increased and decreased number of wet spellsduring the La Nin˜ a and El Nin˜ o phases, respectively(Fig. 4a). A curious result is the PDFs of rainfall in-tensity per wet spell (Fig. 4c), which are not only statis-tically insignificant between the opposing ENSO phases,but are also more skewed toward its mean. This result isconsistent with that of Samuel and Sivapalan (2008)who did not find sufficient evidence (by virtue of theStudent’s t and MWU tests) to conclude that averagestorm intensities differed between El Nin˜o and La Nin˜aphases in both Newcastle and Darwin, both of whichare in east Australia.It is worth noting that the composite PDFs revealLa Nin˜a phases as having fatter tails in both the totalrainfall amount and number of wet day statistics thanEl Nin˜o phases (Figs. 3a,b). This suggests a certain de-gree of nonlinearity in the ENSO response, and thatrainfall extremes during La Nin˜ a are more intenseand/or frequent. Nonetheless, it should be noted that theresults from linear regression analysis are still in quali-tative agreement with those of the PDF analysis.To conclude this section, we provide a continent-wideperspective of the ENSO influence by presenting thespatial distribution of the MWU and K–S tests for thedaily rainfall at each station. The MWU tests (at the 95%significance level) were set out with the null hypothesisthat each rainfall variable for El Nin˜o is not significantlydifferent from La Nin˜a phases. The corresponding alter-native hypotheses are as follows:1) Seasonal rainfall total during El Nin˜o is significantlylower than during La Nin˜a (one tailed).2) Number of wet days during El Nin˜o is significantlylower than during La Nin˜a (one tailed).3) Rainfall per wet day during El Nin˜o is significantlydifferent than during La Nin˜a (two tailed).4) Dry spell lengths during El Nin˜o are significantlylarger than during La Nin˜a (one tailed).The K–S tests were conducted on the same rainfallvariables to ascertain if their respective distributionswere significantly different at the 95% level. Figure 5shows the daily rainfall stations returning a p value ofless than 0.05 (i.e., significant; in red) for each rainfallvariable for the MWU (top row) and K–S (bottom row)tests, as well as statistically insignificant stations (in black).The MWU test results confirm that the seasonalrainfall amount at most stations is significantly differentTABLE 1. Average percentage difference corresponding to 11 standard deviation SOI and proportion of stations that are statisticallysignificant (in parentheses) for each rainfall variable of interest across 88 daily rainfall stations and 34 subdaily rainfall stations in eastAustralia for all seasons.SeasonDaily rainfall variables Subdaily rainfall variablesRainfall(mm)No. ofwet daysRainfallper wet dayNo. ofwet spellsWet spell length(h)Rainfall intensityper wet spell (mm h21)DJF 8.9 (0.28) 3.3 (0.17) 5.3 (0.24) 5.6 (0.24) 4.4 (0.15) 0.3 (0.03)MAM 14.4 (0.51) 8.6 (0.45) 5.9 (0.25) 11.3 (0.38) 2.2 (0.06) 1.8 (0.03)JJA 18.3 (0.76) 12.5 (0.68) 7.2 (0.31) 10.4 (0.50) 4.9 (0.21) 1.2 (0.03)SON 16.7 (0.78) 10.8 (0.86) 6.1 (0.33) 12.3 (0.58) 2.5 (0.09) 3.6 (0.12)MAY 2012 P U I E T A L . 1671
  8. 8. between El Nin˜o and La Nin˜a phases, except at a fewstations along the east coast where local processes in-cluding orographic effects are more likely to interferewith ENSO signals. The MWU test for the rest of thedaily rainfall variables shows the number of wet daysexhibiting widespread significance across the region, incontrast to the rainfall intensity and dry spell length,which exhibit apparent heterogeneity with a relativelylarge proportion of stations showing statistical insig-nificance. This again confirms the number of wet daysas the determining factor behind seasonal rainfall re-sponse to ENSO variability across east Australia, con-sistent with what was revealed from the regressionanalysis (Figs. 1d–f).The K–S tests show that there are significant shiftsin the PDFs of the seasonal rainfall total between theopposing ENSO phases. These shifts are most pro-nounced in the inland east region, with most of thestatistically insignificant stations located along theeast coast. The implication of the significance of thesetests for the inland region of east Australia is thatseasonal rainfall response to ENSO does not only in-volve shifts in the mean, but also the characteristicsof its probability distribution. However, note that theshifts in the PDFs of the underlying daily rainfallcharacteristics paint a more mixed picture than the shiftsin the mean, suggesting that they cannot be solely at-tributed to number of wet days. Rainfall per wet day asFIG. 