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AUSTRALIAN CLIMATE and WEATHER EXTREMES:

PAST, PRESENT AND FUTURE

Neville Nicholls

A Report for the Department of Climate Change

January 2008


AUSTRALIAN CLIMATE and WEATHER EXTREMES:

PAST, PRESENT AND FUTURE

Neville Nicholls

A Report for the Department of Climate Change

January 2008


Published by the Department of Climate Change © Commonwealth of Australia, 2008

ISBN – 978-1-921297-67-0

This work is copyright. It may be reproduced in whole or in part for study or training purposes subject to the inclusion of an

acknowledgment of the source, but not for commercial usage or sale. Reproduction for purposes other than those listed above

requires the written permission of the Department of Climate Change.

Requests and inquiries concerning reproduction and rights should be addressed to:

Communications Manager

Department of Climate Change

GPO Box 854

CANBERRA ACT 2601

Acknowledgements

Participants in a workshop on Climate Change and Extreme Climate Events held in Canberra on 8 September 2006 contributed

to the discussions about research that needs to be done and to the content of this report.

Designed by Roar (DE&WR 3988)


CONTENTS

Executive summary ________________________________________________________________1

Introduction: Why the focus on extremes? _____________________________________________2

Box 1: Can individual extreme events be explained by climate change? ______________________ 3

What is a climate or weather extreme? ________________________________________________4

Recent progress in global monitoring of changes in extremes ______________________________7

Box 2: Tropical cyclones and climate change __________________________________________ 8

How are climate extremes changing across the world?____________________________________9

How have climate extremes changed in Australia? ______________________________________ 11

Box 3: Australian climate data – quality and availability ________________________________ 11

Temperature ________________________________________________________________ 12

Rainfall ____________________________________________________________________ 14

Tropical cyclones, extra-tropical systems, strong winds, and hail __________________________ 15

Droughts ___________________________________________________________________ 16

Sea level ___________________________________________________________________ 17

What has caused these changes in extremes? __________________________________________17

Box 4: How well do climate models simulate extremes? ________________________________ 18

How will extremes change in the future? _____________________________________________19

What needs to be done? ___________________________________________________________20

References ______________________________________________________________________22

DEPARTMENT OF CLIMATE CHANGE 2008 I


EXECUTIVE SUMMARY

Extremes are the infrequent events at the high and low end

of the range of values of a particular climate or weather

variable. A small change in the average of a climate variable

such as temperature can cause a large change in the

frequency of extreme temperatures such as frosts. Extreme

weather and climate events can cause severe impacts

on society, the economy, and the environment. Several

climate and weather extremes have cause severe impacts

in Australia in recent years. For example, Eastern Australia

experienced record temperatures during the period 1-22

February 2004 which led to “the most signifi cant medical

emergency in the south-east corner [of Queensland] on

record” (Canberra Times, 24 February). Tropical Cyclone Larry

left 100 square kilometres of World Heritage listed rainforest

in north Queensland as “coleslaw and sticks” in March 2006,

according to the Queensland Parks and Wildlife Service

(The Age, 11 April 2006, p 13). But are these examples of a

human influence on weather and climate extremes? A wide

range of extreme weather events is possible even with an

unchanging climate, so it is difficult to attribute an individual

event to a changed climate. As well, until recently the quality

and quantity of data to study changes in extremes have

been insufficient to allow credible examination of whether

extremes are changing.

Over the past decade there have been increased international

efforts to improve the quality and availability of data suitable

for determining whether or not extremes have changed.

Analyses of these data indicate that more than 70% of the

global land area sampled exhibited a statistically signifi cant

decrease in the annual occurrence of cold nights and a

significant increase in the annual occurrence of warm nights.

Precipitation extremes showed a widespread and signifi cant

increase, but the changes are much less spatially coherent

compared with temperature change. Other extremes such as

tornadoes are much harder to monitor and it is thus much

harder to determine whether or not there has been a change

in their frequency and/or intensity. Changes in Australian

extremes are generally similar to the changes that have been

observed globally. Some of these changes, at least in the case

of extreme temperatures, now appear to be at least partly

attributable to human influences on the climate.

The changes in Australian extremes likely to accompany anticipated

future increases in atmospheric concentrations of greenhouse

gases include (from various sources – see text for details):

> Increase in frequency of days over 35ºC by 2020;

> Decrease in frequency of days below 0ºC by 2020;

> Increases in intensity of heavy daily rainfall events,

although there appears likely to be considerable spatial

variation in this;

> Decrease over north-east Australia of the number of

tropical cyclones, accompanied by an increase in intensity;

> Decreased hail frequency in some places;

> Increase in large hail (2cm diameter) and reduction in

average recurrent interval for hail exceeding 6cm diameter

in Sydney;

> More droughts over most of Australia by 2030;

> Increased frequency of extreme fire danger days

(except Tasmania).

There is, however, considerable uncertainty in these

projections, arising from the limited number of climate

simulations from which they are derived, as well as

model defi ciencies.

Further work is needed to refine our understanding of

extremes and their possible changes, including:

> A reanalysis of tropical cyclone data, to facilitate

comparisons of the frequency and intensity of current-day

cyclones with those in the past.

> Analysis of historical changes in drought frequency,

intensity and duration, using multiple drought indices,

e.g. rainfall deficiency, standardised precipitation index,

soil moisture deficit and Palmer Drought Severity Index.

Climate change projections are required for the same

indices, including estimation of drought return periods

relevant for assessment of Exceptional Circumstances.

> A regional reanalysis, including homogenisation of upperair

data, to facilitate studies linking specific extremes with

synoptic patterns.

> Improved downscaling of small-scale synoptic events

such as tornadoes, hailstorms and thunderstorms that are

difficult to monitor using conventional meteorological

networks and approaches, to facilitate an increased focus

on studies of these small-scale events.

> Improved climate models, with higher resolution and

improved parameterisation of small-scale processes that

lead to extremes. This will require involvement of the

user community in the design of ACCESS (the Australian

Community Climate Earth System Simulator).

> Development of high quality historical data sets for wind

speed and hail, to facilitate documentation of any trends in

these extremes.

> Improved historical data sets of rainfall and temperature

should include the effects of urban heating, rather than

removing such effects.

DEPARTMENT OF CLIMATE CHANGE 2008 1


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

> An increased emphasis is required on sub-daily

precipitation extremes, and the analysis of historical

changes in extremes would be facilitated by increased

palaeo-climatic emphasis on extreme events.

> Joint analyses of multiple extremes (e.g. strong winds and

heavy rainfall) that might exacerbate the impacts either

extreme would have on its own.

> Studies to determine how much of the recent trends in

extreme temperatures are attributable to human actions,

and how this varies seasonally and spatially.

> Integrated assessments are needed to determine how

communities could or should react to changes in extremes.

> A comprehensive assessment of projected changes in

extreme daily temperature, rainfall, wind, fi re danger,

tropical cyclones, hail, tornadoes and storm surges. To

ensure internal consistency, this would require a suite of

simulations from selected climate models that perform well

in the Australian region.

2

INTRODUCTION:

WHY THE FOCUS ON

EXTREMES?

Extreme weather and climate events can cause severe

impacts on our society and environment. For instance,

heatwaves can be devastating for societies that are not used

to coping with such extremes. The 1995 Chicago heatwave

was such an event (Karl and Knight, 1997) where over 500

people died from heat-related illnesses. The 2003 heatwave

in Europe was unprecedented in terms of loss of life, with

over 30,000 deaths in Europe (14,947 deaths in France alone,

Poumadere et al. (2005)) attributable, at least in part, to

the excessive and persistent heat (IFRCRC, 2004). The 2003

heatwave also led to destruction of large areas of forests

by fire, and affected ecosystems and glaciers (Gruber et al.,

2004; Koppe et al., 2004; Kovats et al., 2004; Schär and

Jendritzky, 2004; Kovats and Koppe, 2005). According to

reinsurance estimates, the drought conditions during the

summer of 2003 caused crop losses of around US$13 billion,

while forest fires in Portugal were responsible for an additional

US$1.6 billion in damage (Schär and Jendritzky, 2004).

Impacts of some recent extreme events in Australia

(Hennessy and Fitzharris, pers. comm.)

Droughts: the droughts of 1982/83, 1991-95, and

2002/03 cost about $2.3 billion, $3.7 billion and

$10 billion, respectively. Government drought relief

averaged $100 million per year.

Sydney hailstorm, April 1999: Cost $2,300 million of

which $1,700 million was insured.

East Australian heatwave, 1-22 February 2004: The

south-eastern Queensland ambulance service recorded

a 53% increase in ambulance callouts.

Canberra bushfi res 2003: Wildfires caused $350 million

damage. About 500 houses destroyed, and four people

killed. Three of the city’s four dams were contaminated

for several months by sediment-laden runoff.

South-east Australia storm, 2 February 2005:

Insurance claims reached almost $200 million.

Transport was severely disrupted. Both Melbourne

commercial airports were inaccessible for some hours.

Tropical Cyclone Larry, 20 March 2006: Signifi cant

damage or disruption to houses, businesses,

industry, utilities, infrastructure (including road, rail

and air transport systems, schools, hospitals and

communications), crops and state forests, costing

$350 million. Fortunately, the 1.75 m storm surge

occurred at low tide.

DEPARTMENT OF CLIMATE CHANGE 2008


Several climate and weather extremes have had deleterious

impacts on Australia in recent years. Eastern Australia

experienced record temperatures during the period 1-22

February 2004. Mean maximum temperatures for the

period were 5-6ºC above average throughout large areas of

eastern Australia, reaching up to 7ºC above average in parts

of New South Wales (National Climate Centre, 2004). The

number of successive hot days and nights set new records.

