Impact of the El Nino–Southern Oscillation, Indian Ocean Dipole, andSouthern Annular Mode on Daily to Subdaily Rainfall Characteristics

in East Australia

ALEXANDER PUI AND ASHISH SHARMA

School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia

AGUS SANTOSO

Climate Change Research Centre, University of New South Wales, Sydney, Australia

SETH WESTRA

School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia

(Manuscript received 22 July 2011, in final form 10 December 2011)

ABSTRACT

The relationship between seasonal aggregate rainfall and large-scale climate modes, particularly the

El Nino–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 of

each of these climate modes. This study investigates the change in rainfall occurrence, intensity, and storm

interevent time at both daily and subdaily time scales 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 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 of

rainfall 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 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

farther inland during the winter and spring seasons. These results have important implications for how water

resources are managed, as well as how the implications of large-scale climate modes are included in rainfall

models to best capture interannual and longer-scale variability.

1. Introduction

Understanding rainfall variability at interannual and

longer time scales is an important area of study with

many practical implications for how we manage our

water resources. This variability, which is often linked

to both internally and externally forced fluctuations in

the global sea surface temperature (SST) field (Bigg

et al. 2003; Westra and Sharma, 2010), can often affect

the weather patterns of entire continents via a set of

large-scale pressure and circulation anomalies known

as teleconnections that transmit these anomalies across

large distances (e.g., Barnston and Livezey 1987).

In Australia, much of the early research into climate

variability focused on the El Nino–Southern Oscillation

(ENSO) phenomenon, which has been linked to pre-

cipitation anomalies across Australia, particularly over

the eastern third of the continent (Pittock 1975; McBride

and Nicholls 1983; Drosdowsky 1993; Chiew et al. 1998).

This phenomenon is usually described in terms of cou-

pled oceanic and atmospheric variability centered on

the central and eastern tropical Pacific Ocean, with the

extreme phases of El Nino and La Nina typically re-

sulting in below- and above-average rainfall throughout

large portions of Australia. Importantly, ENSO cycles

between its extreme phases with a period of between

approximately 3 and 7 years (McBride and Nicholls 1983;

Corresponding author address: Ashish Sharma, School of Civil

and Environmental Engineering, University of New South Wales,

Sydney 2032, Australia.

E-mail: a.sharma@unsw.edu.au

MAY 2012 P U I E T A L . 1665

DOI: 10.1175/MWR-D-11-00238.1

� 2012 American Meteorological Society

Drosdowsky 1993), although the predictability of in-

dividual ENSO events generally does not extend much

beyond 1 year (Barnston et al. 1999).

The vast majority of the research described above has

focused either on aggregate rainfall at seasonal time

scales (e.g., McBride and Nicholls 1983; Drosdowsky

1993; 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 data

can result in significant loss of information about the in-

dividual processes that make up interannual variability.

For example, is an increase in spring average rainfall

during La Nina events due to an increase in the number

of discrete precipitation events occurring, or is it due to

more intense rainfall occurring during each precipitation

event? Such information is not only useful for developing

a better mechanistic understanding on the nature of in-

terannual variability, but also has significant practical

implications in areas such as agriculture (e.g., the number

of wet days per crop growing season, as well as the time

between consecutive wet days) and flood estimation.

ENSO attribution on daily time scales has already

been the subject of several studies, which capture vari-

ous facets of the multiscale rainfall variability issue. For

example, Ropelewski and Bell (2008) focused on shifts

in fine temporal-scale rainfall statistics conditional to

ENSO in the South American continent, whereas Samuel

and Sivapalan (2008) analyzed the variability of inter- and

intra-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 characteristics

such as intensities and number of wet days, at five lo-

cations in east Australia that were influenced by the

Southern Oscillation.

In this present study, we will build upon this earlier

research, and address the following questions: 1) to

what extent are rainfall occurrences and the intensity of

rainfall per occurrence affected by these climate modes?

2) Are these changes also reflected when considering

individual storm events using the subdaily rain gauge

dataset? 3) Do these changes fully account for observed

variability at the seasonal and longer time scale? To this

end we will use a much larger dataset of both daily and

subdaily precipitation sequences across east Australia.