3. Probability density function plots of daily rainfall variables conditioned to El Nin˜o (in red) and La Nin˜a (inblue) years. Thin lines denote PDFs of mean removed variables for each daily rainfall station, while the thick linesrepresent east Australia region averaged values for all stations. Over the period 1920–99, there are 14 and 27 yrclassified as El Nin˜o and La Nin˜a, respectively [see Meyers et al. (2007), their Table 2 for specification of the years].Thin lines denote PDFs of variable anomalies for each daily rainfall station, while the thick lines represent eastAustralia region averaged values for all stations.1672 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  9. 9. well as the temporal distribution of the wet days canalso play a role.Similar tests performed on subdaily rainfall variablesreveal that the changes to rainfall per wet day and thetemporal distribution of wet days appear to be solelyattributed to changes in the number of wet spells, withapproximately 71% (24/34) of subdaily rainfall stationsreturning a significant result for the MWU test (spatialplots not shown here). In particular, this result lendsfurther support to our argument that changes to rain-fall per wet day is caused by changes to the number ofwithin-day wet spells rather than changes to rainfallintensity.We next turn our attention to the contribution by theIOD and the SAM, and how they may interplay withENSO and/or exacerbate its impacts on east Australianrainfall documented thus far.4. Contribution by the IOD and the SAMa. IODThe linear regression analysis on daily rainfall for theIOD presented in Fig. 6 shows a decrease in seasonalrainfall that is fairly uniform across east Australia as-sociated with a positive one standard deviation DMI forthe winter and spring seasons. These differences, par-ticularly during spring, are driven more predominantlyby changes to the number of wet days rather than rain-fall per wet day (Fig. 6; see also Table 2). Previous studieshave noted the notably significant correlation betweenthe IOD and ENSO (e.g., Meyers et al. 2007; Risbey et al.2009a). The linear correlations during JJA and SONbetween the SOI and DMI indices over the time periodof our analysis (1920–99) are 20.45 and 20.60, respec-tively, both of which are statistically significant. TheFIG. 4. Probability density function plots of subdaily rainfall variables conditioned to El Nin˜o (in red) and La Nin˜a(in blue) years. Thin lines denote PDFs of mean removed variables for each subdaily rainfall station, while the thicklines represent east Australia region averaged values for all stations.MAY 2012 P U I E T A L . 1673
  10. 10. higher correlation in spring is expected, as the IODmatures during this season before it dissipates in sum-mer when ENSO peaks. Because of such substantialIOD–ENSO correlations, it is expected that the IODimpact on the daily rainfall statistics would be due partlyto co-occurrences with ENSO.The isolated effect of the IOD can be examined byremoving the component from the DMI time series thatis linearly correlated to ENSO, and then regressingit onto the time series of the rainfall variables in whichthe ENSO coherence has also been removed. Our re-sults are presented in Table 2 and the figures that follow.From Table 2 and Fig. 7, a partial IOD (i.e., after re-moving the effect of ENSO from the IOD) has a muchless significant impact on the daily rainfall variables dur-ing winter to spring. On the other hand, the partial re-gression on ENSO reveals that the impact of partialENSO (after removing the effect of IOD from ENSO)appears to be less altered from that without the IOD re-moved, particularly in JJA since their correlations areweaker (Tables 1 and 2; Figs. 1 and 8). The effect ofIOD on the ENSO impact is more notable in SON.Again, these effects generally arise from changes in thenumber of wet days rather than rainfall per wet day.These analyses suggest that ENSO appears to havestronger direct impact on rainfall over east Australiain winter than in spring by which time the interactionwith IOD becomes important. This can be explained asfollows. Direct impact of ENSO on northern Australianrainfall is via atmospheric pressure fluctuations as part ofthe Southern Oscillation. In addition, anomalous condi-tions in the Pacific associated with ENSO drive Rossbywave trains that propagate into the extratropics, influ-encing rainfall in the higher latitudes of east Australia(e.g., Hill et al. 2009; Cai et al. 2011). At the same time,ENSO tends to influence the evolution of the IOD andmodulate convection in the eastern and western IndianOcean that in turn drives poleward-propagating baro-tropic wave trains affecting rainfall across southernAustralia (Cai et al. 2011). Cai et al. (2011) showed thatFIG. 