The run of nine consecutive nights above 30ºC in the rural

town of Oodnadatta is without precedent in the Australian

climate record. Adelaide had 17 successive days over 30ºC

(the previous Adelaide record was 14 days). Sydney had

ten successive nights over 22ºC (the previous record was

six, set in 2001 and 1988). About two-thirds of continental

Australia recorded maximum temperatures over 39˚C in

the period 1-22 February. Temperatures peaked at 48.5˚C

in western New South Wales. The high temperatures led to

newspaper headlines such as “Hot spell hits with collapses”

(Sunday Mail, Adelaide, 8 February), “Taking the heat in

state of distress” (Daily Telegraph, Sydney, 12 February),

and “Sweltering temperatures make school children sick”

(Queensland Times, 19 February). Brisbane recorded a

temperature of 41.7ºC on the weekend of 21-22 February,

exceeding the previous February record by nearly one degree.

That weekend the Queensland ambulance service recorded

“a 53% increase in ambulance callouts”, and the ambulance

service Commissioner described it as “the most signifi cant

medical emergency in the south-east corner [of Queensland]

on record” (Canberra Times, 24 February).

Currently, about 1100 heat-related deaths occur each year

in Australian temperate cities (McMichael et al., 2003). The

projected rise in temperature over the next 50 years, along

with anticipated demographic change, is predicted to result

in 3200-5200 more heat-related deaths in all Australian

cities, with decreases in deaths related to cold temperatures

(as the climate warms) being “greatly outnumbered by

additional heat-related deaths” (McMichael et al., 2003). As

well, the warming caused by the enhanced greenhouse effect

may lead to enhanced bush fire risk (Williams et al., 2001;

Hennessy et al., 2006), with increased likelihood of deaths.

Much of the increased risk would be related to an increase in

hot extremes, rather than a general warming.

Another extreme event, Tropical Cyclone Larry, left 100

kilometres square of World Heritage listed rainforest in

north Queensland as “coleslaw and sticks” in March 2006,

according to the Queensland Parks and Wildlife Service

(The Age, 11 April 2006, p 13). Thirty Queensland parks and

state forests were closed or partly closed as a result of the

cyclone impacts, with an estimated damage cost to the parks

and reserves of $10 million. The Parks Service considered

the survival (after Larry) of the southern cassowary, an

endangered species, around Mission Beach, to be tenuous.

Insured damages totalled $350 million.

Box 1: Can individual extreme events be explained

by climate change?

A wide range of extreme weather events is possible even

with an unchanging climate, so it would be diffi cult

to attribute an individual event, by itself, to a changed

climate. As well, extreme weather results from a

combination of factors. For example, the formation of a

tropical cyclone requires warm sea surface temperatures

and specific atmospheric circulation conditions. Because

some factors may be strongly affected by human

influences (e.g. sea surface temperatures) but others

may not, this will complicate the detection of a human

influence on a single, specific extreme event.

However, we may be able to determine whether

anthropogenic forcing has changed the probability of

occurrence of a specific type of extreme weather event

such as heatwaves. This can be addressed, for example

for the 2004 southern Queensland heatwave, by studying

the characteristics of Queensland summers in a climate

model, either forced only with historical changes in

natural factors such as volcanic activity and the solar

output, or by both human and natural factors. Such

experiments may indicate whether, over the 20th century,

human influences have increased the risk of southern

Queensland temperatures as hot as those in February 2004.

The value of a probability-based approach (“is there a

change in the likelihood of an event that results from

human influence?”) is that it can be used to estimate

the influence of external factors, such as increases in

greenhouse gas concentrations, on the frequency of

specific types of weather events (e.g. frost). However,

careful statistical analyses are required, since the

likelihood of individual extremes, such as a late spring

frost, could change due to changes in variability as well

as changes in the mean climate. Such analyses rely on

climate model-based estimates of variability, and thus an

important additional requirement is that climate models

adequately represent climate variability.

The same likelihood-based approach could be adopted

to examine possible changes in the frequency of extreme

hydrological events such as heavy rainfalls or fl oods.

Climate models predict that there will be changes in

the incidence of many types of extreme weather events,

including an increase in extreme rainfall events, due

to human influences on the atmosphere. There is some

evidence of increases in extreme rainfall events in at

least some regions in recent decades. However there

is as yet no conclusive evidence that these increases

are necessarily linked to increasing greenhouse gas

concentrations in the atmosphere.

DEPARTMENT OF CLIMATE CHANGE 2008 3


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

Shorter-lived and smaller-scale phenomena, such as

thunderstorms and tornadoes, can cause severe damage. A

severe hailstorm struck the eastern suburbs of Sydney in the

evening of 14 April 1999, causing damage estimated at $2.3

billion, making it Australia’s costliest natural disaster ever.

The storm was highly unusual. Not only did it produce some

of the largest hail ever recorded in Sydney, but it occurred

at a time of year when severe thunderstorms are normally

rare and at a time of day when the probability of storms

developing, or existing storms maintaining their intensity, is

low (Nicholls, 2001).

Sometimes two or more extremes can occur simultaneously,

thereby increasing the damage or risk that would result

from a single extreme. For example, high winds sometimes

accompany heavy rainfall events. Heavy rainfall can weaken

the hold of tree roots, thereby increasing the likelihood that

a tree will be uprooted in the strong winds. Strong winds

associated with cyclones may also occur at the same time

that high sea levels occur, increasing the likelihood of

coastal inundation. Similarly, heatwaves can cause heatrelated

deaths, fires, smoke pollution, respiratory illness,

increased peak energy demand for air-conditioning,

blackouts, increased water demand and buckling of

railways. Little work has been done on the risk of such joint

occurrences, whether we might expect a change in the

frequency of such simultaneity of extremes in the future, or

determining the impact and cost of the joint extreme relative

to a single extreme.

These are just some examples of the damage and loss

of life caused by climate and weather extremes. Tropical

cyclones and floods together account for more than 70%

of known natural hazard deaths in Australia since 1788

(Blong, 2004). Thunderstorms account for about 11% of

deaths. Meteorological extremes (tropical cyclones, fl oods,

thunderstorms and bushfires) produced 93.6% of known

building damage from disasters suggesting that nonmeteorological

natural hazards are far less important. There

is widespread interest in how and why climate and weather

extremes are changing, and in the question of whether or

not human activities are causing changes in extremes. But

the answer to such questions is rarely simple. Even the

question of definition of a climate or weather extreme can

be complex.

WHAT IS A CLIMATE OR

WEATHER EXTREME?

Extremes are the infrequent events at the high and low end

of the range of values of a particular variable. The probability

of occurrence of values in this range is called a probability

distribution function (pdf) that is, for many variables, shaped

similarly to a “Normal” or “Gaussian” curve (the familiar “bell”

curve). Figure 1 shows such a pdf and illustrates the effect a

small shift (corresponding to a small change in the average

or centre of the distribution) can have on the frequency of

extremes at either end of the distribution. An increase in the

frequency of one extreme (e.g. the number of hot days) will

often be accompanied by a decline in the opposite extreme

(in this case the number of cold days such as frosts). Of

course, changes in the variability or shape of the distribution

can complicate this simple picture but the figure shows that

in this case, the number of very cold nights has been reduced

by more than 50% as the mean temperature increased by less

than a degree 1 .

Figure 1: Illustration of the effect of increase in mean

temperature on risk of extremes. Blue (1957-1980) and red

(1981-2005) show for Melbourne, Australia, the probability

distribution function of daily minimum temperatures.

Vertical coloured broken lines show mean minimum

temperatures for the two periods. Vertical broken black

line shows probability of extreme cold (


The principal focus in this report is on weather extremes,

rather than climate extremes. So much of the focus is on

extremes calculated from daily temperature and precipitation

data, rather than longer-term extremes (although droughts

are considered). Synoptic events such as tropical cyclones are

also examined.

The large impacts climate and weather extremes can have,

and the possibility that their frequency of occurrence may

change substantially with even small changes in average

climate, means that changes in extremes may be the fi rst

indication that climate is changing in a way that can affect

humans and the environment substantially. On the other

hand, problems with data and analyses of extremes can

make it very difficult to determine whether or not they

are changing. The extra effort required for such analysis

of changes in extremes may, in some cases, simply not be

worthwhile. In some cases, it is likely that changes in the

frequency of extremes may simply reflect changes in the

mean of the variable under consideration. Thus an increase in

mean temperature could be expected to lead to an increase

in the number of extremely hot days, unless the probability

distribution changes shape or variance in such a way as

to offset the effect of the increase in mean temperature.

In such cases it may be simpler and more cost effective to

parameterise the changes in extremes by the changes in the

mean of the variable. In other cases however, the shape of

the distribution may change so radically that the change in

the mean does not provide a good prediction of changes in

the frequency of extremes of the distribution. For instance,

Easterling et al. (2000) suggest that precipitation extremes

may be changing more dramatically than would be expected

from a simple shift of the distribution. Determining which of

these cases predominates is an important research question,

and probably will vary from place-to-place as well as

between variables.

One problem with determining whether or not extremes

are changing or will change in the future arises from the

need to define an extreme more precisely than is illustrated

in Figure 1, and the existence of different possibilities for

calculating a specific extreme. Although the concept of

extremes as the tails of a probability distribution appears

simple, in reality there are many defi nitional possibilities.

