ArticlePDF Available
The impact of climate change on
snow conditions in mainland Australia
Kevin Hennessy, Penny Whetton, Ian Smith, Janice Bathols,
Michael Hutchinson and Jason Sharples
August 2003
i
The impact of climate change on snow conditions in
mainland Australia
Kevin Hennessy, Penny Whetton, Ian Smith, Janice Bathols,
Michael Hutchinson* and Jason Sharples*
Published by CSIRO Atmospheric Research, Aspendale, Victoria, Australia.
A report for the Victorian Department of Sustainability and Environment, Victorian Greenhouse
Office, Parks Victoria, New South Wales National Parks and Wildlife Service, New South Wales
Department of Infrastructure, Planning and Natural Resources, Australian Greenhouse Office and
Australian Ski Areas Association.
CSIRO 2003
* Australian National University Centre for Resource and Environmental Studies
Address for correspondence:
Mr Kevin Hennessy
CSIRO Atmospheric Research
PMB 1
Aspendale Victoria 3195
Telephone: (03) 9239 4536
Fax: (03) 9239 4444
E-mail: Kevin.Hennessy@csiro.au
CSIRO Atmospheric Research (http://www.dar.csiro.au) conducts research into weather, climate
and atmospheric pollution, concentrating on environmental issues affecting Australia. Our research
is directed toward meeting the needs of government, industry and the community. We address
issues such as urban and regional air pollution, acid deposition, the enhanced greenhouse effect,
ozone depletion, climatic variability and severe weather. See
http://www.dar.csiro.au/division/docs/DivisionalBrochure.pdf.
The Australian National University Centre for Resource and Environmental Studies
(http://cres.anu.edu.au) addresses resource and environmental issues of national and international
importance through the development and application of interdisciplinary concepts, theories and
methods involving biophysical and socio-economic dimensions. CRES undertakes research and
postgraduate training and conducts consultancies for industry and all levels of government.
Disclaimer
The projections in this report are based on results from computer models that involve
simplifications of real physical processes that are not fully understood. Accordingly, no
responsibility will be accepted by CSIRO for the accuracy of the projections inferred from this
report or for any person’s interpretations, conclusions or actions based on this information.
ii
Contents
Executive Summary 1
Summary 3
Introduction 8
Analysis of alpine climate trends since the mid 1950s 10
Projected changes in natural snow conditions 16
Projected changes in snow making requirements 28
Conclusions 37
Research needed to address gaps in knowledge 38
Acknowledgements 39
References 39
Appendix 1 Methodology: Collection and preparation of
observed climate databases
41
Appendix 2 Intergovernmental Panel on Climate Change
(IPCC) scenarios on global warming
45
1
Executive Summary
The primary aim of this study was to improve the current understanding of the impacts of past and
future climate change on natural snow cover in Australia. A secondary aim was to assess the role of
snow-making in countering projected changes in snow conditions.
Past changes in natural snow cover
Trends in alpine temperature, precipitation and snow depth were analysed over regions or sites for
which data were available. Alpine temperature data at four sites over the past 35 years revealed that
warming trends are slightly greater at higher elevations. Alpine precipitation data for the past 50
years showed evidence of small increases in New South Wales, and small decreases in Victoria.
Snow depth data from four alpine sites from 1957-2002 indicated a weak decline in maximum snow
depths at three sites. A moderate decline in mid-late season snow depths (August-September) was
evident at three sites. This may reflect the tendency for mid-late season snow depth to be driven by
ablation (melt and evaporation) while early season snow depth is precipitation driven.
Future changes in natural snow cover
Simulations of future snow conditions in the Australian alpine regions were prepared for the years
2020 and 2050, based on climate change projections published by CSIRO in 2001. A new climate-
driven snow model was developed and applied to this study.
The results for 2020 are of greatest relevance to future management of both ski resorts and sites of
biological significance due to the much smaller range of uncertainty associated with the projected
changes in temperature and precipitation. Information for 2050 provides some indication of the
future but is associated with a far greater range of uncertainty.
Two scenarios were used in the model, both of which were equally likely, but associated with
uncertainties. The low impact scenario used the lowest projected warming combined with the
highest estimate of increased precipitation. The high impact scenario used the highest projected
warming with the highest estimate of decreased precipitation. We have very high (at least 95%)
confidence that the low impact limits will be exceeded and that the high impact limits will not be
exceeded.
Under the low impact and the high impact scenarios respectively, the total alpine area with an
average of at least one day of snow cover decreases 10-39% by 2020, and 22-85% by 2050. The
area with at least 30 days of snow cover decreases 14-54% by 2020, and 30-93% by 2050. The area
with at least 60 days of cover shrinks 18-60% by 2020, and 38-96% by 2050.
At all sites, the low impact scenario for 2020 only has a minor impact on snow conditions. Average
season lengths are reduced by around five days. Reductions in peak depths are usually less than
10%, but can be larger at low sites (e.g. Mt Baw Baw and Wellington High Plains). The high impact
scenario for 2020 leads to reductions of 30-40 days in average season lengths. At higher sites such
as Mt Hotham, this can represent reductions in season duration of about 25%, but at lower sites
such as Mt Baw Baw the reduction can be more significant (up to 60%). Impacts on peak depth
follow a similar pattern: moderate impacts at higher elevation sites, large impacts at lower elevation
sites. There is also a tendency for the time of maximum snow depth to occur earlier in the season
under warmer conditions. For example, the results for Thredbo show this occurring about 20 days
earlier under the high impact scenario.
2
Future requirements for snow-making by 2020
Snow-making at ski resorts will be one of the major ways of adapting to greenhouse warming. We
assessed the effect of warmer conditions on the number of hours suitable for snow-making by 2020,
and the Potential Volume of snow that could be made using two types of snow-guns (Brand A and
Brand B, names withheld for commercial reasons) at each resort. The average number of hours
suitable for snow-making declines by 2-7% for the low impact scenario and by 17-54% for the high
impact scenario. The potential snow-making volume is reduced by 3-10% under the low impact
scenario, and by 18-55% under the high impact scenario.
Based on target snow-depth profiles for May to September nominated by snow-making managers at
each resort, the snow model was able to simulate the amount of man-made snow required, taking
into account natural snowfall, snow-melt and the pre-existing natural snow depth. We computed the
Target Volume of man-made snow required over a typical ski run at each resort to achieve the
target profile in 90% of years.
Using information about the Potential Volumes of snow that could be made, the number of snow-
guns needed to achieve the Target Volumes was estimated, under present and 2020 conditions. The
results are significantly influenced by the elevations at which snow-making hours were computed,
i.e. 1340 metres at Lake Mountain and Mt Thredbo, 1460 metres at Mt Baw Baw, 1550 metres at
Mt Selwyn, 1642 metres at Falls Creek and 1720 metres at Mt Buller and Mt Perisher. About one
Brand A gun per ski run is needed at Mt Perisher under present conditions, 1.8 at Falls Creek,
almost three at Mt Selwyn and Mt Buller, and 15 at Lake Mountain. An increase of 11-24% in the
number of Brand A snow-guns would be required for the low impact scenario, and 73-200% for the
high impact scenario. Brand B snow-guns produce slightly less snow than Brand A. Under present
conditions, about one Brand B gun per ski run is needed at Mt Perisher, 2.6 at Falls Creek and Mt
Thredbo, 4.2 at Mt Selwyn and Mt Buller, and 21 at Lake Mountain. An increase of 11-27% in the
number of these snow-guns would be required for the low impact scenario, and 71-188% for the
high impact scenario. Therefore, with sufficient investment in snow-guns, the Australian ski
industry will be able to manage the impact of projected climate change on snow cover until at least
2020, bearing in mind the limitations outlined below.
This study required some simplifying assumptions (e.g. only two snow-guns, operated
automatically) and exclusion of various factors that were not easily included in our model, i.e:
likely improvements in snow-making technology;
continuing improvements in snow-making operations such as:
optimized snow-gun start-up temperatures;
management of the number of pumps and pressure gradients to minimize water heating;
increased efficiency of water cooling systems;
snow-plume placement;
elevation of snow guns on towers;
additives to enhance conversion of water to snow;
snow grooming and snow-farming.
the effect of cold air drainage on snow-making capacity at lower elevations;
the effect of topographic aspect on natural snow deposition;
less rapid snow-melt rate for man-made snow relative to natural snow;
possible water-supply limitations due to projected reductions in precipitation and increased
evaporation in south-east Australia;
acceptable levels of environmental impact, e.g. likely increase in demand for water and energy
due to increased snow-making.
3
Summary
Scope of this report
This assessment of past and future changes in snow conditions was prepared by CSIRO and the
Australian National University (ANU) based on completion of the following tasks:
1. Collection and preparation of climate databases;
2. Analysis of databases for trends in alpine conditions;
3. Modification of CSIRO’s existing snow model and its application to the estimation of
natural snow cover by the years 2020 and 2050;
4. Preliminary assessment of the implications of the findings for future snow-making.
Aims of this study
The primary aim of this study was to improve the current understanding of the impacts of past and
future climate change on natural snow cover in Australia.
A secondary aim was to assess the role of snow-making as an adaptive response in countering
projected changes in snow conditions.
Past changes in natural snow cover
Trends in alpine temperature, precipitation and snow depth were analysed over regions or sites for
which data were available. Alpine temperature data at four sites over the past 35 years revealed that
warming trends are slightly greater at higher elevations. Alpine precipitation data for the past 50
years showed evidence of small increases in New South Wales, and small decreases in Victoria.