This region is selected because of the abundance of data

compared with other locations within Australia, together

with 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 of

climate variability in Australia, other climate modes are

known to have significant impact on regional rainfall

(e.g., Risbey et al. 2009a; Hill et al. 2009). The influence

of various climate modes increases the complexity in

the attribution and thus prediction of seasonal rainfall,

which is further exacerbated by complexities within

ENSO itself (e.g., Westra and Sharma 2006). Thus,

a second goal of this present study is to evaluate the

impact of other major climate modes on daily and sub-

daily rainfall statistics. To this extent, we focus on the

Indian Ocean dipole (IOD), which is of tropical origin,

and the southern annular mode (SAM), of extratropical

origin, 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 here

therefore adds to the body of research in this area by

extending the analysis to the finest subdaily time scales,

encompassing ENSO and these large-scale climate

modes, using the long record of daily and subdaily

rainfall 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 conduct

our analyses in this study are described. Section 3 is re-

served for presenting results on the impact of ENSO on

daily and subdaily rainfall statistics. The effects of the

IOD and the SAM, and their potential influence on the

ENSO–rainfall teleconnection, are explored in section

4, and conclusions are presented in section 5.

2. Data and methodology

a. Rainfall data

Rainfall data for this study comprises daily rainfall

and subdaily (hourly) rainfall records. The daily rain-

fall data is based on a set of high-quality rain gauges

throughout Australia that were identified by Lavery

et al. (1997). For the purpose of this study, only locations

with near-complete records (defined as less than 1%

of days containing missing data) over the period from

1920 and 1999 were used. A total of 88 high-quality

continuous daily rainfall records across Australia met

this criterion, and that the locations of the rainfall stations

provide reasonable spatial coverage for east Australia.

For subdaily rainfall data, a total of 34 rainfall stations

maintained by the Australian Bureau of Meteorology

were used. The number of the stations used is less than

that for the daily rainfall because of missing data at

a number of stations. Our constraint in this case was to

retain the longest possible records while ensuring less

than 15% of the data was missing, with seasons where

data was missing not considered in the analysis. As a

result, these available stations are sparsely distributed

and concentrated over the southeastern region. These

stations have an average record length spanning 54 yr

1666 M O N T H L Y W E A T H E R R E V I E W VOLUME 140

from 1952 to 2005, with each record containing at least

40 yr of data.

b. Climate indices

To examine the effect of the climate modes on the

daily and subdaily rainfall variables, we used a single

index representative of the evolution of each mode. The

Southern Oscillation index (SOI), defined as the anoma-

lous pressure difference between Tahiti and Darwin, is

used as an indicator for ENSO activity. As demonstrated

by Risbey et al. (2009a), rainfall correlations across Aus-

tralia are not sensitive to whether SST-based indices or

SOI 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 remain

largely unaltered when the Nino-3.4 index was used. The

dipole mode index (DMI; Saji et al. 1999), which is defined

as the difference in SST anomaly between the western and

eastern tropical Indian Ocean regions, is used to represent

the evolution of the IOD (Rayner et al. 2003). The index of

Fogt et al. (2009) based on the difference between nor-

malized monthly zonal mean sea level pressure at 408 and

658S is used to describe the evolution of the SAM.

c. Multiscale rainfall variables

Rainfall variables derived from the daily and subdaily

data were selected to explain variations occurring at

seasonal time scales. An ENSO event typically initiates

in late austral autumn, peaks in summer, and dissipates

in the autumn of the following year. While the timing of

ENSO impacts on rainfall may vary spatially even in east

Australia, we have primarily focused on variables that

were computed from the winter [June–August (JJA)]

and spring [September–November (SON)] seasons as

they coincide with the maximum impact period for the

majority of stations in east Australia (Chiew et al. 1998).

These seasons also coincide with the period of signifi-

cant correlation between east Australian rainfall and

the IOD, as well as the SAM (e.g., Risbey et al. 2009a;

Hendon et al. 2007), and are thus relevant for assessing

the interaction of impacts among the climate modes.

Here, we derived three variables computed from the

daily rainfall data for each season:

1) Rainfall total (from aggregated daily rainfall),

2) Number of wet days, and

3) Rainfall per wet day.

From the subdaily rainfall data, we derived the following

variables:

4) Number of wet spells,

5) Average wet spell length, and

6) Rainfall intensity per wet spell.