For instance, Haylock and Nicholls (2000) examined three

different measures of extreme precipitation: the number of

events above the long-term 95th percentile, referred to as

the extreme frequency; the average intensity of rain falling

in the highest events, referred to as the extreme intensity;

and the proportion of total rainfall falling in the highest

events, referred to as the extreme percent. The extreme

frequency index examines changes in the number of extreme

events. The extreme intensity describes changes in the upper

percentiles and, unlike an analysis of a single percentile

threshold (e.g. Hennessy et al., 1999), incorporates changes in

all events above this percentile. This index was calculated by

Haylock and Nicholls (2000) using three different methods:

averaging the highest four events for each year; averaging

the highest 5% of daily rainfall totals above 1mm; and

averaging all events above the long-term 95th percentile. The

extreme percent reflects changes in the upper portion of the

daily rainfall distribution. The percentage of the total rainfall

from the higher events is an indictor of changes in the

shape of the rainfall distribution. This index was calculated

for each year by dividing the extreme intensity by the year’s

total rainfall. Different trend magnitudes are found for the

different definitions of extremes (Haylock and Nicholls 2000;

Alexander et al., 2006b; Gallant et al., submitted). Different

definitions even resulted in different signs of the trends, as

well as their magnitude.

So which is the best index of extreme rainfall intensity

and how should it be calculated? The best guide of index

design must be the final purpose of the index (Haylock and

Nicholls, 2000). If the aim is to use the index for climate

change detection, then a complex index can be considered.

On the other hand, an index for use by the public should be

as clear and simple as possible. Explaining to a farmer that

the proportion of annual rainfall from the highest 5% of

events has increased but the actual number of events has

decreased may be confusing. An index such as the amount

of rain from the top four events is much clearer. However,

if the desire is to find an index that reflects changes in the

shape of a frequency distribution, then an index such as the

average intensity of the highest 5% of events may be better.

For analysis of shapes of distributions, parametric approaches

(i.e. fitting statistical distributions to the data and then

examining how the values of the parameters defi ning these

distributions change with time) might also be considered

(e.g. Groisman et al., 1999). The difficulties in defi ning

appropriate indices for different users highlights the need for

readily accessible climate datasets which users can analyse

according to their individual needs.

Similar problems with definitions can arise no matter what

climate or weather variable is being considered. For instance,

should a time series showing changes in the number of

tropical cyclones include all tropical cyclones or just the

most intense? There have been attempts to reduce the

confusion caused by a multiplicity of definitions of extremes,

by promulgating definitions and undertaking analyses with

a defined subset of possible definitions. Table 1 provides

such a set of definitions for extremes determined from

daily temperature and rainfall records (Alexander et al.,

2006b). Even after a set of definitions is arrived at, however,

there still remain major problems in monitoring changes in

weather and climate extremes, although substantial advances

have been made in this area over the past 15 years or so.

Care does need to be taken to ensure that some or all of the

common problems with instrumental climate data do not

DEPARTMENT OF CLIMATE CHANGE 2008 5


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

affect the analyses of extremes. These problems include but are not restricted to (Nicholls et al., 2006):

> changes in site location

> changes in site condition or local environment

> changes in instrumentation

> changes in observing practices

> changes in network distribution.

ID Indicator name Indicator defi nitions UNITS

TXx Max Tmax Monthly maximum value of daily maximum temperature ºC

TNx Max Tmin Monthly maximum value of daily minimum temperature ºC

TXn Min Tmax Monthly minimum value of daily maximum temperature ºC

TNn Min Tmin Monthly minimum value of daily minimum temperature ºC

TN10p Cool nights Percentage of time when daily minimum temperature < 10th percentile %

TX10p Cool days Percentage of time when daily maximum temperature < 10th percentile %

TN90p Warm nights Percentage of time when daily minimum temperature > 90th percentile %

TX90p Warm days Percentage of time when daily maximum temperature > 90th percentile %

DTR Diurnal temperature Monthly mean difference between daily maximum and minimum ºC

range

temperature

FD0 Frost days Annual count when daily minimum temperature < 0ºC days

SU25 Summer days Annual count when daily maximum temperature > 25ºC days

TR20 Tropical nights Annual count when daily minimum temperature > 20ºC days

WSDI* Warm spell duration Annual count when at least 6 consecutive days of maximum

days

indicator

temperature > 90th percentile

CSDI* Cold spell duration Annual count when at least 6 consecutive days of minimum temperature days

indicator

< 10th percentile

RX1day Max 1-day

precipitation amount

Monthly maximum 1-day precipitation mm

RX5day Max 5-day

precipitation amount

Monthly maximum consecutive 5-day precipitation mm

SDII Simple daily intensity The ratio of the number of wet days (> 1mm) to annual total

mm/day

index

precipitation

R10 Number of heavy

precipitation days

Annual count when precipitation > 10mm days

R20 Number of very heavy

precipitation days

Annual count when precipitation > 20mm days

CDD* Consecutive dry days Maximum number of consecutive days when precipitation < 1mm days

CWD* Consecutive wet days Maximum number of consecutive days when precipitation ≥ 1mm days

R95p Very wet days Annual total precipitation from days > 95th percentile mm

R99p Extremely wet days Annual total precipitation from days > 99th percentile mm

PRCPTOT Annual total wet-day

precipitation

Annual total precipitation from days ≥ 1mm mm

Table 1: The extreme temperature and precipitation indices used by Alexander et al., (2006a7). Precise defi nitions are

given at http://cccma.seos.uvic.ca/ETCCDMI/list_27_indices.html. (From Alexander et al., 2006a)

6

DEPARTMENT OF CLIMATE CHANGE 2008


RECENT PROGRESS IN

GLOBAL MONITORING

OF CHANGES IN

EXTREMES

The various assessments by the Intergovernmental Panel on

Climate Change (IPCC) provide an indication of progress over

the past 15 years in the assessment of climate extremes and

their changes. The 1992 Supplement Report to the (First)

Scientific Assessment of climate change from the (IPCC)

concluded that global mean surface air temperature had

increased by about 0.3 to 0.6ºC over the past 100 years, but

did not even consider the question of whether extremes

in temperature, precipitation or circulation features such

as tropical cyclones had changed (Folland et al., 1992). By

1995, the Second Assessment Report (SAR) of the IPCC was

specifically addressing the question “Has the climate become

more variable or extreme?” (Nicholls et al., 1995). They

concluded that “Overall, there is no evidence that extreme

weather events, or climate variability, has increased, in a

global sense, through the 20th century, although data and

analyses are poor and not comprehensive.” The SAR noted

that the data on climate extremes and variability available

at that time were inadequate to say anything about recent

global changes, although in some regions where data are

available, there had been changes in extreme events. The

SAR also concluded that we should expect “an increase in

the occurrence of extremely hot days and a decrease in the

occurrence of extremely cold days”, in the future (Houghton

et al., 1995, p 7).

Nicholls (1996) observed that a major problem undermining

our ability to determine whether extreme weather and

climate events were changing was that it is more diffi cult

to maintain the long-term homogeneity of observations

required to observe changes in extremes, compared to

monitoring changes in means of variables. Ambiguities

in defining extreme events and difficulties in combining

different analyses from different sites also complicate

attempts to determine, on a global scale, whether extreme

events are changing in frequency.

An international workshop on weather and climate extremes

was held in 1997 to examine what needs to be done

to improve datasets and analyses for extreme weather

monitoring (Karl et al., 1999), inspired by the inability of the

IPCC SAR to determine whether extreme events had been

increasing globally. The Workshop noted that the “first step in

the detection/attribution of climate change is the assembly

of high-quality time-series of key variables”. This led to a

series of workshops using a common approach to select high

quality stations, perform quality control, and investigate

trends in extreme events (Nicholls and Alexander, 2007).

The collation and analyses of daily datasets has not been

a simple task. One reason is that many countries do not

have the capacity to freely distribute daily data. Another

reason is that data need to undergo rigorous quality control

before being used in any extremes analysis since values are

likely to show up erroneously as extreme when incorrectly

recorded. In recent years, the World Meteorological

Organisation (WMO) Expert Team on Climate Change

Detection, Monitoring and Indices (ETCCDMI) has overseen

the development of a standard software package that not

only quality controls data but provides researchers with

the opportunity to exchange and compare results. The

main purpose of the quality control procedure is to identify

errors in data processing such as negative precipitation or

daily minimum temperatures greater than daily maximum

temperatures. In addition, “outliers” are identified in daily

temperatures i.e. values outside a given number of standard

deviations of the climatological mean value for that day.

These can then be manually checked and removed or

corrected as necessary. The software, RClimDex, developed by

the Climate Research Branch of the Meteorological Service of

Canada (http://cccma.seos.uvic.ca/ETCCDMI/software.html),

also calculates a standard set of 27 extremes indices derived

from daily temperature and precipitation. While the quality

controlled daily data are rarely exchanged, there have been

fewer obstacles to exchanging the climate extremes data

calculated using this software.

In addition to these quality control measures, perhaps an

even more important aspect of the study of extremes is to

remove inconsistencies or “inhomogeneities” (that is, artifi cial

changes which cannot be explained by changes in climate –

see Nicholls et al., 2006) from the daily data prior to analysis.

As noted above, such inhomogeneities can be introduced

into climate data by the relocation of an observing site to

a more shaded or exposed location, or the implementation

of more accurate recording instrumentation. However, the

identification, removal or indeed correction of these types

of errors is complex and diffi cult (Aguilar et al., 2003). The

ETCCDMI has therefore also coordinated the development

of other standard software, RHTest, using the Wang (2003)

methodology, which can be used in tandem with RClimDex.

However, identifying potential problems is only the fi rst step.

On regional scales there has been some limited success in

correcting daily temperatures (e.g.Vincent et al., 2002) and

precipitation (e.g. Groisman and Rankova, 2001) for such

inhomogeneities, but globally, given the many different

climate regimes, this task has proved too problematic and so,

in general, suspicious data have not been included in studies

(Alexander et al., 2006a).

DEPARTMENT OF CLIMATE CHANGE 2008 7


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

8

Box 2: Tropical cyclones and climate change

Tropical cyclones generally only form in areas with

sea surface temperatures (SSTs) above 26.5ºC (e.g.