Snow depth data from four alpine sites from 1957-2002 indicated a weak decline in maximum snow
depths at three sites. A moderate decline in mid-late season snow depths (August-September) was
evident at three sites. This may reflect the tendency for mid-late season snow depth to be driven by
ablation (melt and evaporation) while early season snow depth is precipitation driven.
Climate change projections used in this study
The most recent projections for climate change in Australia were released by CSIRO in May 2001.
Application of these projections in CSIRO’s snow model has provided estimates of changes in
snow conditions for 2020 and 2050 under two different scenarios, 'low impact' and 'high impact',
with projected changes in temperature and precipitation as described in Table 1S. The low impact
scenario used the lowest projected warming combined with the highest estimate of increased
precipitation. The high impact scenario used the highest projected warming with the highest
estimate of decreased precipitation.
4
Table 1S: Changes in alpine temperature and precipitation for 2020 and 2050, relative to 1990.
Scenario
(Year)
Projected Change
in Temperature
(°C)
Projected Change in
Precipitation
(%)
Low impact (2020) +0.2 +0.9
High Impact (2020) +1.0 -8.3
Low impact (2050) +0.6 +2.3
High impact (2050) +2.9 -24.0
It should be noted that the values within the ranges shown in Table 1S are equally probable. We
have very high confidence (at least 95%) that the low impact scenarios for 2020 and 2050 will be
exceeded and that the high impact scenarios will not be exceeded.
Relevance of the data to managers of ski resorts and natural resources
Results for 2020 are of greatest relevance to future management of both ski resorts and sites of
biological significance due to the smaller range of uncertainty in the projected changes in
temperature and precipitation. Projections to 2050 provide an indication of the future but are
associated with a far greater range of uncertainty.
Future changes in natural snow cover
A new version of CSIRO’s climate-driven snow model was developed and applied to this study.
Standard outputs of the snow model are:
snow depth;
snow cover duration;
snow ablation-rate;
snow-to-rainfall ratio;
regional maps and site-specific snow-depth profiles;
probability of snow depth at a given date;
snow-line elevation.
Simulations of future snow conditions in the Australian alpine regions were prepared for the years
2020 and 2050, based on climate change projections published by CSIRO in 2001, applied to the
new snow model.
Under the low impact and the high impact scenarios respectively, the total alpine area with an
average of at least one day of snow cover decreases 10-39% by 2020, and 22-85% by 2050. The
area with at least 30 days of snow cover decreases 14-54% by 2020, and 30-93% by 2050. The area
with at least 60 days of cover shrinks 18-60% by 2020, and 38-96% by 2050.
The low impact scenario for 2020 has a minor impact on snow conditions. Average season lengths
are reduced by around five days. Reductions in peak depths are usually less than 10%, but can be
larger at lower sites (e.g. Mt Baw Baw and Wellington High Plains). The high impact scenario for
2020 leads to reductions of 30-40 days in average season lengths. At higher sites such as Mt
Hotham, this can represent reductions in season duration of about 25% (Figure 1S), but at lower
sites such as Mt Baw Baw the reduction can be more significant (up to 60%). Impacts on peak
depth follow a similar pattern: moderate impacts at higher elevation sites, large impacts at lower
elevation sites. There is also a tendency for the time of maximum snow depth to occur earlier in the
season under warmer conditions. For example, the results for Thredbo show this occurring about 20
days earlier under the high impact scenario.
5
Mt Hotham
0
20
40
60
80
100
120
140
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 1S: Simulated 20-year average snow-depth profiles at Mt Hotham (1,882 m) for present
(1979-1998), 2020 and 2050.
The snowline is expected to rise with global warming. For example, at Mt Kosciuszko, the snowline
elevation on 1 September is predicted to rise from the present average of 1,461 metres to between
1,488 and 1,624 metres by 2020. The probability of exceeding a natural snow depth of 30 cm each
day also declines with greenhouse warming. For example, at Mt Hotham on 1 July, the probability
drops from the present value of 65% to 15-60% by 2020.
Under the low impact scenario for 2050, season durations are decreased by 15-20 days at most sites.
Such reductions are relatively minor at high sites but can represent a substantial impact at low sites.
The reductions in peak depths range from around 10% at the highest sites to more than 80% at low
sites such as Mt Baw Baw. The high impact scenario for 2050 leads to very large reductions in
season duration and peak depth at all sites. Season durations are typically reduced by around 100
days, which leaves only the highest sites with durations of more than ten days. Maximum depths
shrink to less than 10% of their present value and occur much earlier in the season.
Future requirements for snow-making by 2020
Adaptation to greenhouse warming will be needed in natural and managed systems. Adaptation
strategies for native fauna and flora are beyond the scope of this report but have been discussed in
other literature (e.g. Brereton et al., 1995). One of the various adaptation options at ski resorts is
increased use of snow-making to maintain adequate snow depths.
We assessed the effect of warmer conditions on the number of hours suitable for snow-making and
the Potential Volume of snow that could be made with two types of snow-guns typically used at
Australian resorts (brand-names withheld for commercial reasons). Brand A snow-guns produced
more snow at each resort than Brand B. The results are significantly influenced by the elevations at
which snow-making hours were computed, i.e. 1340 metres at Lake Mountain and Mt Thredbo,
1460 metres at Mt Baw Baw, 1550 metres at Mt Selwyn, 1642 metres at Falls Creek and 1720
metres at Mt Buller and Mt Perisher. On average each year, Mt Perisher could produce about
50,000 cubic metres of snow per Brand A snow-gun. Mt Thredbo, Mt Selwyn, Mt Buller and Falls
Creek could produce about 20,000 cubic metres of snow per Brand A snow-gun (Figure 2S), and
about 15,000 cubic metres per Brand B snow-gun. Baw Baw could produce about half these
amounts, and Lake Mountain could produce about one quarter. For both snow-guns, the average
number of hours suitable for snow-making declines by 2-7% for the low impact scenario and by 17-
54% for the high impact scenario. The potential snow volumes are reduced by 4-10% under the low
impact scenario, and by 27-55% under the high impact scenario.
6
Brand A snow-gun
0
10000
20000
30000
40000
50000
Perisher Thredbo Selwyn Buller Falls
Creek
Baw Baw Lake
Mountain
Average snow volume (m
3
)
now
2020 lo
2020 hi
Figure 2S: Average potential volume of snow (cubic metres) that could have been made during
May to September by each Brand A snow-gun when wet-bulb temperatures were below -2
o
C for
present and 2020. No wet-bulb temperature data were available for Mt Hotham.
Based on target snow-depth profiles nominated by snow-making managers at each resort (Table
2S), we used the snow model to simulate the amount of man-made snow required, taking into
account natural snowfall, snow-melt and the pre-existing natural snow depth. June was the month
with greatest need for man-made snow. Using data for 1950 to 1998, we computed the Target
Volume of man-made snow required at each resort to achieve the target profile in 90% of Junes
over a typical ski run (500 metres long and 40 metres wide).
Table 2S: Target snow-depths (cm) defined by snow-making managers at each ski resort.
Results for Mt Hotham and Baw Baw are not shown since monthly wet-bulb temperature data
were unavailable. * The target depth at Selwyn and Lake Mountain was set at 0 cm on 15 Sept.
Resort 1 June 30 June 31 July 31 August 30 Sept
Perisher 1 30 60 100 40
Thredbo 1 30 60 100 40
Selwyn 1 20 30 45 0*
Falls Creek 1 30 60 100 40
Mt Buller 1 30 50 90 20
Lake Mountain 1 30 30 30 0*
Using information about the Potential Volumes of snow that could be made, the number of snow-
guns needed to achieve the Target Volumes was estimated, under present and 2020 conditions.
About one Brand A snow-gun per ski run is needed at Mt Perisher under present conditions, 1.8 at
Falls Creek, almost three at Mt Selwyn and Mt Buller, and 15 at Lake Mountain. An increase of 11-
24% in the number of Brand A snow-guns would be required for the low impact scenario, and 73-
200% for the high impact scenario (Figure 3S). Brand B snow-guns produce slightly less snow than
Brand A. Under present conditions, about one Brand B snow-gun per ski run is needed at Mt
Perisher, 2.6 at Falls Creek and Mt Thredbo, 4.2 at Mt Selwyn and Mt Buller, and 21 at Lake
Mountain. An increase of 11-27% in the number of these snow-guns would be required for the low
impact scenario, and 71-188% for the high impact scenario. Therefore, with sufficient investment in
snow-guns, the Australian ski industry will be able to manage the impact of projected climate
change on snow cover until at least 2020, bearing in mind the limitations outlined below.
1720 m 1340 m 1550 m 1720 m 1642 m 1460 m 1340 m
7
Brand A snow-gun
0
1
2
3
4
5
6
7
Perisher Thredbo Selwyn Buller Falls Creek
Snow-guns per ski run
now
2020 lo
2020 hi
Figure 3S: Number of Brand A snow-guns needed to achieve resort-specific target snow-depth
profiles (Table 1S) in 90% of Junes on a typical ski-run, for present and 2020 conditions.
Limitations of the model in relation to snow making
This study required some simplifying assumptions, whilst excluding a number of physical and
management effects that are not easily included in CSIRO’s modeling framework. Apart from
limiting the results to two types of snow-guns, other physical and management exclusions were:
likely improvements in snow-making technology;
continuing improvements in snow-making operations such as:
optimized snow-gun start-up temperatures;
management of the number of pumps and pressure gradients to minimize water heating;
increased efficiency of water cooling systems;
snow-plume placement;
elevation of snow guns on towers;
additives to enhance conversion of water to snow;
snow grooming and snow-farming.
the effect of cold air drainage on snow-making capacity at lower elevations;
the effect of topographic aspect on natural snow deposition;
less rapid snow-melt rate for man-made snow relative to natural snow;
possible water-supply limitations due to projected reductions in precipitation and increased
evaporation in south-east Australia;
acceptable levels of environmental impact, e.g. likely increase in demand for water and energy
due to increased snow-making.