A wet day occurs when daily precipitation is equal to

or exceeds 1 mm as defined by the Australian Bureau

of Meteorology (BOM; available online at http://www.

bom.gov.au/climate/change/about/extremes.shtml). It is

noted that seasonal rainfall total is a function of both

number of wet days and rainfall intensity of each wet day

within the season. The wet days are in turn attributed

by the characteristics of a ‘‘wet spell’’ within each day. A

central assumption pertaining to the subdaily variables

is the definition of a wet spell. Rainfall is often reported

as falling in ‘‘events’’ or ‘‘storms’’ whose beginning and

end are defined by rainless intervals (dry spells) of a

fixed duration minimum interevent time (MIT), with

a wide range of values previously adopted for the MIT

from 15 min to 24 h (Dunkerley 2008). In line with

Tsubo et al. (2005), a 3-h MIT was used here to dis-

tinguish between separate rain events or wet spells to

allow for a compromise between the independence of

rain events and intraevent variability in rain rates. We

considered this MIT to be appropriate in this case

since the focus of this study is not to obtain the most

accurate storm separation threshold, but to perform a

comparative analysis across a vast region with varying

climatic regimes.

d. Impact of climate modes on rainfall variables

To evaluate the impact of the climate modes on the

daily and subdaily rainfall variables, a linear regression

analysis is implemented. The regressed rainfall variables

onto the climate indices are expressed as percentage

change from the corresponding mean values in response

to one standard deviation of the climate index. The im-

pact of interactions between the different climate modes

on rainfall variables is evaluated using multiple linear

regression. Furthermore, to isolate the influence of a cer-

tain climate mode, a partial regression analysis is used

if two climate indices are found to be significantly cor-

related with each other for a particular season. This

widely used technique (see, e.g., Risbey et al. 2009a; Cai

et al. 2011) involves first removing the linear compo-

nent of a given time series (e.g., SOI) from a time series

of interest (e.g., DMI) and a rainfall variable before

performing regression between the two. While many

previous studies involved the use of standard correla-

tion methods, we have elected to apply linear regression

as it enables the ranking of each rainfall variable based

on sensitivity to rainfall through magnitude of percent-

age change. This was used in preference to criteria based

on statistical significance, because practically useful or

important relationships may only be marginally statis-

tically significant, for instance due to a limited sample

size. Conversely, in the case of hydroclimatology re-

search in particular, sensitivities or impacts arising from

MAY 2012 P U I E T A L . 1667

statistically significant relationships can be very small

such that they may be of limited practical value (see

Clarke 2010). Nonetheless, statistical significance is still

reported to provide an indication of a statistically sig-

nificant relationship.

While the linear regression approach is useful, as

previously adopted in various related studies (e.g.,

Nicholls and Kariko 1993; Chiew et al. 1998; Risbey

et al. 2009a), it relies on the assumption of linearity in

the relationships between variables. This assumption

has been found to be reasonable for aggregate sea-

sonal rainfall (e.g., Westra and Sharma 2010). Further-

more, since a comparison of correlations computed based

on the Pearson product moment agrees well with the

nonparametric rank-based correlation performed in

this study, this suggests that our assumption of linearity

is reasonable. As such, for succinctness, much of the

discussions in the next sections will be based with ref-

erence to the La Nina condition, since the linearity en-

sures that changes as a function of El Nino or La Nina are

symmetric.

To specifically examine the nonlinear nature of the

ENSO-driven rainfall impact, a composite analysis was

also conducted. Meyers et al. (2007) conducted a care-

ful study in the classification of years of El Nino and

La Nina using a lagged empirical orthogonal function

analysis. These ENSO years as identified by Meyers

et al. (2007) have been adopted as a basis for our com-

posite analysis. Here, the nonparametric Mann Whitney

U (MWU; Mann and Whitney 1947; Xu et al. 2003) and

Kolmogorov–Smirnov (K–S) tests (Smirnov 1948) are

used. In this case, we use a one-tailed MWU test to infer

whether there is a significant shift in the probability

distribution of each rainfall statistic in a particular di-

rection, at the 95% significance level. The K–S test on

the other hand uses the maximum absolute difference

between the cumulative distribution curves from two

samples to test if the samples come from different

populations. Here, we use the K–S test to ascertain if

rainfall variables from the El Nino and La Nina dis-

tributions are significantly different from each other

at the 95% level. In addition to hypothesis tests, we

also present density estimates of the rainfall variables

such that notable differences, where present, can be

identified via visual inspection. As our study essentially

concerns interannual characteristics of daily/subdaily

rainfall variables arising from the climate modes, linear

trends, although generally small over the entire period

of analysis, were removed from all variables prior to the

above 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 least

squares method.