Pielke, 1990). This well-documented climatological

relationship has led many to speculate that years with

warmer SSTs should, all other factors remaining equal,

have more tropical cyclones (e.g. Riehl, 1951). In turn,

this has led to many studies exploring links between

SSTs and Atlantic tropical cyclone numbers. Thus

Wendland (1977) concludes that an increase in annual

SSTs of 1.1ºC would lead to almost a doubling of

cyclone numbers in the North Atlantic, if other factors

did not change. However, tropical cyclones would also

be affected by changes in the stability of the tropical

atmosphere (Oouchi et al., 2006) and the importance of

factors other than SSTs complicates the attribution of

changes in cyclones to warming trends in tropical SSTs.

In the Australian region, variations in the number

of tropical cyclones from year-to-year are strongly

correlated with local SSTs before and near the start of

the cyclone season, the strongest correlations being

with October SSTs (Nicholls, 1984). However, the

correlations with SSTs later in the cyclone season fi rst

drop to zero, and then become negative (with SSTs

from February and later). Australian region tropical

cyclone numbers are also correlated with indices of the

El Niño from the central and east equatorial Pacifi c,

suggesting a remote effect on tropical cyclone numbers

(presumably operating through the effects of the El

Niño on the tropical atmosphere around Australia)

(Kuleshov, 2003). It is therefore difficult to estimate,

based on these empirical results, what the effect of a

general warming (i.e. an increase in SSTs) would have

on Australian region tropical cyclone numbers. If the

only variable affecting tropical cyclone numbers was

the SST just prior to the start of the cyclone season

(September-November), then the results of Nicholls

(1984) suggest (see his Figure 9) that a 1ºC increase in

SST would result in about five more tropical cyclones

(cf the mean number of ~10) per year. However, since

1981 (when reliable records start) there has been no

significant trend in the number of Australian tropical

cyclones, but there has been an increase in cyclone

intensity (a reduction in central pressure) (Kuleshov,

2003; Hennessy, 2004).

Given the empirical evidence of a relationship between

SSTs and tropical cyclone numbers, it is not surprising

that one of the early concerns about the possible

effects of the enhanced greenhouse effect was an

increase in the frequency of tropical cyclones, although

atmospheric scientists have tended to discount this

(e.g. Holland et al., 1988; IPCC, 2001). More recently,

there has been concern that the relative frequency

of very strong tropical cyclones may be increasing

(Emanuel 2005; Webster et al., 2005; Hoyos et al.,

2006), although there are concerns with the quality

of the historical tropical cyclone data on which

these studies relied (McBride et al., 2006). However,

the evidence for more intense hurricane activity in

the North Atlantic seems strong, and Goldenburg et

al., (2001) attribute this increased activity partly to

increases in SSTs, as do Klotzbach and Gray (2006).

Santer et al., (2006) attributed the warming SSTs

to human (enhanced greenhouse) factors, so it

seems more likely than not that human activity has

contributed to the recent enhanced hurricane activity,

at least in the North Atlantic. The frequency of severe

tropical cyclones (Categories 3, 4 and 5) on the east

Australian coast is simulated to increase 22% for the

IS92a greenhouse gas scenario from 2000-2050, with a

200 km southward shift in the cyclone genesis region,

leading to greater exposure in south-east Queensland

and north-east NSW.

These and other multi-national efforts to collate and quality

control daily weather data meant that, by the time of the

IPCC Third Assessment (TAR) in 2001, more could be said

about how extreme weather events appeared to be changing.

The IPCC TAR concluded (IPCC Summary for Policymakers,

2001) that:

> In the mid- and high latitudes of the Northern Hemisphere

over the latter half of the 20th century, it is likely that there

had been a 2 to 4% increase in the frequency of heavy

precipitation events.

> Since 1950 it is very likely that there had been a reduction

in the frequency of extreme low temperatures, with

a smaller increase in the frequency of extreme high

temperatures.

> In some regions, such as parts of Asia and Africa, the

frequency and intensity of droughts had been observed to

increase in recent decades.

> Changes globally in tropical and extra-tropical storm

intensity and frequency were dominated by inter-decadal

to multi-decadal variations, with no signifi cant trends

evident over the 20 th century. Conflicting analyses make it

difficult to draw definitive conclusions about changes in

storm activity, especially in the extra-tropics.

> No systematic changes in the frequency of tornadoes,

thunder days, or hail events were evident in the limited

areas analysed.

DEPARTMENT OF CLIMATE CHANGE 2008


The TAR also concluded that it was likely or very likely that

continued anthropogenic interference with the atmosphere

would lead to increased numbers of warm extremes, heavy

rainfall events, tropical cyclone peak wind intensities, and

droughts, and decreased numbers of cool extremes.

Subsequently, increased efforts to collate and analyse data

on weather and climate extremes (including the more recent

workshops noted above) meant that much more of the

globe could be examined for trends in extremes by 2006 (e.g.

Alexander et al., 2006a), in time for inclusion in the IPCC

Fourth Assessment completed in January 2007.

HOW ARE CLIMATE

EXTREMES CHANGING

ACROSS THE WORLD?

Alexander et al., (2006a) found that over 70% of the global

land area sampled showed a statistically signifi cant decrease

in the annual occurrence of cold nights and a signifi cant

increase in the annual occurrence of warm nights. Some

regions experienced a more than doubling of these indices.

This implies a shift in the distribution of daily minimum

temperature throughout the globe towards warmer

temperatures. Daily maximum temperature indices showed

similar changes but with smaller magnitudes. Precipitation

extremes showed a widespread and signifi cant increase,

but the changes are much less spatially coherent compared

with temperature change. Significant increases in observed

extreme precipitation have been reported over some parts

of the world, for example over the United States, where the

increase is similar to changes expected under greenhouse

warming (Semenov and Bengtsson, 2002; Groisman et al.,

2005). Summaries of how various climate extremes have

changed in recent decades, an assessment of whether there is

evidence that these changes are the result of human activity,

and projections of future changes of these extremes due to

human interference in the climate system are noted in Table

2 (from Solomon et al., 2007).

The strongest evidence that extremes are changing, and

that this is the result of human activity, is for daily

temperature extremes (both warm and cold extremes). The

evidence is less compelling with regard to precipitation

extremes (either short-term heavy rainfall events or extended

extremes such as droughts), although there is evidence

suggesting that changes in these extremes are similar to

those expected from human influences on the climate.

Trends in synoptic systems (e.g. tropical cyclones) are more

difficult to assess, because of difficulties in monitoring these

systems consistently over several decades, and diffi culties in

modelling and understanding them. Determining whether

sub-synoptic scale systems (e.g. tornadoes) are even

changing is even more challenging.

DEPARTMENT OF CLIMATE CHANGE 2008 9


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

Phenomenon a and

direction of trend

Warmer/fewer cold days/

nights over most land

areas

Warmer & more frequent

hot days/nights over most

land areas

Warm spells / heat waves.

Frequency increases over

most land areas

Heavy precipitation events.

Frequency (or proportion

of total rainfall from heavy

falls) increases over most

areas

Area affected by droughts

increases

Intense tropical cyclone

activity increases

Increased incidence of

extreme high sea level

(excludes tsunamis) g

Likelihood that trend

occurred in late 20th

century (typically post

1960)

Likelihood of

discernible human

infl uence on

observed trend b

D

Likelihood of future trend

based on projections for 21st

century using SRES scenarios

Very likely c Likely e * Virtually certain d

Very likely d Likely (nights) e * Virtually certain d

Likely More likely than

not f

Likely More likely than

not f

Likely in many regions since

1970s

Likely in some regions since

1970

More likely than

not

More likely than

notf Likely More likely than

not f,h

Very likely

Very likely

* Likely

Table 2: Trends, attribution and projections of global extreme weather and climate events. Only extremes for which there

is evidence of an observed late 20 th century trend are included. Thus, cold spells and small-scale weather phenomena

for which there are insufficient studies for assessment of observed changes are not included in Table. Asterisk in column

headed “D” indicates that formal detection and attribution studies were used, along with expert judgement, to assess the

likelihood of a discernible human influence. Where this is not available, assessments of likelihood of human infl uence are

based on attribution results for changes in the mean of a variable or in physically related variables, on qualitative similarity

of observed and simulated changes, combined with expert judgement. Likelihood terminology: “very likely” means >90%

probability, but 66% but 50%. (from Solomon et al., 2007).

(a) See Table 3.7 for further details regarding defi nitions.

(b) See Table TS-4, Box TS-3.4 and Table 9.4.

(c) Decreased frequency of cold days and nights (coldest 10%).

(d) Increased frequency of hot days and nights (hottest 10%).

(e) Warming of the most extreme days and nights each year.

(f) Magnitude of anthropogenic contributions not assessed. Attribution for these phenomena based on expert judgement rather than formal

attribution studies.

(g) Extreme high sea level depends on mean sea level and on regional weather systems. It is defined here as the highest 1% of hourly values of

observed sea level at a station for a given reference period.

(h) Changes in observed extreme high sea level closely follow the changes in mean sea level {5.5.2.6}. It is very likely that anthropogenic activity

contributed to a rise in mean sea level. {9.5.2}

(i) In all scenarios, the projected global mean sea level at 2100 is higher than in the reference period. {10.6}. The effect of changes in regional

weather systems on sea level extremes has not been assessed. (Solomon et al., 2007)

10

Likely

Likely i

DEPARTMENT OF CLIMATE CHANGE 2008


HOW HAVE CLIMATE

EXTREMES CHANGED IN

AUSTRALIA?