1720 m 1340 m 1550 m 1720 m 1642 m
8
Introduction
Global warming
Since the Industrial Revolution in the 18
th
century, human activities have increased the levels of the
main greenhouse gases carbon dioxide, water vapour, methane, nitrous oxide and ozone in the
lower atmosphere, and chlorofluorocarbons. The growth of carbon dioxide emissions is derived
largely from the burning of fossil fuels and land clearing, results in about half of carbon dioxide
remaining in the atmosphere, with the remaining half taken up almost equally between the oceans
and vegetation.
The present carbon dioxide concentration is greater than any recorded level for the past 420,000
years. During the past 100 years, the Earth’s average temperature has risen by about 0.6
o
C, with
1998 being the warmest on record, 2002 the second warmest and the 1990s being the warmest
decade (WMO, 2002). The Intergovernmental Panel on Climate Change (IPCC) has concluded that
“an increasing body of observations gives a collective picture of a warming world and other
changes in the climate system”. The other changes include a 10 to 20 cm rise in global-average sea-
level since 1900, warming of the deep ocean and the lowest 8 km of the atmosphere, and a
reduction in snow cover and the area of sea-ice.
Australia's alpine regions and climate change
Greenhouse warming has the potential to reduce snow cover in the Australian Alps (Figure 1),
however the large annual variability in snow season characteristics at various locations makes it
difficult to detect trends. Traditionally, data have been collected from a small number of alpine sites
and analysed statistically to estimate trends.
a
n
b
e
r
r
a
Mt Baw Baw
(1563m)
Lake Mountain
(1463m)
Melbourne
Mt Buller
(1809m)
Mt Wellington
Mt Buffalo
Three Mile Dam snow course
Mt Selwyn
Perisher Valley
Thredbo
Falls Creek
Mt Hotham
(1860m)
N
e
w
S
o
u
t
h
W
a
l
e
s
V
i
c
t
o
r
i
a
10 0 10 20 30 40 50 60 70 80
LEGEND
Land above 1400 metres in elevation
(this area is usually snow covered for
at least one month a year)
Mt Kosciuszko
(2229m)
Spencer’s Creek snow course
Deep Creek snow course
Figure 1: Study region and alpine sites referred to in this report. From Ruddell et al. (1990).
km
Mt Nelse
Rocky Valley Dam
Mt Jagungal
Whites River Valley
9
Ruddell et al. (1990) showed that snow depths had declined at some sites from the 1950s to 1989,
but no trends were statistically significant the 90% confidence level. Given that most of south-
eastern Australia has continued to warm over the past decade, an updated assessment of snow trends
is needed to show whether there has been any change in the rate of decline, and whether these
trends are consistent with projections based on greenhouse warming. This has been assessed in the
current study.
The most recent projections of natural snow cover taking into account greenhouse warming were
published by Whetton (1998), based upon CSIRO’s (1996) climate change scenarios and CSIRO’s
snow model (Whetton et al. 1996). Since 1998, CSIRO has released new Australian climate change
projections (May 2001) and the snow model has been modified to give a broader range of outputs
and more reliable projections of natural snow (See Appendix 1).
The current study updates information on observed changes in climate and snow in the Australian
Alps since 1950. It also brings together the improved snow model and updated climate projections
(CSIRO, 2001) to estimate potential changes in natural snow cover and depth by 2020 and 2050.
An assessment is made of the ability for ski resorts to adapt through increased snow-making.
10
Analysis of alpine climate trends since the mid-1950s
The databases and methods used to assess alpine climate trends are described in Appendix 1.
Changes in maximum and minimum temperature, precipitation and natural snow depth since the
1950s are outlined below.
Temperature changes
Australia has warmed by 0.7
o
C since 1910, with most of the warming occurring since 1950. Grided
data from the Bureau of Meteorology from 1950 to 2001 (Figure 2) show an increase in winter
maximum temperature in south-east Australia with little change in minimum temperature. However,
Cabrumurra was the only alpine site included in this grided dataset.
Figure 2: Trends in winter maximum (left) and minimum (right) temperature (ºC/decade) from
1950 to 2001, based on Bureau of Meteorology Reference Climate Stations.
In the current study, temperature data were analysed at eight high altitude sites in south-east
Australia for which reliable temperature data were available, four of which were above 1,300
metres. Trends in minimum and maximum temperatures from June to September were calculated
over various periods between 1962 and 2001 (Table 1). Positive trends were evident in most months
at Cabramurra, Perisher Valley, Thredbo Automatic Weather Station (AWS) and Thredbo Village
(Figures 3 and 4), especially for July and September. The alpine trends over approximately 35 years
were close to +0.02
o
C per year. At sites below 1,000 metres, trends were weak and inconsistent.
Table 1: Maximum and minimum temperature trends averaged over June to September at four
sites above 1,300 metres elevation.
Site Elevation
(m)
Period
(years)
T
max
trend
o
C/yr
T
min
trend
o
C/yr
Cabrumurra 1,480 1962-1998 +0.023 +0.004
Perisher 1,735 1976-2001 +0.057 +0.034
Thredbo AWS 1,957 1967-2001 +0.020 +0.021
Thredbo Village 1,380 1967-2001 +0.035 +0.031
11
Elevation (metres) Elevation (metres)
Figure 3: Trends for June, July, August and September maximum and minimum temperatures at
eight sites in south-east Australia for various periods between 1962 and 2001 (see Table 1).
From left to right in each panel, the stations are Rutherglen, Mt Beauty, Canberra, Rubicon,
Thredbo Village, Cabrumurra, Perisher and Thredbo Automatic Weather Station (AWS).
Figure 4: Time series of winter (June-Aug) maximum (red) and minimum (blue) temperature
(
o
C) at three alpine sites.
Precipitation changes
Annual average precipitation has decreased over most of eastern Australia and south-western
Australia, and increased over north-western Australia since 1950. The grided precipitation data
from the Bureau of Meteorology for 1950 to 2001 (Figure 5) show a decrease over the southern
Alps and an increase over the northern Alps. Although these results are based on interpolation of
data from non-alpine sites, they were confirmed using the finer resolution ANU CRES grided
precipitation data that include some alpine sites (Figure 6). For June to September between 1951
and 2000, small changes in alpine precipitation tended toward increases in the New South Wales
Alps and decreases in the Victorian Alps, consistent with the lower than average rainfall seen in
southern parts of Victoria over the past seven years.
12
Figure 5: Trends in winter precipitation (%/decade) from 1950-2001, based on Bureau of
Meteorology Reference Climate Stations. The “normal” reference period is 1961-1990.
Figure 6: Trends in June to September precipitation (mm/century) from 1951 to 2000, based
on the ANU CRES interpolation of data from the Bureau of Meteorology.
13
Changes in depth of natural snow
Analysis of maximum snow depth data from 1957 to 2002 at Deep Creek (NSW), Three Mile Dam
(NSW), Rocky Valley Dam (Vic) and Spencers Creek (NSW) gave trends of +0.09, -0.07, -0.33 and
0.43 cm/year, respectively (Figure 7). These trends represented percentage changes per decade of
+0.7, -1.3, -2.8 and 2.2 respectively. None of the trends was statistically significant at the 90%
confidence level.
To compare these results with those of Ruddell et al. (1990), we needed to convert snow depth data
to water equivalent data using an average snow-density factor of 0.4. Table 2 shows that the decline
in maximum snow depth has slowed in the 1990s at Three Mile Dam, Rocky Valley Dam and
Spencers Creek, and reversed at Deep Creek.
Slater (1995) estimated that snow depth had declined 25% at Spencers Creek between 1954 and
1993. Davis (1998) found a decrease in the number of Snowy Mountain snow-days from 1970 to
1996, particularly in May and August and Green (2000) noted a decreasing trend in integrated
weekly snow depth at Spencers Creek from 1959 to 1999.
Figure 7: Maximum snow depth (cm) at Spencers Creek (1,830 m), Deep Creek (1,620 m),
Rocky Valley Dam (1,650 m) and Three-Mile Dam (1,460 m).
14
Table 2: Trends in water-equivalent maximum snow depth (cm/yr and %/decade) at Deep
Creek, Spencers Creek, Three-Mile Dam and Rocky Valley Dam.
Site 1957-1989
1
1957-1989
1
1957-2002 1957-2002
cm/yr %/decade cm/yr %/decade
Deep Creek -0.05 -1.00 +0.04 +0.70
3 Mile Dam -0.05 -2.90 -0.03 -1.30
Rocky Valley Dam -0.54 -11.10 -0.13 -2.80
Spencers Creek -0.68 -7.40 -0.17 -2.20
1
Values for 1957-1989 taken from Ruddell et al. (1990).
Because maximum snow depth usually occurs during late August, trends in maximum snow depth
may not be the best indicator of changes in the snow profile at other times of the year, nor of the
length of the season. Since the mid-1950s, trends in snow depth on 1 July, 1 August and 1
September at Spencers Creek, Three Mile Dam and Deep Creek showed a decline from August to
September (Table 3 and Figure 8). Daily data for Rocky Valley Dam were unavailable. The August
1 decrease was 0.56 to 0.91 cm/year, while the September 1 decrease was 0.27 to 0.46 cm/year.
Table 3: Trends in snow depth (cm/yr) on 1 July, 1 August and 1 September at Deep Creek,
Spencers Creek and Three-Mile Dam. Daily data for Rocky Valley Dam were unavailable.