3. Daily and subdaily rainfall variability linkedto ENSO

Results from linear regression of the SOI onto the

daily rainfall variables show a significant increase in

winter and spring rainfall totals at nearly all stations

across east Australia as associated with a positive change

in SOI (i.e., a La Nina condition; Figs. 1a,d). This find-

ing is in agreement with previous studies (McBride and

Nicholls 1983; Kane 1997). However, it is particularly

interesting that these changes in rainfall totals, on the

continent scale, are found here to arise more predomi-

nantly from changes in the number of wet days rather

than 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 wet

spells are significant across many stations (Figs. 2a,d),

compared to those for the duration and intensity of the

wet spells that, on the other hand, show insignificant

changes (Figs. 2b,c,e,f). This suggests that most of the

changes to the rainfall totals appear to stem from changes

in the number of occurring wet spells.

Note that the direction of changes at nearly all of the

stations is uniform across most of the domain being

analyzed, with the exception of the eastern coastal fringe

where the changes are of a lower magnitude and gen-

erally not statistically significant, although they are of

the same sign (Figs. 1 and 2). This was also found in

earlier work by Timbal (2010), who used this to high-

light the additional difficulty in characterizing variability

across the east Australian coastline. Nevertheless, this

region is only a small part of the study domain, and

given the spatial homogeneity elsewhere, we present

the region wide average percentage change for each

rainfall statistic in Table 1 to aid with interpretation of

the results. The corresponding proportion of number of

stations that are statistically significant is also shown

in Table 1 as an indication of field significance. For

completeness, we also present the results for austral

summer and autumn. With respect to daily rainfall vari-

ables, Table 1 shows a progressively stronger rainfall

impact into the spring season, with a 17% increase in

rainfall per positive standard deviation change in SOI.

It also reveals that changes to the seasonal rainfall total

are in general more apparently driven by changes to the

number of wet days (11% increase) with a much higher

proportion of stations returning statistical significance,

as compared to the less pronounced changes and lower

field significance in the rainfall per wet day statistic.

Table 1 further shows that the only subdaily rainfall

variable to exhibit any significant change is the number

of wet spells across east Australia. It should be noted that

changes to aggregated statistics such as total rainfall

1668 M O N T H L Y W E A T H E R R E V I E W VOLUME 140

amount, number of wet days, and rainfall per wet day

derived using the subdaily rainfall data for the east

Australian region were consistent with the same vari-

ables computed using daily rainfall. This represents a

validation 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 record

lengths and increased missing data.

Based on these results, we suggest that the implica-

tions of ENSO on multiscale rainfall change averaged

over the entire east Australian region are in general

driven by changes in the number of wet spells occurring

within a day, rather than their length or intensity. Thus,

these changes in turn explain the changes in the number

of wet days rather than rainfall per wet day. Regional

variations exist whereby either or both the number of

FIG. 1. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation change

in normalized seasonal SOI. 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 . 1669

wet days and intensity appear important at a few sta-

tions, possibly due to processes other than ENSO.

Nonetheless, our results appear consistent with those

of Nicholls and Kariko (1993) who found, using only

five daily-rainfall stations, that the Southern Oscilla-

tion primarily influences the number of rain events,

and to a lesser extent their intensity. Examination of

the physical processes behind this differing impact on

the number and intensity of rain events is beyond the

scope of our study. It is likely that, among other things,

stochastic behavior of the air trajectory associated with

rain-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 examine

the ENSO-driven rainfall characteristics by focusing on

FIG. 2. Changes for subdaily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation

change in normalized seasonal SOI. 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.

1670 M O N T H L Y W E A T H E R R E V I E W VOLUME 140

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 should

emerge clearly in probability density plots and pass the

MWU 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 Nino and La Nina are presented in Fig. 3,

with the corresponding bold curves indicating the east

Australia–wide averaged response. It can be seen that

the seasonal rainfall total during La Nina years is higher

compared to El Nino years at most stations (Fig. 3a).