Trends in Australian temperature and precipitation extremes

have been examined extensively (e.g. Suppiah and Hennessy

(1996, 1998); Plummer et al., (1999); Collins et al., (2000);

Haylock and Nicholls, (2000); Manton et al., (2001); Griffi ths

et al., (2005); Nicholls and Collins (2006); Gallant et al.,

submitted). Nicholls et al., (2000) examined Australian trends

in a wide variety of climate extremes and concluded that:

> the number of weak and moderate tropical cyclones

observed has decreased since 1969 which, although

consistent with changes in the Southern Oscillation

Index, may be partly caused by changes in the

observational system;

> the number of intense tropical cyclones has increased

slightly since 1969;

> windiness in the eastern Bass Strait has fallen, while it

has increased slightly in the western Bass Strait, since the

early 1960s;

> there has been a strong decrease, since 1910, in

the intensity of rain falling on very wet days, and in the

number of very wet days, in the south-west of

the continent;

> there has been a strong increase in the proportion of

annual rainfall falling on very wet days in the north-east;

> no clear trend has emerged in the percentage of the

country in extreme rainfall (drought or wet) conditions,

since 1910, although Burke et al., (2006) reported an

increase in the Palmer Drought Severity Index in southwestern

and eastern Australia from 1952-1998;

> there is a downward trend in frequency of cool nights, with

some evidence of an upward shift in frequency of warm

nights (since 1957);

> there is some suggestion of an increase in frequency of

warm days since the mid-1970s; and

> no clear trend exists in the frequency of cool days.

The remainder of this section updates the results of Nicholls

et al., (2000), where possible, based on recent studies.

Box 3: Australian climate data – quality and availability

The Australian Bureau of Meteorology has developed

a number of datasets for use in climate change

monitoring. These datasets typically include

50-200 stations distributed as evenly as possible over

the Australian continent, and have been subject to

detailed quality control and homogenisation. This

involves identifying and correcting data problems using

statistical techniques, visual checks and station history

information (Nicholls et al., 2006).

The period for which data are available for each

element is largely determined by the availability

of data in digital form. Whilst nearly all Australian

monthly and daily precipitation data have been

digitised, a significant quantity of pre-1957 data

(for temperature) or pre-1987 data (for some other

elements) is yet to be digitised, and so is not currently

available for use in the climate change monitoring. In

the case of temperature, the start date of the datasets

is also determined by major changes in instruments or

observing practices for which no adjustment is feasible

at the present time.

The datasets currently available cover:

> monthly and daily precipitation (most stations

commence 1915 or earlier, with many extending

back to the late 19th century, and a few to the

mid-19th century);

> annual temperature (commences 1910);

> daily temperature (commences 1910, with limited

station coverage pre-1957);

> dewpoint/relative humidity (commences 1957); and

> monthly evaporation (commences 1970).

Datasets covering cloud amount, wind speed and

mean sea level pressure are under development. The

development of a homogenised wind speed dataset is

expected to be particularly challenging because of the

great sensitivity of measured wind speed to changes in

instruments or the local site environment, and a lack

of field comparison studies between different types of

instruments used over the period of record.

The trends based on these datasets, as they become

available, can be found at http://www.bom.gov.au/

cgi-bin/silo/reg/cli_chg/trendmaps.cgi. This site uses

gridded analyses based on the datasets, and also

provides more information about the datasets.

Care does need to be taken with using Australian

climate datasets. For instance, some earlier work

DEPARTMENT OF CLIMATE CHANGE 2008 11


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

reported a substantial decline in precipitation in the

Snowy Mountains, but this was an artifi cial decline

resulting from changes in stations used to calculate

District Average Rainfall (Nicholls, 2000). Viney and

Bates (2004) pointed out that the datasets used in

studies of rainfall events including extremes usually

included stations with unidentifi ed accumulations

(i.e. some daily rainfall reports represented the total

collected over more than a single day), and that this

raised concerns about the findings of these studies.

The possible presence and impact of untagged

accumulations needs to be considered on a case-bycase

basis.

Temperature

The 2004 east Australian heatwave occurred against a

background of a long-term increase in the frequency of

hot days and nights and a decrease in the number of cold

days and nights in Australia (Figure 2; Collins et al. 2000;

Nicholls and Collins, 2006) and more generally across the

western Pacific–eastern Asia region (Manton et al., 2001).

Griffi th et al., (2005) reported almost universal increases

in maximum and minimum mean temperature across the

Asia–Pacific region, along with decreases in the frequency

of cold nights and cool days. Most stations showed an

increase in the frequency of hot days and warm nights,

with only a few significant decreases. Signifi cant decreases

were observed in both maximum and minimum temperature

standard deviation in some coastal Australian stations. For

both maximum and minimum temperature, the dominant

distribution change involved a change in the mean, impacting

on either one or both distribution tails, with no signifi cant

change in standard deviation. Over the 1957 – 2004 period,

the Australian average number of hot days (35ºC or more) per

year has increased by 0.10 days per year, the number of hot

nights (20ºC or more) per year by 0.18 nights per year, while

the number of cold days (15ºC or less) per year has decreased

by 0.14 days per year and cold nights (5ºC or less) per year by

0.15 nights per year. On the longer-term, Stone et al., (1996)

found a decline in the number of frost days in north-east

Australia. Plummer et al. (1999) looked at the frequency of

occurrence of low minimum temperatures across Australia

since 1961, using a high quality daily temperature record.

There has been a 3% decrease of cool nights over Australia

annually, with a 5% decrease in winter. The stations examined

were from small towns or remote locations, so this decrease

presumably does not reflect urbanisation. The strongest

decrease has occurred over the northern parts of the country.

These areas have experienced an apparent increase in cloud

cover that may have contributed to the decline in cold

temperatures and frosts.

12

Figure 2: Australian average number of hot days (daily

maximum temperature ≥ 35°C), cold days (daily maximum

temperature ≤ 15°C), hot nights (daily minimum

temperature ≥ 20°C) and cold nights (daily minimum

temperature ≤ 5°C) per year. Note that annual averages of

extreme events are based on only observation sites that

have recorded at least one extreme event per year for more

than 80% of their years of record. Dashed lines represent

linear lines of best fit. (from Nicholls and Collins, 2006)

More detailed Australian spatial and seasonal analyses of

trends in temperature extremes have been produced by

Alexander et al., (2006b). They showed that annually averaged

maximum and minimum temperatures are increasing across

most of Australia with an associated statistically signifi cant

decrease in the annual occurrence of cold nights (Figure 3a)

and cold days (Figure 3b). All the other temperature indices

show similar spatially coherent trends commensurate with

warming: reductions in frost days and cold spells and an

associated significant increase in all the other temperature

indices, particularly the annual occurrence of warm nights

and warm days (not shown). These results agree well with

Collins et al. (2000) who studied changes in annual extreme

temperature trends up to 1996. The trend in the minimum

temperature and cool nights is in general larger than the

corresponding location for maximum temperature and cool

days. Spatially, the trends in mean maximum and minimum

temperatures are mostly statistically significant in the east

of the continent and are up to 0.4°C per decade, i.e. a total

increase of about 2°C since 1957. In the south-east, the trend

in cool nights is stronger than the underlying warming of the

minimum temperature. Within the south-east region there

are small pockets where the mean minimum temperature has

been decreasing. There are also non-significant decreases in

temperature in the north-west of the continent along with

small increases in the number of cool days and nights.

DEPARTMENT OF CLIMATE CHANGE 2008


Annual results can mask significant seasonal changes so

Alexander et al., (2006b) analysed minimum and maximum

temperatures for four seasons separately. This analysis (Figure

3) demonstrated that decreases in annual mean maximum

temperature in north-west Australia are due to a decrease in

daytime temperature in summer. Cold days are increasing in

this region and warm days are decreasing.

Figure 3a: Seasonal trends (ºC/decade) in mean minimum

temperature (LHS) and mean maximum temperature

(RHS) for 1957-2005. Statistically signifi cant trends

shown in colour. Maps overlaid with annual trends

(%/decade) at each station location with suffi cient data

represented by upward (downward) triangles for increasing

(decreasing) trends for (a), (c), (e) and (f) warm nights

(TN90p) and (b), (d), (f) and (h) warm days (TX90p). Size

of the triangle reflects the magnitude of the trend. Bold

indicates statistically significant change. (from Alexander

et al., 2006b)

Figure 3b: Seasonal trends (ºC/decade) in mean minimum

temperature (LHS) and mean maximum temperature

(RHS) for 1957-2005. Statistically signifi cant trends

shown in colour. Maps overlaid with annual trends

(%/decade) at each station location with suffi cient data

represented by upward (downward) triangles for increasing

(decreasing) trends for (a), (c), (e) and (f) cold nights

(TN10p) and (b), (d), (f) and (h) cold days (TX10p). The size

of the triangle reflects the magnitude of the trend. Bold

indicates statistically significant change. (from Alexander

et al., 2006b)

The cold tails of the probability distributions of minimum

daily temperature are warming faster than the warm tails

of maximum daily temperature in every season (Alexander

et al., 2007), consistent with the results of Trewin (2001). In

general the rate of warming in the cold tails of maximum

temperature distributions is more similar to the warming

trend in the warm tails of maximum temperature than is

the case with minimum temperature. The warming in the

extremes is greater in proportion than the warming in

the mean indicating that the shape and scale of the daily

DEPARTMENT OF CLIMATE CHANGE 2008 13


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

maximum and minimum temperature distribution may

be changing.

This possibility of a changing shape of the distributions

would account for the fact that the very extreme minimum

temperatures have tended to warm much faster than the

mean minimum temperatures. An example is shown in

Figure 4, for Melbourne. Both the coldest night of the

year and the mean minimum temperatures have increased

since 1957, but the rate of increase of the cold extreme

is about twice the rate of increase of the mean minimum

temperature. The same result is exhibited at nearby stations

Wilsons Promontory and Cape Otway, suggesting that the

stronger trend in the extremes is not due to an urban heat

island effect.

Figure 4: Trends in annual mean minimum temperature at

Melbourne and in the temperature of the coldest night of

the year.