Site Elevation
(m)
Period
(years)
1 July 1 Aug 1 Sept
Spencers Creek 1,830 1954-2002 -0.43 -0.91 -0.46
Deep Creek 1,620 1957-2002 +0.56 -0.56 -0.27
Three Mile Dam 1,460 1955-2002 +0.03 -0.57* -0.26
* significant at the 97% confidence level.
In summary, warming trends at four alpine sites over the past 35 years are greater than the trends
assessed at lower elevations. Over the past 50 years, there is evidence of small increases in New
South Wales alpine precipitation and small decreases in Victorian alpine precipitation. A small
decline in maximum snow depths is evident at three of the four alpine sites in the years between
1957 and 2002. A moderate decline in August and September snow depths is evident at three sites,
possibly indicative of the tendency for mid to late season snow depth to be determined by
temperature-dependent ablation (melt and evaporation), whereas the depth of early season snow is
determined by precipitation.
15
Figure 8: Snow depth (cm) on 1 July, 1 August and 1 September at Spencers Creek (1,830 m),
Deep Creek (1,620 m) and Three Mile Dam (1,460 m).
16
Projected changes in natural snow conditions
Whetton (1998) estimated that the total area with snow cover for at least 30 days in south-east
Australia would decline by 18 to 66% by 2030 and by 39 to 96% by 2070. This was based on the
1994 version of CSIRO’s snow model and the 1996 version of CSIRO’s climate change projections
for the alpine region (a warming of 0.3 to 1.3
o
C and a precipitation change of between 0 and -8% by
2030, and a warming of 0.6 to 3.4
o
C and a precipitation change of between 0 and -20% by 2070).
Since the snow model and climate change projections have been updated, a new assessment of
projected impacts on natural snow conditions was needed.
CSIRO climate change projections for the Australian Alps in 2020 and 2050
The IPCC (2001) provided estimates of global warming for the 21
st
century (Appendix 2). Each of
the IPCC climate models gave a unique climate response for a given increase in greenhouse gases
some models have a greater global warming than others. To estimate climate change in the
Australian alpine region, model results were compared by expressing the regional climate responses
as a change per
o
C of global-average warming. To estimate regional changes in temperature and
precipitation for 2020 and 2050, the regional changes per
o
C of global warming were multiplied by
global warming values for 2020 and 2050 (Figure 2-A2 in Appendix 2). Alpine climate change
projections are shown in Table 4. For snow, the low impact scenario is the combination of the
lowest warming and the greatest precipitation increase, while the high impact scenario is the highest
warming combined with the greatest precipitation decrease. We have very high confidence (at least
95%) that the low impact scenarios will be exceeded and the high impact scenarios will not be
exceeded. Values within the ranges shown in Table 4 are equally probable.
Table 4: Changes in alpine temperature and precipitation for 2020 and 2050, relative to 1990.
Scenario
(Year)
Projected Change
in Temperature
(°C)
Projected Change in
Precipitation
(%)
Low impact (2020) +0.2 +0.9
High Impact (2020) +1.0 8.3
Low impact (2050) +0.6 +2.3
High impact (2050) +2.9 24.0
Simulated natural snow depth and duration
An improved version of the snow model (Appendix 1) has been used with the climate change
projections in Table 4. Simulated regional patterns of maximum snow depth and snow-cover
duration are shown in Figures 9 and 10, respectively, for the present, 2020 and 2050. The total area
with an average of at least 1 day of snow cover decreases 10-39% by 2020, and 22-85% by 2050
(Table 5). The area with at least 30 days of snow cover decreases 14-54% by 2020, and 30-93% by
2050. The area with at least 60 days of cover decreases 18-60% by 2020, and 38-96% by 2050.
Table 5: Percentage change in area with at least 1, 30 or 60 days simulated annual-average
snow-cover duration for 2020 and 2050, relative to 1990.
Snow duration 2020 low
a
2020 high
b
2050 low
a
2050 high
b
At least 1 day 9.9 39.3 22.0 84.7
At least 30 days 14.4 54.4 29.6 93.2
At least 60 days 17.5 60.3 38.1 96.3
a
low impact scenario,
b
high impact scenario
17
Site-specific results have been calculated for the alpine resorts, each of which has significant
biological attributes such as populations of the Mountain Pygmy-possum and Alpine She-oak
Skink, as well as alpine snow-patch communities. These resorts are:
Mt Baw Baw (Victoria);
Lake Mountain (Victoria);
Mt Buller (Victoria);
Mt Buffalo (Victoria);
Falls Creek (Victoria);
Mt Hotham (Victoria);
Mt Thredbo (New South Wales);
Mt Perisher (New South Wales);
Mt Selwyn (New South Wales).
The distribution of flora and fauna is expected to change under greenhouse climate effects at the
sub-continental level (Brereton et al. 1995) and in the Alps (Green and Pickering, 2002). Snow
conditions, depth and snowline can have important implications for the distribution and persistence
of biodiversity in the alpine area, so identification of potential changes in conditions can inform
future management.
Present
2020 low 2020 high 2020 high
2050 low 2050 high
Figure 9: Simulated maximum snow depth (cm) for present, 2020 and 2050.
Centimetres
18
Figure 10: Simulated snow-cover duration (days) for present, 2020 and 2050.
To augment the ecological value of the datasets, five additional non-resort areas were selected to
provide site specific data on:
a broader geographical range across the alps (e.g. Mt Kosciuszko, Wellington High Plains);
specific biological attributes or ecological processes (e.g. Long Plain-Jagungal - tree line
inversion, Mt Nelse - snowpatch);
sites of scientific interest for which limited data were already available and amenable to longer
term monitoring.
The additional five sites were:
Wellington high plains (Victoria);
Mt Nelse (Victoria);
Whites River valley (New South Wales);
Mt Jagungal (New South Wales);
Mt Kosciuszko (New South Wales).
Figures 11 to 24 present the snow depth profiles averaged over 20 years for the various alpine sites.
Profiles for present conditions were averaged over the period from 1979 to 1998 and profiles for the
future were created using climate data from the same 20 years modified by the climate change
scenarios. Table 6 shows average duration of snow cover at all sites.
Present
Days
2020 low
2050 low
2020 high
2050 high
19
Results for 2020 are of greatest relevance to future management of both ski resorts and sites of
biological significance due to the smaller range of uncertainty in the projected changes in
temperature and precipitation. Projections to 2050 remain useful as an indication of the future but
are associated with a far greater range of uncertainty.
The low impact scenario for 2020 has a minor impact on snow conditions. Average season lengths
are reduced by around five days. Reductions in peak depths are usually less than 10%, but can be
larger at lower sites (e.g. Mt Baw Baw and Wellington High Plains). The high impact scenario for
2020 leads to reductions of 30-40 days in average season lengths, with smaller impacts at higher
elevations and larger impacts at lower elevations. For example, at higher sites such as Mt Hotham,
this can represent reductions in season duration of about 25%, but at lower sites such as Mt Selwyn
the reduction can be more significant (up to 60%). Impacts on peak depth follow a similar pattern:
moderate impacts at higher elevation sites, large impacts at lower elevation sites. There is also a
tendency for the time of maximum snow depth to occur earlier in the season under warmer
conditions. For example, the results for Mt Thredbo show this occurring about 20 days earlier under
the high impact scenario. There is also a tendency for depth reductions to be larger in the late
season than in the early season. This pattern is consistent with observed trends.
Under the low impact scenario for 2050, season durations are decreased by 15-20 days at most sites.
Such reductions are relatively minor at high sites but can represent a substantial impact at low sites.
The reductions in peak depths range from around 10% at the highest sites to more than 80% at low
sites such as Mt Baw Baw. The high impact scenario for 2050 leads to very large reductions in
season duration and peak depth at all sites. Season durations are typically reduced by around 100
days, which leaves only the highest sites with durations of more than ten days. Maximum depths
shrink to less than 10% of their present value and occur much earlier in the season.
Mt Hotham
0
20
40
60
80
100
120
140
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 11: Simulated 20-year average snow-depth profiles at Mt Hotham (1,882 m) for present
(1979-1998), 2020 and 2050.
20
Perisher
0
20
40
60
80
100
120
140
160
180
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 12: Simulated 20-year average snow-depth profiles at Mt Perisher (1,835 m) for present
(1979-1998), 2020 and 2050.
Falls Creek
0
20
40
60
80
100
120
140
160
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 13: Simulated 20-year average snow-depth profiles at Falls Creek (1,797 m) for present
(1979-1998), 2020 and 2050.
Thredbo
0
20
40
60
80
100
120
140
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 14: Simulated 20-year average snow-depth profiles at Mt Thredbo (1,715 m) for present
(1979-1998), 2020 and 2050.
21
Mt Buller
0
10
20
30
40
50
60
70
80
90
100
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 15: Simulated 20-year average snow-depth profiles at Mt Buller (1,740 m) for present
(1979-1998), 2020 and 2050.
Mt Baw Baw
0
5
10
15
20
25
30
35
40
45
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 16: Simulated 20-year average snow-depth profiles at Mt Baw Baw (1,560 m) for
present (1979-1998), 2020 and 2050. See Appendix 2 for limitations of these data.
Lake Mountain
0
5
10
15
20
25
30
35
40
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 17: Simulated 20-year average snow-depth profiles at Lake Mountain (1,400 m) for
present (1979-1998), 2020 and 2050. See Appendix 2 for limitations of these data.
22
Mt Selwyn
0
10
20
30
40
50
60
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 18: Simulated 20-year average snow-depth profiles at Mt Selwyn (1,604 m) for present
(1979-1998), 2020 and 2050.