5. (top) MWU and (bottom) K–S test results for daily rainfall variables across the east Australian region. Red (black) filled circlesdenote statistically significant (not significant) results at the 95% level for both the MWU and K–S tests.1674 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  11. 11. during winter the wave trains associated with the IODemanate only from the eastern Indian Ocean, as ENSOtends to offset convection in the western counterpartduring the developing IOD. In spring, however, both thewestern and eastern Indian Ocean convective regions, inassociation with stronger ENSO–IOD coherence, con-tribute to stronger rainfall effects over the southern partsof the continent (Cai et al. 2011), by conditioning cutofflow systems that are an important source of rainfall inthe southeastern Australia (Risbey et al. 2009b). Inaddition, our results show that ENSO also impacts onrainfall intensity per wet day in the southern stations(Fig. 1f), and that this effect becomes apparent oncethe IOD effect is removed (Figs. 8d–f).b. SAMThe SAM impact in winter shows notable spatial var-iations (Fig. 9a). Phases of positive SAM as associatedFIG. 6. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation changein normalized seasonal DMI. Blue and red circles denote an increase and a decrease in daily rainfall, respectively; while filled circlesdenote the statistical significance at the 95% significance level.MAY 2012 P U I E T A L . 1675
  12. 12. with a poleward shift of the subpolar westerlies, corre-spond to anomalously dry conditions in the southernparts of the region, including Tasmania. Rainfall in-creases are, however, seen in the eastern coastal regions,which become more widespread and apparent in SON,in association with anomalous easterly flows that in-tensify in this season (Hendon et al. 2007). As the SAMwas not found to be significantly correlated with eitherIOD or ENSO during our period of analysis, partialregression analysis is not necessary and thus not per-formed here. Figure 9 shows that the maximum impactregion and season for SAM is the east coast region inSON. This is consistent with findings in other studiesthat found the east coast of Australia experienced thelargest increases during the positive SAM phase, whichmay be explained by anomalous easterly winds enhanc-ing moisture advection from the ocean and hence in-creased orographic rainfall in this region (Sen Guptaand England 2007; Gillett et al. 2006; Hendon et al. 2007).Conversely, the positive SAM also resulted in rainfallreductions in the southern part of the region especiallyduring the JJA period (Cai and Cowan 2006; Meneghiniet al. 2007). Previous studies have attributed this de-crease to the contraction of the westerly wind belt to-ward the poles (Hendon et al. 2007). However, despitethe mixed response in total rainfall occasioned by SAM,it transmits its influence in a manner consistent with theother climate modes above: that changes, where present,are more pronounced in number of wet days comparedto rainfall per wet day (Fig. 9).It should be noted that the way in which the IOD andthe SAM transmit their influence to subdaily rainfallvariables mirrored that of ENSO, with changes, wherepresent, almost exclusively limited to the occurrenceof subdaily wet spells (results not presented here).c. Interaction between IOD, SAM, and ENSOFor this section of analysis, we aim to test if inter-actions between the different climate modes resultedin enhanced effects on the three daily rainfall variablesdescribed above. A time series of yearly east Australianormalized rainfall, as well as the SOI, DMI, and theSAM index is presented in Fig. 10 as a visual aid. We firsttested for changes corresponding to positive one andnegative one standard deviation of SOI and DMI, re-spectively, using multiple linear regression (i.e., the com-bination that is expected to result in wetter conditions).We then paired positive one standard deviation ofSOI and SAM index (again, based on the direction ofchange in index that produced coherent impact onrainfall variables) before including all three climateindices as predictors into the model. The results aresummarized in Table 3, suggesting that the effect ofstratifying rainfall variables to multiple predictors didnot always translate to an additive effect to rainfallvariables. In particular, the ENSO–IOD pairing resultedin minimal increase in magnitude of changes comparedto ENSO alone. This result was not surprising consid-ering that they are correlated. With respect to the SOI–SAM pairing, rainfall during spring season increasedsignificantly, with up to 30% change in total seasonalrainfall amount that can be attributed to 18% increase innumber of wet days, and 