Rainfall

Nicholls and Kariko (1993) calculated the number, average

length, and average intensity of rain events at fi ve stations

located in eastern Australia for each year from 1910 to

1988, using daily rainfall totals. A rain event was defi ned

as a period of consecutive days on which rainfall has been

recorded on each day. Annual rainfall variations were

primarily caused by variations in intensity. Fluctuations in the

three rain event variables were essentially independent of

each other. This was due, in some cases, to interrelationships

at interdecadal timescales offsetting relationships of the

opposite sense at shorter timescales. Twentieth century

increases in east Australian rainfall (up to the late 1980s –

14

Nicholls and Lavery, 1992) were due, primarily, to increased

numbers of events. Intensity of rain events had generally

declined, offsetting some of the increase in rainfall that

might have been expected from more frequent events.

Suppiah and Hennessy (1996, 1998) found positive trends in

heavy rainfall from 1910 to 1990 during the summer halfyear,

but only 10-20% of stations had statistically signifi cant

trends. During the winter half-year, heavy rainfall also

increased, except in far south-west Western Australia and

inland Queensland. There was a reduction in the number of

dry days in both halves of the year, except in far south-west

Western Australia and at a few stations in eastern Australia

where there had been an increase in the number of dry days

in the winter half-year. Changes in the number of dry days

were statistically significant at over 50% of stations. There

had been a general decrease in dry days with an increase in

heavy rainfall intensity in the north-east and south-east, and

a decrease in total and heavy rainfall in the south-west.

Haylock and Nicholls (2000) analysed daily rainfall at 91 high

quality stations over eastern and south-western Australia

to determine if extreme rainfall had changed between 1910

and 1998. Three indices of extreme rainfall discussed earlier

were examined with significant results including a decrease

in the extreme frequency and extreme intensity in south-west

Western Australia and an increase in the extreme percent in

New South Wales and Queensland. Total rainfall is strongly

correlated with the extreme frequency and extreme intensity

indices, suggesting that extreme events are more frequent

and intense during years with high rainfall. Due to an

increase in the number of rain days during such years, the

proportional contribution from extreme events to the total

rainfall is not necessarily high.

The most recent examination of trends in extreme

precipitation in Australia (Alexander et al., 2006b) found

that the trends in precipitation totals and extremes vary

throughout the seasons, highlighting the importance of

examining each season rather than just the annual average,

particularly for rainfall (Figure 5). For instance, southern

Queensland (central-east) shows decreasing rainfall trends

in summer and autumn, yet in spring there is an increase in

rainfall through this region. As well, the spatial variability in

precipitation is much greater than for temperature, and in

some places the trends in the means and extremes are not in

the same direction. Most striking is the signifi cant decrease

in both the mean and maximum 1-day rainfall in southeastern

Australia in March-May.

In winter a decline in rainfall in the south-west is evident,

and the totals on the extreme days are also declining

over the last 100 years, however there is a mixed response

more recently. In the last 50 years mean rainfall decreases

are evident down the east coast, and the extremes show

strong declines.

DEPARTMENT OF CLIMATE CHANGE 2008


In spring there was generally little change in the mean

across Australia from 1901-2005, except for some increases

in the centre and east and decreases in the south-west.

The trends in the rainfall total on the day with the maximum

precipitation are increasing almost everywhere, even in the

south-west, indicating that the intensity of the rainfall is

increasing. This signature appears to be present in the most

recent 50 years except that the regions with increases in

the extremes have more definitive increases than the means

while in the south-east and far-west there are decreases

and the magnitude of the maximum 1-day rainfall has

also decreased.

In summer, as in spring, the 1-day maximal rainfall trends

were increasing almost everywhere over the period

1910-2005. These follow the slight increase in the mean

precipitation. The more recent trend shows a very mixed

signal in the means across the continent. In general,

the directions of the trends in the extremes follow the

mean trends, with significant decreases in maximal 1-day

precipitation on the east coast and in the far south-west.

One important feature is the statistically signifi cant

decrease in total rainfall in the east and the increase in

the north-west. As suggested in Nicholls (1997) and Power

(1998), the increase in rainfall since 1950 in the northwest

in summer is associated with a decrease in maximum

temperature. The driver behind the increase in rainfall over

this region is not clear. One suggestion is that the continental

warming further south is driving an enhancement of the

Australian monsoon (Wardle and Smith, 2004), which in turn

may be due to an increase in anthropogenic aerosols over

Asia (Rotstayn et al., 2006).

The focus in most of the work on rainfall extremes in

Australia cited above has been on extremes identifi able from

daily observations. Little work has been done on changes in

extremes on a sub-daily timescale, or on longer (multi-day)

sequences of rainfall extremes. Yet these timescale extremes

can cause enormous damage e.g. through fl ash fl ooding.

Figure 5: Seasonal trends (%/decade) in mean rainfall

for 1910-2005 (LHS) and 1951-2005 (RHS). Statistically

significant trends shown in colour. Maps overlaid with

annual trends (%/decade) at each station location with

sufficient data represented by upward (downward) triangles

for increasing (decreasing) trends for (a)-(h) seasonal

maximum 1-day precipitation totals (RX1day). The size

of the triangle reflects the magnitude of the trend. Bold

indicates statistically significant change. (from Alexander

et al., 2006b).

Tropical cyclones, extra-tropical systems, strong

winds, and hail

Nicholls et al. (1998) showed that the number of tropical

cyclones observed in the Australian region (south of equator;

105-160°E) had apparently declined since the start of reliable

(satellite) observations in the 1969/70 season (although

note that Kuleshov, 2003, found that data reliability was

lower prior to 1980). However, the number of more intense

cyclones (with minimum pressures dropping to 970 hPa or

lower) had increased slightly while the numbers of weak

(minimum pressures not dropping below 990 hPa) and

moderate systems (minimum pressures between 970 and

990 hPa) had declined. The decline in the number of weaker

DEPARTMENT OF CLIMATE CHANGE 2008 15


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

cyclones partly reflects changes in which systems are

considered as tropical cyclones. The decline in the number

of cyclones more intense than 990 hPa primarily refl ects

the downward trend in the Southern Oscillation Index (SOI).

Previous work has demonstrated that the number of tropical

cyclones observed in the Australian region each cyclone

season is related to the value of the SOI prior to the start

of the cyclone season. This relationship is clearest with the

number of moderate cyclones. The SOI is only weakly related

to the number of intense or weak cyclones. The increase in

the number of intense cyclones is not attributable to the

trend in the SOI. Recent work (Hennessy, 2004; John McBride,

pers. comm.) suggests that the increase in the frequency of

intense tropical cyclones noted by Nicholls et al., (1998) has

not continued, although such an increase appears to have

occurred in other ocean basins (Webster et al., 2005). It is

still not clear whether the historical database is suffi ciently

accurate to credibly diagnose multi-decadal trends, because

of changes in observing systems (e.g. satellite imagery) and

techniques for diagnosing cyclone intensity.

Mid-latitude westerly winds appear to have increased in both

hemispheres, related to changes in the so-called “annular

modes” (related to the zonally averaged mid-latitude

westerlies) which have strengthened in most seasons from

1979 to the late 1990s, with poleward displacements of

corresponding jetstreams and enhanced storm tracks. These

have been accompanied by a tendency toward stronger

wintertime polar vortices throughout the troposphere and

lower stratosphere. Significant decreases in cyclone numbers,

and increases in mean cyclone radius and depth over the

southern extra-tropics have occurred over the last two or

three decades (Simmonds et al., 2003).

Trends in wind speed are an important aspect of climate

change and variability (Nicholls et al., 2000). These trends are

difficult to determine directly, as records of wind speed at

any given station are highly sensitive to changes in the local

environment (e.g. buildings erected in the vicinity) as well as

to systematic changes arising from altered instrument types.

Wind speed can also vary greatly over short intervals in both

space and time. This makes it difficult to verify the validity

of any given observation at a station. The field of sea level

atmospheric pressure is much more coherent in space and

time, and is more suited to validity checks. Nicholls et al.,

(2000) used pressure gradients as a surrogate for windiness.

The pressure gradient is the major influence on the largescale

wind field. Only locations in Bass Strait could be used

for this, because of the specific data needs. The windiness

index seemed most appropriate for coastal regions, but a

network of stations recording pressure is needed to estimate

windiness. Bass Strait was one ocean situation where

sufficient data were readily available to allow the appropriate

calculations. There are eight stations in or bordering Bass

Strait with daily pressure records over most of the last 40

16

years. The starting point of this study was 1957, as it is the

starting point of daily data in digital form at the majority

of stations. Nicholls et al., noted a marked fall in pressure

gradient (and thus in wind speed) over eastern Bass Strait,

offset to some extent by a slight rise in the west. This is more

marked in summer than winter. Trends in the direct wind

measurements at Flinders Island and at King Island support

these findings. Considerably more work is needed to produce

a dataset useful for determining trends in wind speed across

Australia, especially extreme winds.

Small-scale phenomena such as tornadoes and hail are

very difficult to monitor over the long periods required to

diagnose possible changes in frequency or intensity. Schuster

et al., (2005) document how improvements in monitoring

networks have led to apparent massive increases in the

recording of hail over New South Wales since European

colonisation. They do report, however, a decline of about

30% in the number of hailstorms affecting Sydney in the

period 1989-2002 compared with 1953-1988. This decline is

presumably not reflecting a change in monitoring systems.

One way around the problems with monitoring small-scale

extremes is to use downscaling, to relate the small-scale

systems to larger-scale circulation (which should be more

consistently monitored). Kounkou and Timbal (2006) used

a downscaling tool to analyse cool-season tornadoes and

their likely changes. The tool was used to detect areas

over Australia where cool season tornadoes (CST) are

likely to occur. It is based on the analysis of two particular

parameters: the 700 hPa surface lifted index and the vertical

wind shear between 850 hPa and the surface. There has been

a marked increase in the risk as diagnosed from the reanalyses

since 1979.