Mt Buffalo
0
10
20
30
40
50
60
70
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 19: Simulated 20-year average snow-depth profiles at Mt Buffalo (1,516 m) for present
(1979-1998), 2020 and 2050.
Wellington
High Plains
0
10
20
30
40
50
60
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 20: Simulated 20-year average snow-depth profiles at Wellington High Plains (1,560 m)
for present (1979-1998), 2020 and 2050. See Appendix 2 for limitations of these data.
23
Mt Nelse
0
20
40
60
80
100
120
140
160
180
200
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 21: Simulated 20-year average snow-depth profiles at Mt Nelse (1,829 m) for present
(1979-1998), 2020 and 2050.
Whites River Valley
0
20
40
60
80
100
120
140
160
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 22: Simulated 20-year average snow-depth profiles at Whites River Valley (1,746 m) for
present (1979-1998), 2020 and 2050.
Jagungal
0
50
100
150
200
250
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 23: Simulated 20-year average snow-depth profiles at Mt Jagungal (2,061 m) for
present (1979-1998), 2020 and 2050.
24
Mt Kosciuszko
0
50
100
150
200
250
18-May
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
16-Nov
30-Nov
Date
Snow depth (cm)
now
2020 low
2020 high
2050 low
2050 high
Figure 24: Simulated 20-year average snow-depth profiles at Mt Kosciuszko (2,228 m) for
present (1979-1998), 2020 and 2050.
Table 6: Simulated average duration (days) of at least 1 cm of snow-cover at selected resorts
(some with results for low, mid and high elevations) and sites of biological significance.
Site (elevation) Present 2020 2050
Lake Mountain (1,400 m) 74 30-66 1-48
Mt Baw Baw (1,560 m) 80 32-71 1-53
Mt Buller low (1,383 m) 33 7-25 0-15
Mt Buller mid (1,560 m) 76 36-67 1-56
Mt Buller high (1,740 m) 108 70-102 7-89
Mt Buffalo low (1,477 m) 70 29-63 0-50
Mt Buffalo mid (1,516 m) 80 39-73 1-59
Mt Buffalo high (1,723 m) 113 78-108 10-96
Mt Wellington high plains (1,560 m) 82 38-75 2-59
Mt Nelse (1,829 m) 133 101-128 27-117
Falls Creek low (1,504 m) 77 41-71 2-59
Falls Creek mid (1,643 m) 105 68-99 8-87
Falls Creek high (1,797 m) 125 92-120 18-108
Mt Hotham low (1,400 m) 51 15-44 0-29
Mt Hotham mid (1,650 m) 98 59-92 4-77
Mt Hotham high (1,882 m) 129 97-124 21-114
Mt Perisher low (1,605 m) 90 53-87 4-69
Mt Perisher mid (1,835 m) 131 100-125 30-115
Mt Perisher high (2,021 m) 151 122-146 56-136
Mt Thredbo low (1,350 m) 32 8-26 0-17
Mt Thredbo mid (1,715 m) 113 80-108 13-97
Mt Thredbo high (2,023 m) 153 122-148 56-138
Mt Selwyn (1,604 m) 81 43-74 3-60
Whites River valley (1,746 m) 118 88-113 18-103
Mt Jagungal (2,061 m) 156 128-151 65-141
Mt Kosciuszko (2,228 m) 183 153-178 96-169
25
More detailed analysis of future snow conditions at selected sites
Elevation of the snowline
The daily elevation of the snowline was estimated for Mt Hotham by identifying the lowest snow-
covered grid-point within 25 km. Results were averaged over the 20-year period 1979 to 1998 and
plotted as a snowline profile throughout the year (Figure 25). For example, the snowline is
predicted to rise from the present average of 1,412 metres on 1 September, to between 1,440 and
1,600 metres by 2020. At Mt Selwyn, the snowline on 1 September rises from 1,415 metres at
present to between 1,500 and 1,660 metres by 2020. At Mt Kosciuszko, the snowline on 1
September rises from 1,460 metres at present to between 1,490 and 1,625 metres by 2020. As
expected, there was little variation between sites in the behaviour of the snowline with warming.
Mt Hotham
0
500
1000
1500
2000
2500
1-Jun
16-Jun
1-Jul
16-Jul
31-Jul
15-Aug
30-Aug
14-Sep
29-Sep
14-Oct
29-Oct
Date
Snowline (m)
Present
2020 low
2020 high
2050 low
2050 high
Mt Selwyn
0
500
1000
1500
2000
2500
1-Jun
16-Jun
1-Jul
16-Jul
31-Jul
15-Aug
30-Aug
14-Sep
29-Sep
14-Oct
29-Oct
Date
Snowline (m)
Present
2020 low
2020 high
2050 low
2050 high
Mt Kosciuszko
0
500
1000
1500
2000
2500
1-Jun
16-Jun
1-Jul
16-Jul
31-Jul
15-Aug
30-Aug
14-Sep
29-Sep
14-Oct
29-Oct
Date
Snowline (m)
Present
2020 low
2020 high
2050 low
2050 high
Figure 25: Simulated average snow-line elevation for Mt Hotham, Mt Selwyn and Mt Kosciuszko
for present (1979-1998), 2020 and 2050. High impact scenarios are truncated when no snow is
simulated.
26
Probability of exceeding 30 cm natural snow depth
The probability of exceeding a natural snow depth of 30 cm each day was calculated for Mt Hotham
using data for 1979 to 1998 (Figure 26). For example, on 1 September, 18 of the 20 years had at
least 30 cm of snow, so the present probability is 90%. By 2020, this probability is predicted to
decline to between 60 and 85%. On 1 July, the probability drops from the present value of 65% to
15-60% by 2020.
Mt Hotham
0
10
20
30
40
50
60
70
80
90
100
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
Date
Probability of > 30 cm snow depth (%)
Present
2020 low
2020 high
2050 low
2050 high
Figure 26: Simulated profiles of the probability of exceeding 30 cm natural snow depth at Mt
Hotham (1,882 m) for present (1979-1998), 2020 and 2050.
Ratio of snow to rain
A decrease in the ratio of snow to rain in each precipitation event is expected as the climate warms,
i.e. precipitation will tend to fall as rain rather than snow. The simulated ratio of snow to rain was
calculated for Mt Hotham for the present (1979-1998), 2020 and 2050 (Figure 27). Changes by
2020 are small for the low impact scenario but significant for the high impact scenario. For
example, on 1 September, the ratio declines from the present value of 4.2 to between 4.0 and 2.9 by
2020. This represents a decrease of 5-31%. On 1 July and 1 August, the present ratios are 5.2 and
5.7, respectively, and the decreases by 2020 are 0-16% and 1-23%, respectively.
Mt Hotham
0
1
2
3
4
5
6
7
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Ratio of snow to rain
now
2020 low
2020 high
2050 low
2050 high
Figure 27: Simulated profiles of the ratio of snow to rain at Mt Hotham (1,882 m) for present
(1979-1998), 2020 and 2050. An 11-day running mean has been used to smooth the data.
27
Daily rate of snow ablation
Greenhouse warming is likely to enhance the rate of ablation in future. The simulated daily rate of
ablation was calculated at Mt Hotham (Figure 28). The low impact scenario for 2020 shows little
change from present, but the high impact scenario for 2020 shows substantial increases during
September, e.g. a 58% increase on 1 September. Results for a lower site (Mt Selwyn) and a higher
site (Mt Kosciuszko) are also shown in Figure 28.
Mt Hotham
0
10
20
30
40
50
60
70
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow ablation rate (mm/day)
now
2020 low
2020 high
2050 low
2050 high
Mt Selwyn
0
10
20
30
40
50
60
70
80
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow ablation rate (mm/day)
now
2020 low
2020 high
2050 low
2050 high
Mt Kosciuszko
0
5
10
15
20
25
30
35
40
45
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow ablation rate (mm/day)
now
2020 low
2020 high
2050 low
2050 high
Figure 28: Simulated profiles of the daily snow ablation rate at Mt Hotham, Mt Selwyn and Mt
Kosciuszko for present (1979-1998), 2020 and 2050. Units are water-equivalent mm per day.
28
Projected changes in snow-making requirements
A recent report on the Swiss ski industry (Elsasser and Bürki, 2002) notes that 85% of
Switzerland’s current ski resorts can be designated as having reliable natural snow, and this may
decline to 44% over the coming decades if the elevation of reliable snow rose 600 metres due to
greenhouse warming. The report concludes that climate change should be viewed as a catalyst for
reinforcing and accelerating the pace of structural changes in alpine tourism. Various adaptation
strategies are outlined (Figure 29) adopting a fatalistic attitude toward climate change is unlikely
since consumers and suppliers will undoubtedly alter their behaviour. Greenhouse warming over the
coming decades will require adaptation by the ski industry through various operational and
technical advances, many of which have been ongoing in the past decade, such as snow-making.
Figure 29: Possible adaptation strategies. Adapted from Elsasser and Bürki (2002).
Snow-making is used in Australia to supplement natural snow cover on heavily used or low-
elevation ski runs and lift access areas (NPWS, 2001). Snow is usually guaranteed for the opening
of the season in early June due to the availability of man-made snow. Snow-guns may be triggered
automatically, by selected wet-bulb temperatures, or operated manually, with location, flow-rate
and duration optimized to suit prevailing conditions. Snow fences and grooming are also important
for creating and placing snow in the right location with some resorts selectively using nucleating
agents to enhance snow-making efficiency (NPWS, 2001).