12% increase in rainfall per wetday (Table 3). Note that the effects of the SAM andENSO are offsetting in the southern region during win-ter. However, when all three climate modes were in-cluded as predictors, no significant new informationcould be gleaned suggesting that interactions betweenthese modes resulted in only minor changes to the dy-namics of rainfall in east Australia.5. ConclusionsIn this study, we have explored daily to subdaily rain-fall variability at several sites across east Australia. Themain findings from our analyses can be summarized asfollows:1) Changes to seasonal rainfall amounts associated withENSO variability over east Australia as a whole,particularly during the cool seasons, can be primarilyattributed to changes in the number of wet days andTABLE 2. Average percentage difference corresponding to 11 standard deviation of DMI, SAM in isolation, as well as that of partialSOI and partial DMI, and the proportion of stations that are statistically significant (in parentheses) for each rainfall variable of interestacross 88 daily rainfall stations.Season Variable 11 std dev DMI 11 std dev SAM 11 std dev partial SOI 11 std dev partial DMIJJA Rainfall tot 28.7 (0.39) 20.3 (0.34) 17.9 (0.67) 21.8 (0.00)No. of wet days 24.7 (0.22) 0.2 (0.53) 12.6 (0.56) 0.2 (0.03)Rainfall per wet day 24.3 (0.18) 20.6 (0.13) 5.8 (0.18) 22.0 (0.03)SON Rainfall tot 210.9 (0.46) 12.7 (0.53) 17.0 (0.58) 21.9 (0.08)No. of wet days 27.8 (0.52) 7.7 (0.58) 10.3 (0.48) 22.4 (0.09)Rainfall per wet day 22.9 (0.18) 5.2 (0.28) 7.1 (0.25) 1.1 (0.07)1676 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  13. 13. to a lesser extent, rainfall per wet day. Analysis ofsubdaily rainfall data has further established thatchanges to rainfall per wet day are in fact caused bychanges to the number of wet spells per day, and notrainfall intensity per wet spell suggesting that it is theprobability of rainfall occurrence, rather than thecharacter of rainfall given that it has occurred, whichis the dominant mechanism driving seasonal rainfallchanges.2) The impacts of ENSO and IOD in combination arefairly spatially homogenous on the continent scale.Spatial variations do nonetheless exist, with ENSOand IOD more strongly impacting the inland east andsoutheast regions, respectively. The SAM’s influ-ence on rainfall is strongest over the east coast regionduring SON, but is associated with a more mixedrainfall response during JJA, with the southern partof the region experiencing opposite effects to theFIG. 7. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 1 1 standard deviation changein normalized seasonal DMI with the SOI signal removed. Blue and red circles denote an increase and a decrease in daily rainfall,respectively; while filled circles denote the statistical significance at the 95% significance level.MAY 2012 P U I E T A L . 1677
  14. 14. northern part. Despite having different maximumimpact regions and seasons, the manner in whichIOD and SAM impact on rainfall is somewhat similarto that of ENSO, in that changes to the number ofwet days is overall more significant than rainfall perwet day.3) ENSO has been found to be the primary driver ofclimate variability over east Australia during the longperiod of analysis (1920–99). While combination ofthe modes did occasionally produce additive effectssuch as the ENSO–SAM in SON, we found signif-icant redundancy in the effects of climate modes incombination perhaps as a result of significant cor-relation with each other (such as the ENSO–IODpairing).Importantly, we have shown that insight into sub-daily rainfall characteristics may help explain changesFIG. 8. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation changein normalized seasonal SOI with the DMI signal removed. Blue and red circles denote an increase and a decrease in daily rainfall,respectively; while filled circles denote the statistical significance at the 95% significance level.1678 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
  15. 15. occurring at coarser time steps as well as assist withmaking some initial inferences into the physical mech-anisms behind climate-mode-driven rainfall. Becauserainfall intensity per wet spell remains fairly constantunder the influence of climate modes, we suggest thatthe nature of the rainfall generating mechanism (i.e.,stratiform vs convective) does not differ significantly asa result of climate mode influence. Rather, it is the fre-quency of rainfall events that are subject to change. It isinteresting that teleconnections appear to influence theprobability rather than the intensity of events when theyoccur. The processes by which each of the modes trans-mits their influence on rainfall characteristics warrantfurther investigation.It should be noted that the influence of climate modeson rainfall characteristics over east Australia, and therelationships between the climate modes, can be mod-ulated by lower-frequency modes such as the IPO (e.g.,FIG. 9. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation changein normalized seasonal SAM. Blue and red circles denote an increase and a decrease in daily rainfall, respectively; while filled circlesdenote the statistical significance at the 95% significance level.MAY 2012 P U I E T A L . 1679