These changes can be attributed more apparently to the

number of wet days (Fig. 3b), as noted by the clearer

separation between the two clusters of probability dis-

tributions for the El Nino and La Nina phases, compared

to those for the rainfall per wet day statistic (Fig. 3c). As

a validation statistic, we have introduced a new variable,

the dry spell length defined as number of intervening dry

days between distinct wet days. If there is an increase in

the 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 wet

days are consecutive rather than interspersed. Figure 3d

shows that while dry spell lengths are shorter during

La Nina periods, the extent of the separation in the

PDFs between the El Nino and La Nina phases is not

as distinct as that of the number of wet days. This sug-

gests that a fraction of the increase in the number of

wet days at some stations during La Nina is associated

with 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 the

clearest separation in the PDFs of number of wet spells,

with an increased and decreased number of wet spells

during the La Nina and El Nino 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 is

consistent with that of Samuel and Sivapalan (2008)

who did not find sufficient evidence (by virtue of the

Student’s t and MWU tests) to conclude that average

storm intensities differed between El Nino and La Nina

phases in both Newcastle and Darwin, both of which

are in east Australia.

It is worth noting that the composite PDFs reveal

La Nina phases as having fatter tails in both the total

rainfall amount and number of wet day statistics than

El Nino phases (Figs. 3a,b). This suggests a certain de-

gree of nonlinearity in the ENSO response, and that

rainfall extremes during La Nina are more intense

and/or frequent. Nonetheless, it should be noted that the

results from linear regression analysis are still in quali-

tative agreement with those of the PDF analysis.

To conclude this section, we provide a continent-wide

perspective of the ENSO influence by presenting the

spatial distribution of the MWU and K–S tests for the

daily rainfall at each station. The MWU tests (at the 95%

significance level) were set out with the null hypothesis

that each rainfall variable for El Nino is not significantly

different from La Nina phases. The corresponding alter-

native hypotheses are as follows:

1) Seasonal rainfall total during El Nino is significantly

lower than during La Nina (one tailed).

2) Number of wet days during El Nino is significantly

lower than during La Nina (one tailed).

3) Rainfall per wet day during El Nino is significantly

different than during La Nina (two tailed).

4) Dry spell lengths during El Nino are significantly

larger than during La Nina (one tailed).

The K–S tests were conducted on the same rainfall

variables to ascertain if their respective distributions

were significantly different at the 95% level. Figure 5

shows the daily rainfall stations returning a p value of

less than 0.05 (i.e., significant; in red) for each rainfall

variable 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 seasonal

rainfall amount at most stations is significantly different

TABLE 1. Average percentage difference corresponding to 11 standard deviation SOI and proportion of stations that are statistically

significant (in parentheses) for each rainfall variable of interest across 88 daily rainfall stations and 34 subdaily rainfall stations in east

Australia for all seasons.

Season

Daily rainfall variables Subdaily rainfall variables

Rainfall

(mm)

No. of

wet days

Rainfall

per wet day

No. of

wet spells

Wet spell length

(h)

Rainfall intensity

per 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

between El Nino and La Nina phases, except at a few

stations along the east coast where local processes in-

cluding orographic effects are more likely to interfere

with ENSO signals. The MWU test for the rest of the

daily rainfall variables shows the number of wet days

exhibiting widespread significance across the region, in

contrast to the rainfall intensity and dry spell length,

which exhibit apparent heterogeneity with a relatively

large proportion of stations showing statistical insig-

nificance. This again confirms the number of wet days

as the determining factor behind seasonal rainfall re-

sponse to ENSO variability across east Australia, con-

sistent with what was revealed from the regression

analysis (Figs. 1d–f).

The K–S tests show that there are significant shifts

in the PDFs of the seasonal rainfall total between the

opposing ENSO phases. These shifts are most pro-

nounced in the inland east region, with most of the

statistically insignificant stations located along the

east coast. The implication of the significance of these

tests for the inland region of east Australia is that

seasonal rainfall response to ENSO does not only in-

volve shifts in the mean, but also the characteristics

of its probability distribution. However, note that the

shifts in the PDFs of the underlying daily rainfall

characteristics paint a more mixed picture than the shifts

in the mean, suggesting that they cannot be solely at-

tributed to number of wet days. Rainfall per wet day as

FIG. 3. Probability density function plots of daily rainfall variables conditioned to El Nino (in red) and La Nina (in

blue) years. Thin lines denote PDFs of mean removed variables for each daily rainfall station, while the thick lines

represent east Australia region averaged values for all stations. Over the period 1920–99, there are 14 and 27 yr

classified as El Nino and La Nina, 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 east

Australia 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

well as the temporal distribution of the wet days can

also play a role.