Scott et al. (2003) investigated the vulnerability of the southern Ontario (Canada) ski industry to
climate change, including adaptation through snow-making. They used a 17-year record of daily
snow conditions and operations from a major ski area to calibrate a ski season model including
snow-making and operational decision rules based on interviews with ski area managers. The
average ski season duration was projected to decline 0-16% by 2020, requiring a compensating
Develop higher
terrain
Ski slope design
Maintain ski industry
Adaptation strategies
Alternatives to skiing
Subsidies
Fatalism
Artificial
snow-making
Co-operation
Annual
contributions
Single grant
Non-snow
related
activities
All-year tourism
Hikes, tennis
Business
as usual
Give up skiing
29
increase in snow-making by 36-144%. They concluded that the southern Ontario ski areas could
remain operational in a warmer climate within existing business planning and investment time
horizons (into the 2020s). In our assessment for Australia, we have used a similar methodology to
Scott el al. (2003).
Future demand for snow making will be influenced by:
1. Fewer hours with temperatures cold enough for making snow;
2. Less natural snow cover;
3. Faster ablation of snow;
4. Improvements in snow-making technology and operations;
5. The effect of cold air drainage on snow-making capacity at lower elevations;
6. The effect of topography and aspect on natural snow deposition; and
7. Possible water supply limitations and increased demand for water and power.
In our study, results are presented for the effects of factors 1, 2 and 3. The effect of factor 1 is
presented first, followed by results for the combination of factors 1, 2 and 3. The exclusion of
factors 4-7 are limitations of this study, outlined in more detail in the Conclusions.
Impact of greenhouse warming on potential snow-making hours and volume
Sub-zero wet-bulb temperatures are necessary for snow-making. Unlike dry-bulb temperature, wet-
bulb temperature is influenced by humidity. Snow-making managers at most resorts were able to
supply data showing the number of hours with wet-bulb temperatures in the range 2 to –12
o
C, in
0.1
o
C intervals, for May to September in various years between 1997 and 2002. Temperatures are
lower at higher elevations, as shown by the annual number of hours below -5
o
C in Figure 30, and
this has a significant effect on the apparent snow-making capacity of each resort. Hence it is
important to note the elevations at which the wet-bulb temperatures were measured (Table 7). Data
for Mt Perisher and Mt Buller relate to a much higher elevation (1720 metres) than data for other
resorts (e.g. 1340 metres at Mt Thredbo and Lake Mountain).
Wet-bulb warming scenarios for 2020 were derived from the output of ten climate models
(Hennessy and Whetton, 2001) and applied to the observed hourly wet-temperature data at each
resort. The warming was 0.2-0.9
o
C at Mt Hotham and Falls Creek, and 0.1-0.7
o
C at the other
resorts. These changes are less than those for dry-bulb temperature (Table 4) due to regional
changes in humidity associated with greenhouse warming. The average number of hours suitable for
snow-making declines by 2-7% for the low impact scenario and by 17-54% for the high impact
scenario (Figure 31).
30
Table 7: Locations of sites at which wet-bulb temperatures were measured during May to
September in specified years at each resort. No data were available for Mt Hotham.
Resort Years Site(s) Elevation (m)
Mt Perisher 1997-2001 Bottom of Perisher Express Quad Chair 1720
Mt Thredbo 1997-2001 Valley Terminal weather station 1340
Mt Selwyn 1997-2001 New Chum Beginner Bowl 1550
Falls Creek 1997-1999 Average of 5 sites 1642
Mt Buller 1997 & 2000 Average of 3 sites 1720
Mt Baw Baw 1998-2002 Bottom of Maltese Cross T Bar 1460
Lake Mountain 1997-2002 Gerratys 1340
Mt Selwyn
0
10
20
30
40
50
60
-2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
Wet-bulb temperature (
o
C)
Hours
1997
1998
1999
2000
2001
Thredbo
0
10
20
30
40
50
60
-2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
Wet-bulb temperature (
o
C)
Hours
1997
1998
1999
2000
2001
Mt Buller
0
10
20
30
40
50
60
-2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
Wet-bulb temperature (
o
C)
Hours
1997
2000
Falls Creek
0
10
20
30
40
50
60
-2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
Wet-bulb temperature (
o
C)
Hours
1997
1998
1999
Figure 30: Total number of hours in wet-bulb temperature intervals of 0.1
o
C from May to
September in various years at Mt Selwyn, Mt Thredbo, Mt Buller and Falls Creek.
31
May-Sep wet-bulb tempertures below -2
o
C
0
250
500
750
1000
1250
1500
Perisher Thredbo Mt Selwyn Mt Buller Falls Creek Baw Baw Lake Mtn
Hours
now
2020 lo
2020 hi
Figure 31: Average number of hours during May to September when wet-bulb temperatures
were below -2
o
C for present (available years) and 2020. No wet-bulb temperatures were
available for Mt Hotham.
To assess the impact of fewer snow-making hours on the volume of snow that could be made, we
need to make some simplifying assumptions about snow-guns and how they are used. Resort
operators have access to a range of snow-guns for making snow, each of which has different
production characteristics. There are two basic types: air-water guns and fan guns. Each technology
has strengths and weaknesses related to snow output, capital costs and operating costs. Most resorts
have a mix of snow-making equipment operating at different pressures and water temperatures,
sometimes automatically activated at selected temperatures and sometimes manually operated. In
this study we assume automatic activation at temperatures below -2
o
C and unlimited water supply.
Snow-gun specifications include the amount of water used (litres per second) for wet-bulb
temperatures ranging from -2 to -12
o
C (Figure 32). At lower temperatures, more snow can be made
and more water is used in the process. To simplify calculations, we limited our analysis to the
Brand A air-water gun and Brand B fan gun (brand-names withheld for commercial reasons). At
each resort, the wet-bulb temperatures were combined with water-flow specifications for each
snow-gun to estimate the amount of snow that could have been produced each year we define this
amount as the Potential Volume. An average snow density of 0.4 was used to convert water
volumes to snow volumes.
1720 m 1340 m 1550 m 1720 m 1642 m 1460 m 1340 m
32
Snowgun water flow
0
1
2
3
4
5
6
7
8
9
10
-2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12
Wet-bulb temperature (
o
C)
Water flow (litres/second)
A
B
C
D
E
Figure 32: Water-flow specifications (litres/second) at various wet-bulb temperatures for a
selection of snow-guns (Brands A, B, C, D, E) commonly used at Australian resorts.
Figure 33 shows the Potential Volume of snow that could have been made by each Brand A or
Brand B snow-gun, based on the performance curves in Figure 32. Brand A snow-guns produced
more snow at each resort than Brand B. Under the present climate, Mt Perisher showed the best
snow-making capacity since it had the coldest wet-bulb temperature data for the highest elevation
site. On average each year, Mt Thredbo, Mt Selwyn, Mt Buller and Falls Creek could produce about
20,000 cubic metres of snow using Brand A snow-guns, and about 15,000 cubic metres using Brand
B. Mt Baw Baw could produce about half these amounts, and Lake Mountain could produce about
one quarter. For both snow-guns, the potential snow volumes are reduced by 3-10% under the low
impact scenario for 2020, and by 18-55% under the high impact scenario.
33
Brand A snow-gun
0
10000
20000
30000
40000
50000
Perisher Thredbo Selwyn Buller Falls
Creek
Baw Baw Lake
Mountain
Average snow volume (m
3
)
now
2020 lo
2020 hi
Brand B snow-gun
0
10000
20000
30000
40000
50000
Perisher Thredbo Selwyn Buller Falls
Creek
Baw Baw Lake
Mountain
Average snow volume (m
3
)
now
2020 lo
2020 hi
Figure 33: Average potential volume of snow (cubic metres) that could have been made during
May to September by each Brand A or B snow-gun when wet-bulb temperatures were below
–2
o
C for present (available years) and 2020. No wet-bulb temperature data were available for
Mt Hotham.
Adapting to greenhouse warming through increased snow-making
The snow-making manager at each resort nominated a target depth profile for natural plus man-
made snow, to be achieved 90% of the time. For example, at Mt Perisher, Mt Thredbo and Falls
Creek, the profile was defined as 1 cm by 1 June, 30 cm by 30 June, 60 cm by 31 July, 100 cm by
31 August and 40 cm by 30 September (Table 8). Lower profiles were specified for Mt Buller, Mt
Selwyn and Lake Mountain, reflecting their lower natural snow cover. The CSIRO daily snow
model was modified to calculate the amount of man-made snow required to achieve these target
depths, allowing for natural snowfall, ablation and the pre-existing natural snow-depth, as shown
for Mt Hotham in 1997 in Figure 34.
1720 m 1340 m 1550 m 1720 m 1642 m 1460 m 1340 m
34
Table 8: Target snow-depth (cm) profiles defined by snow-making managers at each ski resort.
Results for Mt Hotham and Mt Baw Baw are not shown since monthly wet-bulb temperature
data were unavailable. * Target depth at Mt Selwyn and Lake Mountain was 0 cm on 15 Sept.
Resort 1 June 30 June 31 July 31 August 30 Sept
Mt Perisher 1 30 60 100 40
Mt Thredbo 1 30 60 100 40
Mt Selwyn 1 20 30 45 0*
Falls Creek 1 30 60 100 40
Mt Buller 1 30 50 90 20
Lake Mountain 1 30 30 30 0*
Mt Hotham 1997
-20
0
20
40
60
80
100
120
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
Date
Snow depth (cm)
ablation
total snow depth
natural snowfall
man-made snow
Figure 34: Simulated daily snowfall, ablation and man-made snow required to meet the target
snow-depth profile from 1 June to 30 September 1997. The target depths are 30 cm on 30
June, 60 cm on 31 July, 100 cm on 31 August and 40 cm on 30 September.