Droughts

Droughts have been widespread in various parts of the

world since the 1970s (Dai et al., 2004). Some droughts seem

to be influenced by changes in SSTs, especially in Africa

and western North America, and through changes in the

atmospheric circulation and precipitation in central and

south-west Asia.

In Australia and Europe, direct relationships to global

warming have been inferred through the extreme nature

of high temperatures and heatwaves accompanying recent

droughts (Nicholls, 2004). Recent Australian droughts, in

general, were no worse, in terms of total precipitation, than

were earlier droughts. The driest period, across Australia,

since the start of the 20th century was the 1930s and early

1940s. However, temperatures have been higher in the more

recent droughts. Thus mean maximum temperatures were

very high during the 2002 drought, as was evaporation.

This would suggest that drought conditions (precipitation

minus evaporation) were worse than in previous recent

DEPARTMENT OF CLIMATE CHANGE 2008


periods with similarly low rainfall (1982, 1994). Mean

minimum temperatures were also much higher during

the 2002 drought than in the 1982 and 1994 droughts.

The relatively warm temperatures in 2002 were partly the

result of a continued warming evident in Australia since the

middle of the 20 th century. The possibility that the enhanced

greenhouse effect is increasing the severity of Australian

droughts, by raising temperatures and hence increasing

evaporation, even if the rainfall does not decrease, needs

to be considered. Studies of possible trends in Australian

droughts are complicated by the lack of information about

trends in soil moisture – in the absence of such information

droughts are usually diagnosed simply by rainfall defi ciencies.

Nicholls et al., (2000) examined an index combining the area

of the country in drought (i.e. below 10 th percentile, based

on annual rainfall) with that in wet conditions (i.e. above

90 th percentile) and thereby showed how extreme, in terms

of widespread precipitation, a particular year is. If Australia

were tending to have more “droughts and fl oods” there

would be a positive trend apparent in this index. There was

no obvious long-term trend. However, Burke et al., (2006)

did report a trend towards increased Palmer Drought

Severity Index in eastern and south-western Australia over

the period 1952-1998.

Sea level

Relative sea level rise around Australia averaged 1.2 mm/year

from 1920 to 2000. There are only two Australian records of

sufficient length to allow a credible examination of changes

in the frequency of extreme sea level events, Fremantle

(data available since 1897) and Fort Denison, Sydney (data

available since 1914). Church et al., (2006) reported that at

both locations extreme sea level recurrence intervals were

typically three times shorter after 1950 than in the pre-1950

period. They also found evidence that the extreme sea level

events were rising faster than mean sea level.

WHAT HAS CAUSED

THESE CHANGES IN

EXTREMES?

Can we blame climate change for the extreme southern

Queensland heatwave of February 2004, or for other

extremes? The attribution of a single event to climate change

might never be possible, because almost any weather event

might occur by chance, in a climate unmodified by human

behaviour as well as in a changed climate. However, the risk

of specific extreme events occurring (e.g. a heatwave) may be

changed by human influences on climate.

Recently, the first attempts to determine whether any

changes in extremes could be attributed to human

interference with the atmosphere have been reported. Stott

et al., (2004) investigated the extent to which climate change

could be responsible for the high summer temperatures in

Europe during the summer of 2003 over continental Europe

and the Mediterranean. They concluded that it is very likely

that human influence had more than doubled the risk of a

regional scale heatwave like the 2003 event. This was a study

of a regional-average of summer mean temperatures. The

first attempts at attributing changes in extremes based on

daily data (rather than extremes of seasonal means) have

also been undertaken. Christidis et al., (2005) analysed a

new gridded dataset of daily temperature data (Caesar et al.,

2006) and detected robust anthropogenic changes in indices

of extremely warm nights, although with some indications

that the model overestimates the observed warming of warm

nights. Human influence on cold days and nights was also

detected, although less convincingly.

DEPARTMENT OF CLIMATE CHANGE 2008 17


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

Box 4: How well do climate models

simulate extremes?

Since most extreme events are of relatively small

spatial scale, and relatively short in duration, it

seems likely that relatively coarse resolution climate

models would be unable to adequately simulate their

characteristics. Surprisingly, this is not the case, at

least with the modern climate models. Thus Kharin

et al., (2005) note “On the whole, the AGCMs appear

to simulate temperature extremes reasonably well”

which also support the conclusions from Kiktev et al.,

(2003). Vavrus et al., (2005) found that climate models

reproduced the location and strength of cold air

outbreaks. The models have less success in simulating

some other extremes. Sun et al., (2006) investigated

the simulation of daily precipitation and reported that

most models underestimate the intensity of heavy

precipitation events, and simulate too many days of

light precipitation. Emori et al., (2005) showed that

models could realistically simulate daily precipitation if

some restrictions are applied to their parameterisation

schemes. Burke et al., (2006) show that a climate

model can simulate the observed trend in the Palmer

Drought Severity Index. Finally, the spatial resolution of

coupled models is generally not high enough to resolve

tropical cyclones and to simulate their intensity. To

overcome this problem a common approach has been

to use a high-resolution atmospheric model forced

by changes in sea surface temperatures. Bengtsson

et al., (2006) show that at least one model broadly

reproduces the global features of tropical cyclones.

Haylock et al., (2005) examined the ability of

statistical and dynamical downscaling of simulations

by climate models to simulate heavy precipitation.

They found that no one downscaling system, or type

of downscaling, consistently outperformed the others.

They argued for the use of “as many different types of

downscaling models, GCMs and emission scenarios as

possible when developing climate change projections

at the local scale.”

It is possible to argue that human influences are increasing

the likelihood of heatwaves in Australia. Stott (2003)

compared simulations from the UK Hadley Centre coupled

model with observed near-surface temperatures over land,

for continental-scale regions including Australia. The model,

when forced with natural and anthropogenic changes in

forcing factors did an excellent job reproducing the trends

since about 1950. Stott’s study indicated that greenhouse

gases were causing warming in Australia through the

second half of the 20 th century. Stott examined only mean

18

temperatures, but since extreme temperatures have been

increasing similarly to mean temperatures (Griffi ths et al.,

2005) it is reasonable to extrapolate his conclusions to the

frequency of extremes, and to conclude that the enhanced

greenhouse effect is likely contributing to the observed

increased frequency of hot days and nights. An analysis of

temperature records in Australia (Jones and Fawcett, 2004)

suggests that the rate at which extremely hot conditions are

being observed is being inflated by global warming.

Arblaster and Alexander (2005) combined the results

from Alexander et al., (2006a) and Tebaldi et al., (2006) by

comparing observed changes in extremes across the globe,

with the extremes in 20th century simulations in models

forced with historical external forcings. The observed and

simulated trends generally agreed quite well, especially

in the case of increases in the frequency of warm nights

(statistically significant over southern Australia in both

models and observations) and decreases in the frequency

of frost days over southern Australia. There was less clear

similarity between observed and simulated changes in the

frequency of heavy precipitation events over Australia,

although globally there was a tendency in both observations

and simulations for increased frequency of heavy rainfall

events at mid-latitudes.

DEPARTMENT OF CLIMATE CHANGE 2008


HOW WILL EXTREMES

CHANGE IN THE

FUTURE?

Kharin and Zwiers (2005) examined global projected changes

in temperature and precipitation extremes in transient

climate change simulations performed with the second

generation coupled global climate model of the Canadian

Centre for Climate Modelling and Analysis. Three-member

ensembles were produced for the period 1990–2100

using the IS92a, A2, and B2 emission scenarios of the

Intergovernmental Panel on Climate Change. Changes in

temperature extremes over most of the globe are largely

associated with changes in the location of the distribution

of annual extremes without substantial changes in its

shape. Globally averaged changes in warm extremes are

comparable to the corresponding changes in annual mean

daily maximum temperature, while globally averaged cold

extremes warm faster than annual mean daily minimum

temperature. This latter effect occurs because changes in

extremely cold temperatures are amplified by the surface

albedo feedback in regions that are covered with snow in

winter, such as Europe, North America and the Arctic. With

global warming, the snow cover retreats in these areas,

exposing a lower albedo surface, which in turn accelerates

warming at the surface. However, a notable exception is

Australia, where such feedback would not operate. Here

the mean minimum temperature was projected to increase

at double the rate of the extreme minimum temperatures

(defined from a 20-year return period). Note that this is the

opposite of the situation observed at Melbourne in recent

decades, where the extreme minimum temperatures have

warmed faster than the mean minimum (Figure 4).

Kharin and Zweirs (2005) also examined extreme

precipitation events. They found in their model

integrations that changes in precipitation extremes occurred

as a result of changes in both the location and scale of the

extreme value distribution and the changes in the extremes

exceeded substantially the corresponding changes in the

annual mean precipitation. In Australia, their model

projected little if any change in mean precipitation, but

increases of 5-10% in the 20-year return value of daily

rainfall. The probability of precipitation events that are

considered extreme at the beginning of the simulations

is increased by a factor of about 2 by the end of the 21st century everywhere (including Australia).

Tebaldi et al., (2006) reported that the trends in temperature

extremes that began to be detected above the noise in the

late 20th century (e.g. Christidis et al., 2005) are projected to

continue and intensify into the future, regardless of which

IPCC scenario is followed. Spatial patterns of projected

changes in temperature extremes are very stable, and the

pattern increases in amplitude as the rate of emissions

increases. Models also project a trend towards a world

characterised by intensified precipitation, with a greater

frequency of heavy precipitation events and longer dry

spells, although with substantial geographical variability

and more inter-model differences than is the case with the

temperature extremes projections.

Projected changes in Australian extremes with enhanced

greenhouse gases include:

> 5-50% increase in numbers of days over 35ºC by 2030

(Suppiah et al., 2006).