  16. 16. Cai et al. 2010; Kiem et al. 2003), as well as the warmingtrend. Such a modulation effect is not investigated inour study as the long record analyzed here captures bothphases of the IPO, and as such biases due to the recordsrepresenting a single phase alone are not expected. Ouranalysis is, however, useful for inferring a generalizedrole of the climate modes on rainfall characteristics.The effects of the IPO and long-term trends shouldnonetheless be investigated in future studies, in particu-lar, concerning the possibility that the climate modesmay influence finescale rainfall characteristics in a dif-ferent way in a warming climate.Finally, we wish to note a number of practical impli-cations of the results presented here. The overridingimportance of ENSO as the primary driver of rainfallvariability in east Australia was emphasized, with per-centage changes between around 16%–18% per stan-dard deviation of the SOI during the winter and springseasons, and slightly lower but also reasonably signifi-cant increases for the remaining seasons; this highlightsthat this mode of variability can have profound impli-cations on flood risk and water resource availability.This is particularly the case in regions away from theeastern coastal fringe. The implications on flood risk aretherefore less likely to be due to changes in the intensityof the flood-producing rainfall event during ENSO, witha related study recently also showing only a 3.2% and4.6% change in the intensity of the annual maximumrainfall event pre standard deviation of the SOI forsubdaily and daily rainfall, respectively (Westra andSisson 2011). Rather, the increased number of wet spellsduring La Nin˜a periods suggests that it is likely to be thecatchment wetness (also referred to as the ‘‘antecedentmoisture’’) prior to the rainfall-producing flood eventthat is most likely to increase, highlighting that the jointprobability of extreme rainfall and antecedent condi-tions need to be modeled in order to properly capturethe implications of ENSO on flood risk (Kuczera et al.2006). Analogous implications can be seen for other wa-ter resources applications, with infiltration propertiesand evaporation losses likely to be different for a largernumber of less intense events compared with a smallernumber of more intense events.In conclusion, while the stochasticity of the climatesystem prevents exact sequence prediction of weatherevents within a particular season, we have shown thatit is possible to obtain useful information on shiftsin the important rainfall statistics using simple tech-niques, even if there is still variability that remainsunexplained.FIG. 10. Time series of normalized yearly east Australia aggregated rainfall, along withnormalized climate indices of SOI (blue), DMI (red), and SAM (green), respectively. The bluecircles are attached to the SOI indices when the magnitude of rainfall anomaly was greater thanone standard deviation. It is useful to note that the El Nin˜o years encompass 1923, 1925, 1930,1940, 1941, 1957, 1963, 1965, 1972, 1982, 1986, 1987, 1991, and 1997; and La Nin˜a years en-compass 1922, 1924, 1926, 1928, 1933, 1935, 1938, 1942, 1944, 1945, 1946, 1949, 1950, 1954, 1955,1961, 1964, 1967, 1970, 1971, 1973, 1978, 1981, 1983, 1988, 1996, and 1998.TABLE 3. Average percentage difference corresponding to the co-occurrence of different climate modes using multiple linear regressionand proportion of stations that are statistically significant (in parentheses) for each rainfall variable of interest across 88 daily rainfallstations. Note use of 21 standard deviation DMI to highlight positive change in rainfall amount occasioned by 11 standard deviation SOI.Season Variable 11 SOI, 21 DMI 11 SOI, 11 SAM 11 SOI, 21 DMI, 11 SAMJJA Rainfall tot 18.4 (0.74) 20.3 (0.86) 20.3 (0.80)No. of wet days 11.2 (0.56) 13.0 (0.84) 10.0 (0.83)Rainfall per wet day 8.4 (0.32) 7.5 (0.42) 6.4 (0.32)SON Rainfall tot 16.7 (0.72) 29.7 (0.93) 29.2 (0.92)No. of wet days 10.9 (0.66) 18.4 (0.86) 17.7 (0.83)Rainfall per wet day 6.0 (0.26) 11.8 (0.49) 10.5 (0.43)1680 M O N T H L Y W E A T H E R R E V I E W VOLUME 140
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