The snow model simulated the daily snowfall, snow-melt and man-made snow required to meet the
target snow depth profiles from 1950 to 1998 at each resort. According to the simulations, June and
September were the months in which most man-made snow was needed. The accumulated monthly
man-made snow depth for Mt Hotham is shown in Figure 35. An accumulated depth of 43.4 cm was
required to ensure that 90% of Junes reached the target total depth (i.e. the 5
th
greatest depth in 49
years, which was the 1980 value in Figure 35).
35
Mt Hotham
0
10
20
30
40
50
60
70
80
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995
Year
Man-made snow needed (cm)
June
July
August
September
Figure 35: Monthly accumulated man-made snow depths required to meet the target snow-
depth profile at Mt Hotham from 1950 to 1998. The target depths are 30 cm on 30 June, 60 cm
on 31 July, 100 cm on 31 August and 40 cm on 30 September.
A typical ski run is about 500 metres long and 40 metres wide, with an area of 20,000 m
2
. We
calculated that 8,687 m
3
of snow would be required to cover a typical ski run at Mt Hotham in 90%
of Junes. This was defined as the Target Volume for Mt Hotham. Different resorts had different
Target Volumes, based on the combination of site-specific natural snowfall, snow-melt and target
snow depths. Using information about the Potential Volume of snow that could be made (Figure
33), we can estimate the number of Brand A or Brand B snow-guns needed to achieve the Target
Volumes, under present and 2020 conditions. The June and September versions of Figure 33 (not
shown) were used since these were the months with greatest need for snow-making.
Sample Calculation
At Falls Creek, each Brand A snow-gun could have produced 3451 m
3
in June 1997, 7562 m
3
in June 1998 and 5008
m
3
in June 1999. Therefore, the average Potential Volume was 5340 m
3
per gun. Under present conditions, the
Target Volume at Falls Creek is 9614 m
3
. Hence, the target depth on a typical ski run could be achieved in 90% of
Junes with 1.8 Brand A guns per ski-run (i.e. 9614/5340). The low impact scenario for 2020 reduced the average
volume per gun to 4880 m
3
and increased the target volume to 10,315 m
3
, so the number of Brand A guns needed to
achieve the target volume increased to 2.1 per ski run. Similarly, the high impact scenario for 2020 required 5.1
Brand A guns per ski run.
The results are significantly influenced by the elevations at which snow-making hours were
computed, i.e. 1340 metres at Lake Mountain and Mt Thredbo, 1460 metres at Mt Baw Baw, 1550
metres at Mt Selwyn, 1642 metres at Falls Creek and 1720 metres at Mt Buller and Mt Perisher.
Under present conditions, about one Brand A gun per ski run is needed at Mt Perisher, 1.8 at Falls
Creek, and almost three at Mt Selwyn and Mt Buller (Figure 36). At Lake Mountain, 15 Brand A
guns per ski-run would be needed (not shown). An increase of 11-24% in the number of these
snow-guns would be required under the low impact scenario for 2020: 11% at Mt Selwyn, 12% at
Mt Thredbo, 14% at Mt Perisher and Lake Mountain, 17% at Falls Creek and 24% at Mt Buller.
Under the high impact scenario for 2020, a 73-200% increase in snow-guns is needed: 73% at Mt
Perisher, 85% at Mt Thredbo, 94% at Mt Selwyn, 142% at Falls Creek, 181% at Mt Buller and
200% at Lake Mountain. Different results would be obtained if different target snow-depth profiles
were specified in Table 8. Brand B snow-guns produce slightly less snow than Brand A. Under
present conditions, about one Brand B gun per ski run is needed at Mt Perisher, 2.6 at Falls Creek
and Mt Thredbo, 4.2 at Mt Selwyn and Mt Buller, and 21 at Lake Mountain. An increase of 11-27%
in the number of these snow-guns would be required under the low impact scenario for 2020: 11%
at Mt Selwyn, 12% at Mt Thredbo, 14% at Mt Perisher, 15% at Lake Mountain, 18% at Falls Creek
and 27% at Mt Buller. Under the high impact scenario for 2020, a 71-188% increase in Brand B
snow-guns is needed: 71% at Mt Perisher, 90% at Mt Thredbo, 97% at Mt Selwyn, 151% at Lake
Mountain, 171% at Mt Buller and 188% at Falls Creek.
36
Brand A snow-gun
0
1
2
3
4
5
6
7
Perisher Thredbo Selwyn Buller Falls Creek
Snow-guns per ski run
now
2020 lo
2020 hi
Brand B snow-gun
0
1
2
3
4
5
6
7
8
9
10
11
12
Perisher Thredbo Selwyn Buller Falls Creek
Snow-guns per ski run
now
2020 lo
2020 hi
Figure 36: Number of Brand A or Brand B snow-guns needed to achieve resort-specific target
snow-depth profiles (Table 8) in 90% of Junes on a typical ski run measuring 500m x 40 m, for
present and 2020 conditions.
1720 m 1340 m 1550 m 1720 m 1642 m
37
Conclusions
Warming trends at four alpine sites over the past 35 years appear to be greater than trends at lower
elevations. There is evidence of small alpine precipitation changes over the past 50 years, with
increases in the New South Wales Alps and decreases in the Victorian Alps. A weak decline in
maximum snow depth is evident at three of the four alpine sites analysed since the 1950s. A decline
in season snow depths for August and September is evident at three sites which may indicate the
tendency for warmer temperatures to reduce both the snow-to-rainfall ratio and increase the rate of
melting.
The low impact scenario for 2020 has a minor impact on snow conditions. Average season lengths
are reduced by around five days. Reductions in peak depths are usually less than 10%, but can be
larger at lower sites (e.g. Mt Baw Baw and Wellington High Plains). The high impact scenario for
2020 leads to reductions of 30-40 days in average season lengths. At higher sites such as Mt
Hotham, this can represent reductions in season duration of about 25%, but at lower sites such as
Mt Baw Baw the reduction can be more significant (up to 60%). Impacts on peak depth follow a
similar pattern: moderate impacts at higher elevation sites, large impacts at lower elevation sites.
There is also a tendency for the time of maximum snow depth to occur earlier in the season under
warmer conditions. For example, the results for Mt Thredbo show this occurring about 20 days
earlier under the high impact scenario.
The snowline is expected to rise with global warming. For example, at Mt Kosciuszko, the snowline
elevation on 1 September is predicted to rise from the present average of 1,460 metres to between
1,490 and 1,625 metres by 2020. The probability of exceeding a natural snow depth of 30 cm each
day also declines with greenhouse warming. For example, at Mt Hotham on 1 July, the probability
drops from the present value of 65% to 15-60% by 2020.
Under the low impact scenario for 2050, season durations are decreased by 15-20 days at most sites.
Such reductions are relatively minor at high sites but can represent a substantial impact at low sites.
The reductions in peak depths range from around 10% at the highest sites to more than 80% at low
sites such as Mt Baw Baw. The high impact scenario for 2050 leads to very large reductions in
season duration and peak depth at all sites. Season durations are typically reduced by around 100
days, which leaves only the highest sites with durations of more than ten days. Maximum depths
shrink to less than 10% of their present value and occur much earlier in the season
Adaptation to climate change will be necessary at all ski resorts. An obvious strategy is to make
more snow using snow-guns. The Potential Volume was defined as the amount of snow that could
be made using two typical snow-guns at each resort, based on information about the snow-gun
performance and the frequency of wet-bulb temperatures suitable for snow-making. Brand A snow-
guns produced more snow at each resort than Brand B. The results are significantly influenced by
the elevations at which snow-making hours were computed, i.e. 1340 metres at Lake Mountain and
Mt Thredbo, 1460 metres at Mt Baw Baw, 1550 metres at Mt Selwyn, 1642 metres at Falls Creek
and 1720 metres at Mt Buller and Mt Perisher.
On average each year, Mt Perisher could produce about 50,000 cubic metres of snow per Brand A
snow-gun. Mt Thredbo, Mt Selwyn, Mt Buller and Falls Creek could produce about 20,000 cubic
metres of snow per Brand A snow-gun, and about 15,000 cubic metres per Brand B snow-gun. Mt
Baw Baw could produce about half these amounts, and Lake Mountain could produce about one
quarter. For both snow-guns, the average number of hours suitable for snow-making declines by 2-
7% for the low impact scenario and by 17-54% for the high impact scenario. The potential snow
volumes are reduced by 4-10% under the low impact scenario, and by 27-55% under the high
impact scenario.
38
Based on target snow-depth profiles nominated by snow-making managers at each resort, we used
the snow model to simulate the amount of man-made snow required, taking into account natural
snowfall, snow-melt and the pre-existing natural snow depth. Using information about Potential
Volume of snow that could be made, we estimated the number of snow-guns needed to achieve the
target depth profiles over a typical ski-run 90% of the time, under present and 2020 conditions.
About one Brand A gun per ski run is needed at Mt Perisher under present conditions, 1.8 at Falls
Creek, almost three at Mt Selwyn and Mt Buller, and 15 at Lake Mountain. An increase of 11-24%
in the number of Brand A snow-guns would be required for the low impact scenario, and 73-200%
for the high impact scenario. Brand B snow-guns produce slightly less snow than Brand A. Under
present conditions, about one Brand B gun per ski run is needed at Mt Perisher, 2.6 at Falls Creek
and Mt Thredbo, 4.2 at Mt Selwyn and Mt Buller, and 21 at Lake Mountain. An increase of 11-27%
in the number of these snow-guns would be required for the low impact scenario, and 71-188% for
the high impact scenario. Therefore, with sufficient investment in snow-guns, the Australian ski
industry will be able to manage the impact of projected climate change until at least 2020, bearing
in mind the limitations outlined below.