Similar tests performed on subdaily rainfall variables

reveal that the changes to rainfall per wet day and the

temporal distribution of wet days appear to be solely

attributed to changes in the number of wet spells, with

approximately 71% (24/34) of subdaily rainfall stations

returning a significant result for the MWU test (spatial

plots not shown here). In particular, this result lends

further support to our argument that changes to rain-

fall per wet day is caused by changes to the number of

within-day wet spells rather than changes to rainfall

intensity.

We next turn our attention to the contribution by the

IOD and the SAM, and how they may interplay with

ENSO and/or exacerbate its impacts on east Australian

rainfall documented thus far.

4. Contribution by the IOD and the SAM

a. IOD

The linear regression analysis on daily rainfall for the

IOD presented in Fig. 6 shows a decrease in seasonal

rainfall that is fairly uniform across east Australia as-

sociated with a positive one standard deviation DMI for

the winter and spring seasons. These differences, par-

ticularly during spring, are driven more predominantly

by changes to the number of wet days rather than rain-

fall per wet day (Fig. 6; see also Table 2). Previous studies

have noted the notably significant correlation between

the IOD and ENSO (e.g., Meyers et al. 2007; Risbey et al.

2009a). The linear correlations during JJA and SON

between the SOI and DMI indices over the time period

of our analysis (1920–99) are 20.45 and 20.60, respec-

tively, both of which are statistically significant. The

FIG. 4. Probability density function plots of subdaily rainfall variables conditioned to El Nino (in red) and La Nina

(in blue) years. Thin lines denote PDFs of mean removed variables for each subdaily rainfall station, while the thick

lines represent east Australia region averaged values for all stations.

MAY 2012 P U I E T A L . 1673

higher correlation in spring is expected, as the IOD

matures during this season before it dissipates in sum-

mer when ENSO peaks. Because of such substantial

IOD–ENSO correlations, it is expected that the IOD

impact on the daily rainfall statistics would be due partly

to co-occurrences with ENSO.

The isolated effect of the IOD can be examined by

removing the component from the DMI time series that

is linearly correlated to ENSO, and then regressing

it onto the time series of the rainfall variables in which

the 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 much

less 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 partial

ENSO (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 are

weaker (Tables 1 and 2; Figs. 1 and 8). The effect of

IOD on the ENSO impact is more notable in SON.

Again, these effects generally arise from changes in the

number of wet days rather than rainfall per wet day.

These analyses suggest that ENSO appears to have

stronger direct impact on rainfall over east Australia

in winter than in spring by which time the interaction

with IOD becomes important. This can be explained as

follows. Direct impact of ENSO on northern Australian

rainfall is via atmospheric pressure fluctuations as part of

the Southern Oscillation. In addition, anomalous condi-

tions in the Pacific associated with ENSO drive Rossby

wave 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 and

modulate convection in the eastern and western Indian

Ocean that in turn drives poleward-propagating baro-

tropic wave trains affecting rainfall across southern

Australia (Cai et al. 2011). Cai et al. (2011) showed that

FIG. 5. (top) MWU and (bottom) K–S test results for daily rainfall variables across the east Australian region. Red (black) filled circles

denote 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

during winter the wave trains associated with the IOD

emanate only from the eastern Indian Ocean, as ENSO

tends to offset convection in the western counterpart

during the developing IOD. In spring, however, both the

western and eastern Indian Ocean convective regions, in

association with stronger ENSO–IOD coherence, con-

tribute to stronger rainfall effects over the southern parts

of the continent (Cai et al. 2011), by conditioning cutoff

low systems that are an important source of rainfall in

the southeastern Australia (Risbey et al. 2009b). In

addition, our results show that ENSO also impacts on

rainfall intensity per wet day in the southern stations

(Fig. 1f), and that this effect becomes apparent once

the IOD effect is removed (Figs. 8d–f).