> 10-80% decrease in frequency of days below 0ºC by 2030

(Suppiah et al., 2006).

> General increases in rainfall intensity (McInnes et al., 2002;

Whetton et al., 2002; Walsh et al., 2001; Abbs, 2004; Abbs

et al., 2006) but with considerable spatial variation.

> Decrease in numbers of tropical cyclones, accompanied

by an increase in intensity (Abbs et al., 2006; Walsh et

al., 2004). The frequency of severe tropical cyclones

(Categories 3, 4 and 5) on the east Australian coast is

simulated to increase 22% for the IS92a greenhouse gas

scenario from 2000-2050, with a 200 km southward shift

in the cyclone genesis region, leading to greater exposure

in south-east Queensland and north-east NSW.

> Decreased hail frequency in Melbourne and Mt Gambier

(Niall and Walsh, 2005).

> 20% increase in large hail (2cm diameter) and 40%

reduction in average recurrent interval for hail exceeding

6cm diameter in Sydney (Leslie et al., 2006).

> Up to 20% more droughts over most of Australia by 2030

(Mpelesoka et al., submitted). Projected changes in the

Palmer Drought Severity Index for the SRES A2 scenario

indicate an increase over much of eastern Australia

between 2000 and 2046.

However, the considerable uncertainty that is associated with

these projections needs to be recognised. For instance, the

tropical cyclone projections are based on only two climate

simulations, for one scenario of greenhouse gas emissions.

Until more simulations can be performed, with a wider range

of climate models and scenarios, and with improved climate

models, the above-quoted projections should be considered

indicative at best.

Extreme sea level events can be expected to increase,

as mean sea level increases (Church et al., 2006) due to

thermal expansion and glacier and ice-sheet melting. For

the expected mid-level rise in mean sea level over the 21st century the logarithmic relationship between sea level rise

DEPARTMENT OF CLIMATE CHANGE 2008 19


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

and increases in the frequency of extremes may mean that

regions that were inundated only once per year may become

semi-permanently under water. Changes in storminess may

enhance or offset the changes in extreme events resulting

from increases in mean sea level.

Weather conducive to bushfires seems likely to increase in

the future also. Hennessy et al., (2006) used daily-observed

weather variables to calculate fire danger indices for various

Australian locations, for current conditions. They then

applied daily weather data simulated by a climate model

run with an enhanced greenhouse gas situation. They found

that an increase in fire-weather risk was likely at most

sites, including an increase in the number of days when the

fire danger rating was extreme. For example, their results

indicated that Canberra was likely to have an annual average

of 28-38 days of very high or extreme fire danger by 2050,

compared with the current average of 23 days. Tasmania was

found to be relatively unaffected by this intensifi cation of

fire danger, possibly reflecting the fact that rainfall variations

are a strong determinant of Tasmanian bushfi re behaviour

(Nicholls and Lucas, 2007) and so unless rainfall decreases

then a substantial increase in fire danger may be less likely.

20

WHAT NEEDS TO BE

DONE?

Most of the extremes discussed in the previous section lead

to inconclusive results regarding observed trends, because

of concerns about the quality, comprehensiveness, and

comparability of data over decades. The major exception

is for extreme temperatures, where extensive work and

international cooperation over the past decade or so has

led to a clear depiction of increasing warm extremes and

decreasing cold extremes (and some studies now attribute

these changes in extremes to human influences on the

atmosphere). But for all the other extremes (droughts,

heavy rainfalls, cyclones, tornadoes etc.) the data concerns

overwhelm us, still. Is there a way forward? For some

extremes the answer is, of course, “Yes”. Tropical cyclones

have been observed with satellites since before 1970.

Although the satellite technology has changed, along with

the methods used to determine the intensity of the systems,

it should still be possible to examine the historical satellite

pictures to determine whether, for instance, tropical cyclones

in the mid-1970s were routinely analysed as moderate

rather than intense. This effect would need to be very clear,

if it were strong enough to be able to account for the

substantial apparent trend towards more frequent intense

cyclones (Webster et al., 2005). For small-scale events such

as tornadoes, focusing on areas where tornadoes have been

monitored for several decades, and where there has been a

sufficient population to ensure that systems are not missed,

might be the way forward (rather than relying on collating

numbers of systems from all areas including those where

tornado monitoring is a new concept).

For some other extremes (most notably drought) the problem

is more definitional – Dai et al., (2004) and Burke et al.,

(2006) use the Palmer Drought Severity Index (PDSI) to

examine changes in droughts. Is this the most appropriate

index, and how much of an apparent trend is due to the

temperature term in this index? Critics of the PDSI (e.g. Alley,

1984) suggest that it is of insufficient complexity to account

accurately for the wide range of environmental conditions

that may in reality occur such as, frozen soil, snow, and the

presence of roots or vegetation. Therefore the calculated soil

moisture is inferior and should not be used as a measure of

hydrological drought.

Few formal detection and attribution studies have been

applied to extremes, as yet. This is partly because of

concerns regarding the quality of the historical data, or its

completeness. But in Australia these two concerns are, for

many extremes, not a serious problem. Also, model runs

required for such studies have been completed as part of

the IPCC Fourth Assessment (e.g. Arblaster and Alexander,

DEPARTMENT OF CLIMATE CHANGE 2008


2005). So, formal detection and attribution studies could be

applied, without too much difficulty, to at least temperature

extremes and precipitation extremes, for Australia. This could

answer the question “Has human interference with the global

atmosphere led to changes in the frequency and/or intensity

of extreme weather over Australia?”.

These questions need to be answered specifically for a variety

of extremes, notably:

> Extreme temperature

> Heavy precipitation events (and floods and hail)

> Tropical cyclones

> Strong winds

> Droughts

> High sea level events

> Small-scale extremes (e.g. severe thunderstorms, hail

and tornadoes).

The data availability will vary between these different

extremes, as will our ability to model and predict their

frequency or intensity. Also, the various combinations of

these extremes (e.g. the frequency with which strong winds

occur in concert with high sea level) need to be considered.

Specific questions for each of these extremes would include:

> Is this extreme changing in frequency or intensity?

> Is it likely to change in the future?

> What are the gaps in knowledge about this extreme?

> How do we fill these gaps?

In turn, these questions will need to be addressed by a

variety of approaches, requiring improvements in data and

modelling, as well as fundamental understanding of the

causes of these extremes.

Some specific needs for future work for Australian

extremes include:

> A reanalysis of tropical cyclone data, to facilitate

comparisons of the frequency and intensity of current-day

cyclones with those in the past.

> Analysis of historical changes in drought frequency,

intensity and duration, using multiple drought indices, e.g.

rainfall deficiency, standardised precipitation index, soil

moisture deficit, Palmer Drought Severity Index. Climate

change projections are required for the same indices,

including estimation of drought return periods relevant for

assessment of Exceptional Circumstances.

> A regional reanalysis, including homogenisation of upper

air data, to facilitate studies linking specific extremes with

synoptic patterns.

> Improved downscaling of small-scale synoptic events

such as tornadoes and thunderstorms that are diffi cult to

monitor using conventional meteorological networks and

approaches, to facilitate an increased focus on studies of

small-scale events.

> Improved climate models, with higher resolution and

improved parameterisation of small-scale processes that

lead to extremes. This will require involvement of the

user community in the design of ACCESS (the Australian

Community Climate Earth System Simulator).

> Development of high quality historical datasets for wind

speed and hail, to facilitate documentation of any trends in

these extremes.

> Improved historical datasets of rainfall and temperature

should include the effects of urban heating, rather than

removing such effects. An increased emphasis is also

required on sub-daily precipitation extremes, and the

analysis of historical changes in extremes would be

facilitated by increased palaeo-climatic emphasis on

extreme events.

> Joint analyses of multiple extremes (e.g. strong winds and

heavy rainfall) that might exacerbate the impacts either

extreme would have on its own.

> Studies to determine how much of the recent trends in

extreme temperatures is attributable to human actions, and

how this varies seasonally and spatially.

> A comprehensive assessment of projected changes in

extreme daily temperature, rainfall, wind, fi re danger,

tropical cyclones, hail, tornadoes and storm surges. To

ensure internal consistency, this would require a suite of

simulations from selected climate models that perform well

in the Australian region.

> Integrated assessments to determine how communities

could or should react to changes in extremes.

Finally, what needs to be done to reduce the likely impacts

of any changes in extreme weather? As Lynch (2004) notes,

“Australia is facing increasing losses from extreme climate

events, such as more intense hail storms, or more frequent

droughts and fires”. Are such extremes a “dangerous”

interference with the climate? Since heatwaves lead to

human casualties, and since human influences do appear

to be causing increases in the frequency of Australian

heatwaves, can we conclude that we have already reached

a point of “dangerous” interference with the climate? This

depends on the perspective of the affected community

– what might not be considered dangerous to Australia

considered as a single entity might be extremely dangerous

to specific local communities, such as those exposed to an

increased fire risk. Lynch et al., (2004) note that: “Involving

local residents in the integrated assessment of the impacts

DEPARTMENT OF CLIMATE CHANGE 2008 21


AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

of climate change on their community redirects the project

focus to extreme events”. So, an assessment of whether

a particular human action leads to dangerous climate

change will require integrated assessment focused on local

communities, and this, in turn, will lead to an enhanced

focus on the possible increased risks of extremes such as

the February 2004 heatwave. An essential first step is to

investigate how to link the science of extremes with impact

assessment – is a case study approach all that is required?

And if we are to prioritise, in order to focus on the most

“important” extremes, a listing of, for all possible extreme

events, of risk (= hazard + exposure + vulnerability) is

essential. This is also essential if we are to ensure that we

focus on the extremes that deserve most attention.

22

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DEPARTMENT OF CLIMATE CHANGE 2008


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