Australia climate and weather extremes: past, present and future

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Neville Nicholls A Report for the Department of Climate Change January 2008

AUSTRALIAN CLIMATE and WEATHER EXTREMES: PAST, PRESENT AND FUTURE

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Neville Nicholls A Report for the Department of Climate Change January 2008

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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.

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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

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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 upper-air 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



> 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.

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.



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.




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 heat-related 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 non-meteorological 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 degree1 .

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 (<1ºC) temperature. The increase in mean minimum temperature has led to a substantial decrease in the probability of temperatures <1ºC (mean number of days <1ºC was 1.7 per year in fi rst period, declining to 0.7 per year in second period). Nicholls and Alexander (2007).

1 Note that much of the warming in both means and extremes in Figure 1 probably reflects urban heating effects that can be very strong in winter minimum temperatures. The figure is included to illustrate that a small change in the mean of a distribution can lead to large changes in the frequency of extremes, rather than to attribute any change to a specifi c cause.



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




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 range

Monthly mean difference between daily maximum and minimum temperature

ºC

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 indicator

Annual count when at least 6 consecutive days of maximum temperature > 90th percentile

days

CSDI* Cold spell duration indicator

Annual count when at least 6 consecutive days of minimum temperature < 10th percentile

days

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 index

The ratio of the number of wet days (> 1mm) to annual total precipitation

mm/day

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)



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).




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 20th 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.



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.




Phenomenona and direction of trend

Likelihood that trend occurred in late 20th century (typically post 1960)

Likelihood of discernible human infl uence on observed trendb

Likelihood of future trend based on projections for 21st century using SRES scenarios

D

Warmer/fewer cold days/ nights over most land areas

Very likelyc Likelye * Virtually certaind

Warmer & more frequent hot days/nights over most land areas

Very likelyd Likely (nights) e * Virtually certaind

Warm spells / heat waves. Frequency increases over most land areas

Likely More likely than notf

Very likely

Heavy precipitation events. Frequency (or proportion of total rainfall from heavy falls) increases over most areas

Likely More likely than notf

Very likely

Area affected by droughts increases

Likely in many regions since 1970s

More likely than not

* Likely

Intense tropical cyclone activity increases

Likely in some regions since 1970

More likely than notf

Likely

Increased incidence of extreme high sea level (excludes tsunamis) g

Likely More likely than notf,h

Likelyi

Table 2: Trends, attribution and projections of global extreme weather and climate events. Only extremes for which there is evidence of an observed late 20th 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 <99%; “likely” means >66% but <90%; “more likely than not” means >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)



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




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.

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.



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




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 –

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 half-year, 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.



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




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 large-scale 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

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



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 20th 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 10th percentile, based on annual rainfall) with that in wet conditions (i.e. above 90th 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.




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 20th century. Stott examined only mean

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.



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




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.

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,



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




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.

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Neville NichollsA Report for the DeJanuary 2008

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Australia climate and weather extremes: past, present and future

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