b. SAM

The SAM impact in winter shows notable spatial var-

iations (Fig. 9a). Phases of positive SAM as associated

FIG. 6. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation change

in normalized seasonal DMI. 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 . 1675

with a poleward shift of the subpolar westerlies, corre-

spond to anomalously dry conditions in the southern

parts 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 SAM

was not found to be significantly correlated with either

IOD or ENSO during our period of analysis, partial

regression analysis is not necessary and thus not per-

formed here. Figure 9 shows that the maximum impact

region and season for SAM is the east coast region in

SON. This is consistent with findings in other studies

that found the east coast of Australia experienced the

largest increases during the positive SAM phase, which

may be explained by anomalous easterly winds enhanc-

ing moisture advection from the ocean and hence in-

creased orographic rainfall in this region (Sen Gupta

and England 2007; Gillett et al. 2006; Hendon et al. 2007).

Conversely, the positive SAM also resulted in rainfall

reductions in the southern part of the region especially

during the JJA period (Cai and Cowan 2006; Meneghini

et 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, despite

the mixed response in total rainfall occasioned by SAM,

it transmits its influence in a manner consistent with the

other climate modes above: that changes, where present,

are more pronounced in number of wet days compared

to rainfall per wet day (Fig. 9).

It should be noted that the way in which the IOD and

the SAM transmit their influence to subdaily rainfall

variables mirrored that of ENSO, with changes, where

present, almost exclusively limited to the occurrence

of subdaily wet spells (results not presented here).

c. Interaction between IOD, SAM, and ENSO

For this section of analysis, we aim to test if inter-

actions between the different climate modes resulted

in enhanced effects on the three daily rainfall variables

described above. A time series of yearly east Australia

normalized rainfall, as well as the SOI, DMI, and the

SAM index is presented in Fig. 10 as a visual aid. We first

tested for changes corresponding to positive one and

negative 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 of

SOI and SAM index (again, based on the direction of

change in index that produced coherent impact on

rainfall variables) before including all three climate

indices as predictors into the model. The results are

summarized in Table 3, suggesting that the effect of

stratifying rainfall variables to multiple predictors did

not always translate to an additive effect to rainfall

variables. In particular, the ENSO–IOD pairing resulted

in minimal increase in magnitude of changes compared

to ENSO alone. This result was not surprising consid-

ering that they are correlated. With respect to the SOI–

SAM pairing, rainfall during spring season increased

significantly, with up to 30% change in total seasonal

rainfall amount that can be attributed to 18% increase in

number of wet days, and 12% increase in rainfall per wet

day (Table 3). Note that the effects of the SAM and

ENSO are offsetting in the southern region during win-

ter. However, when all three climate modes were in-

cluded as predictors, no significant new information

could be gleaned suggesting that interactions between

these modes resulted in only minor changes to the dy-

namics of rainfall in east Australia.

5. Conclusions

In this study, we have explored daily to subdaily rain-

fall variability at several sites across east Australia. The

main findings from our analyses can be summarized as

follows:

1) Changes to seasonal rainfall amounts associated with

ENSO variability over east Australia as a whole,

particularly during the cool seasons, can be primarily

attributed to changes in the number of wet days and

TABLE 2. Average percentage difference corresponding to 11 standard deviation of DMI, SAM in isolation, as well as that of partial

SOI and partial DMI, and the proportion of stations that are statistically significant (in parentheses) for each rainfall variable of interest

across 88 daily rainfall stations.

Season Variable 11 std dev DMI 11 std dev SAM 11 std dev partial SOI 11 std dev partial DMI

JJA 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

to a lesser extent, rainfall per wet day. Analysis of

subdaily rainfall data has further established that

changes to rainfall per wet day are in fact caused by

changes to the number of wet spells per day, and not

rainfall intensity per wet spell suggesting that it is the

probability of rainfall occurrence, rather than the

character of rainfall given that it has occurred, which

is the dominant mechanism driving seasonal rainfall

changes.

2) The impacts of ENSO and IOD in combination are

fairly spatially homogenous on the continent scale.

Spatial variations do nonetheless exist, with ENSO

and IOD more strongly impacting the inland east and

southeast regions, respectively. The SAM’s influ-

ence on rainfall is strongest over the east coast region

during SON, but is associated with a more mixed

rainfall response during JJA, with the southern part

of the region experiencing opposite effects to the

FIG. 7. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 1 1 standard deviation change

in 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

northern part. Despite having different maximum

impact regions and seasons, the manner in which

IOD and SAM impact on rainfall is somewhat similar

to that of ENSO, in that changes to the number of

wet days is overall more significant than rainfall per

wet day.

3) ENSO has been found to be the primary driver of

climate variability over east Australia during the long

period of analysis (1920–99). While combination of

the modes did occasionally produce additive effects

such as the ENSO–SAM in SON, we found signif-

icant redundancy in the effects of climate modes in

combination perhaps as a result of significant cor-

relation with each other (such as the ENSO–IOD

pairing).

Importantly, we have shown that insight into sub-

daily rainfall characteristics may help explain changes

FIG. 8. Changes for daily rainfall variables at individual stations across east Australia corresponding to a 11 standard deviation change

in 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

occurring at coarser time steps as well as assist with

making some initial inferences into the physical mech-

anisms behind climate-mode-driven rainfall. Because

rainfall intensity per wet spell remains fairly constant

under the influence of climate modes, we suggest that

the nature of the rainfall generating mechanism (i.e.,

stratiform vs convective) does not differ significantly as

a result of climate mode influence. Rather, it is the fre-

quency of rainfall events that are subject to change. It is

interesting that teleconnections appear to influence the

probability rather than the intensity of events when they

occur. The processes by which each of the modes trans-

mits their influence on rainfall characteristics warrant

further investigation.

It should be noted that the influence of climate modes

on rainfall characteristics over east Australia, and the

relationships 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 change

in normalized seasonal SAM. 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 . 1679

Cai et al. 2010; Kiem et al. 2003), as well as the warming

trend. Such a modulation effect is not investigated in

our study as the long record analyzed here captures both

phases of the IPO, and as such biases due to the records

representing a single phase alone are not expected. Our

analysis is, however, useful for inferring a generalized

role of the climate modes on rainfall characteristics.

The effects of the IPO and long-term trends should

nonetheless be investigated in future studies, in particu-

lar, concerning the possibility that the climate modes

may 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 overriding

importance of ENSO as the primary driver of rainfall

variability in east Australia was emphasized, with per-

centage changes between around 16%–18% per stan-

dard deviation of the SOI during the winter and spring

seasons, and slightly lower but also reasonably signifi-

cant increases for the remaining seasons; this highlights

that 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 the

eastern coastal fringe. The implications on flood risk are

therefore less likely to be due to changes in the intensity

of the flood-producing rainfall event during ENSO, with

a related study recently also showing only a 3.2% and

4.6% change in the intensity of the annual maximum

rainfall event pre standard deviation of the SOI for

subdaily and daily rainfall, respectively (Westra and

Sisson 2011). Rather, the increased number of wet spells

during La Nina periods suggests that it is likely to be the

catchment wetness (also referred to as the ‘‘antecedent

moisture’’) prior to the rainfall-producing flood event

that is most likely to increase, highlighting that the joint

probability of extreme rainfall and antecedent condi-

tions need to be modeled in order to properly capture

the implications of ENSO on flood risk (Kuczera et al.

2006). Analogous implications can be seen for other wa-

ter resources applications, with infiltration properties

and evaporation losses likely to be different for a larger

number of less intense events compared with a smaller

number of more intense events.

In conclusion, while the stochasticity of the climate

system prevents exact sequence prediction of weather

events within a particular season, we have shown that

it is possible to obtain useful information on shifts

in the important rainfall statistics using simple tech-

niques, even if there is still variability that remains

unexplained.

FIG. 10. Time series of normalized yearly east Australia aggregated rainfall, along with

normalized climate indices of SOI (blue), DMI (red), and SAM (green), respectively. The blue

circles are attached to the SOI indices when the magnitude of rainfall anomaly was greater than

one standard deviation. It is useful to note that the El Nino years encompass 1923, 1925, 1930,

1940, 1941, 1957, 1963, 1965, 1972, 1982, 1986, 1987, 1991, and 1997; and La Nina 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 regression

and proportion of stations that are statistically significant (in parentheses) for each rainfall variable of interest across 88 daily rainfall

stations. 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 SAM

JJA 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

Acknowledgments. This study was financially sup-

ported by the Australian Research Council. The rainfall

data used was made available by the Australian Bureau

of Meteorology.

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