This study has made some simplifying assumptions and excluded a number of physical and
management effects that are not easily included in CSIRO’s modeling framework. Apart from
limiting results to the two snow-guns, operated automatically at all resorts, exclusions were:
likely improvements in snow-making technology;
improvements in snow-making operations, e.g. optimizing start-up temperatures, managing the
number of pumps and pressure gradients to minimize water heating, improving efficiency of
water cooling systems, plume placement, elevating guns on towers, additives to enhance
conversion of water to snow, snow grooming and snow-farming, the effect of cold air drainage
on snow-making capacity at lower elevations;
effect of topographic aspect on natural snow deposition;
reduced ablation rate for man-made snow relative to natural snow;
possible water-supply limitations due to projected climate change;
acceptable levels of environmental impact, e.g. likely increase in demand for water and energy
due to increased snow-making.
Research needed to address gaps in knowledge
There is significant potential to widen the scope of the current study, and to address uncertainties
and gaps in knowledge, through further research.
Accuracy of the modeling of natural snow cover could be improved by using daily temperature
information, rather than the monthly temperature data currently used. Daily data would allow more
accurate estimation of the proportion of precipitation falling as snow, and would be likely to
improve snow simulation in southern areas of the Alps where the current methods based on monthly
data are likely to be less reliable. More generally, year to year and within-year fluctuations in snow
depth would be improved. It would also allow full integration of the modeling of natural and man-
made snow (see below).
There is also a need to improve the accuracy of the observed climate data sets in the southern
Victorian alpine region, to address problems described in Appendix 2.
39
Time and resource limitations required us to simulate natural snow cover and man-made snow
production with separate datasets (with very different time periods) and models. Although it was
then possible to compare the results to make some assessment of the industry’s capacity to use
snow-making to adapt to climate change, more accurate results would have been obtained from an
integrated modeling system which simultaneously modeled both natural snow and man-made snow
using a consistent daily dataset this type of approach was used recently in the Canadian study by
Scott et al. (2003). It would also be appropriate to make allowance for differing ablation-rates of
man-made and natural snow in the modeling system. It would be beneficial to consider the water
supply and energy implications of increased snow-making.
The geographical scope of the study could expanded to include alpine areas in Tasmania.
Comparison with impacts in other skiing regions such as New Zealand, Canada, USA and Europe
could also be considered. The impact on Australian alpine biodiversity should also be considered.
Acknowledgements
The climate model data were generously provided by climate modellers at CSIRO, the Canadian
Climate Centre, Deutsches Klimarechenzentrum in Germany, the U.S. Geophysical Fluid Dynamics
Laboratory, the U.S. National Center for Atmospheric Research, and the U.K. Hadley Centre for
Climate Prediction and Research. Dr David Etheridge (CSIRO) provided useful comments on the
manuscript.
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41
Appendix 1 Methodology
Collection and preparation of observed climate databases
In the study by Whetton (1998), the CSIRO snow model was driven with monthly temperature and
precipitation data from 1966 to 1985 on a 1/40
th
degree grid (about 2.5 km). To enhance
performance of the model, the ANU Centre for Resource and Environmental Studies (ANU CRES)
developed improved databases for elevation, temperature and precipitation on the same grid, and
extended the monthly climate datasets from 1951 to 2000.
The climate grids were created by fitting thin plate smoothing splines, dependent on longitude,
latitude and elevation, to climate-station data recorded by the Australian Bureau of Meteorology as
described in Hutchinson (1991), and then calculating the surfaces on a digital elevation model. The
thin plate smoothing splines were calculated by Version 4.3 of the ANUSPLIN software package
(Hutchinson 2001). Recent advances in this package include dependent variable transformations
and the ability to process incomplete data sets. A square root transformation was applied to the
precipitation data to remove the natural skewness in these data. This had the advantages of reducing
interpolation error by equilibrating spatial variability between low precipitation areas and high
precipitation areas and of delivering fitted surfaces that naturally gave rise to non-negative
precipitation values.
Data for the monthly mean temperature and precipitation surfaces were first standardised to the
period 1951 to 2000 by regressing short period records with nearby long period records. The
regression with least estimated error was chosen for each short period record. Precipitation
regressions were performed on the square root rainfalls to remove the effects of skewness, as was
done for the spatial precipitation analyses. Appropriate bias corrections were made when the square
root transformation was used. The monthly mean daily minimum temperature grids were supplied
in ASCII Arc/Info grid format, with average estimated standard errors that ranged from 0.5
o
C in
winter to 0.7
o
C in summer. The monthly mean daily maximum temperature grids had average
estimated standard errors that ranged from 0.4
o
C in winter to 0.6
o
C in summer. The monthly
precipitation grids had average estimated standard errors from 15 to 20% of the grid means. The
errors were as low as 10% in wet winter months and greater than 20% in drier months.
For the analysis of alpine climate trends, several data sources were used.
winter-average temperature data from the Bureau of Meteorology for 1950-2001 on a ¼ degree
grid (about 25 km);
winter-average precipitation data from the Bureau of Meteorology for 1950-2001 on a ¼ degree
grid (about 25 km);
monthly-average precipitation data from ANU CRES for 1951-2000 on a 1/40
th
degree grid
(about 2.5 km);
daily temperature data for eight sites in south-eastern Australia, including four sites over 1,300
metres from around 1960 to present;
snow depth data from Southern Hydro for Rocky Valley Dam from 1935 to 2002;
snow depth data from Snowy Mountains Hydroelectric Authority
http://www.snowyhydro.com.au/data/pdf/snowdepths.pdf for Spencers Creek (1954-2002),
Deep Creek (1957-2002) and Three-Mile Dam (1955-2002).
42
Modifications to CSIRO’s snow model
The CSIRO snow model was developed by Whetton et al. (1996) from the model of Galloway
(1988). The model is used to calculate snow duration and water-equivalent depth from monthly-
average temperature and precipitation, and daily standard deviation of temperature. Empirically-
derived relationships incorporating these parameters are used to calculate accumulation (snowfall)
and ablation (melting and evaporation of snow) for each month. Accumulation depends on monthly
precipitation and the proportion of precipitation falling as snow (which is temperature-dependent).
Ablation is calculated from the number of degree-days above 0
o
C. The snow season begins when
accumulation exceeds ablation, and the snow depth grows until ablation exceeds accumulation. The
snow depth then falls until the excess of ablation over accumulation has been sufficient to melt all
snow, at which point the season ends (Figure 1-A1).
Figure 1-A1: Example of snow cover duration calculation for Mt Buller using the CSIRO snow
model. The day number starts from 1 January. From Haylock et al. (1994).
Whetton et al. (1992) ran the snow model with average temperature and precipitation data on a 7
km grid to estimate average snow cover for present and future conditions. The model was
subsequently improved by Haylock et al. (1994) who included interannual variability by using
monthly-mean temperature and precipitation data on a finer resolution (2.5 km) grid from 1966-
1985. Comparison of observed and simulated interannual changes in snow-cover duration allowed a
more thorough validation and improvement of model performance. One of the limitations of the
model was the underestimation of snow depths and durations at lower sites like Lake Mountain. It
was recommended by Haylock et al. (1994) that “shorter simulated durations (less than around 30
days) should simply be viewed as ‘marginal’ and not interpreted literally”.
A key aim of the current project was to improve the performance of CSIRO's snow model at low-
elevation sites by including daily sequences of precipitation and by using more accurate monthly
input climate data generated at the ANU. A feature of the new model has been to change snow
depth units from water-equivalent to snow-equivalent, thereby giving more relevant results for
resort operators and natural resource managers. As a result of changes to the model, the new version
gives a more realistic prediction of the marginal depths in low snow years.
43
Standard outputs of the snow model are now:
snow depth;
duration of snow cover;
rate of ablation;
snow-to-rainfall ratio;
regional maps and site-specific snow-depth profiles;
probability of snow depth at a given date;
elevation of the snow-line.
A basic test of model performance is how well it reproduces the average snow-depth profile.
Examples are shown in Figure 2-A1. The model performs well at all New South Wales and north-
eastern Victorian sites where observed snow depth data were available for validation.
Mt Hotham (1882 m)
0
20
40
60
80
100
120
140
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
Simulated 1979-1998
Observed 1988-2002
Mt Buller (1708 m)
0
10
20
30
40
50
60
70
80
90
100
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
simulated 1979-98
observed 1979-98
3 Mile Dam (1460 m)
0
5
10
15
20
25
30
35
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
Simulated 1979-98
observed 1979-98
Spencers Creek (1830 m)
0
20
40
60
80
100
120
140
160
180
200
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
Simulated 1979-1998
Observed 1979-1998
Baw Baw (1560 m)
0
10
20
30
40
50
5-Jun
19-Jun
3-Jul
17-Jul
31-Jul
14-Aug
28-Aug
11-Sep
25-Sep
9-Oct
Date
Snow depth (cm)
Observed 1993-2002
Simulated 1979-1998
Deep Creek (1620 m)
0
20
40
60
80
100
120
1-Jun
15-Jun
29-Jun
13-Jul
27-Jul
10-Aug
24-Aug
7-Sep
21-Sep
5-Oct
19-Oct
2-Nov
Date
Snow depth (cm)
Simulated 1979-1998
Observed 1979-1998
Figure 2-A1: Comparison of simulated and observed snow depths at Mt Hotham, Mt Buller,
Three Mile Dam, Spencers Creek, Mt Baw Baw and Deep Creek.
44
However, at Mt Baw Baw, Lake Mountain and Mt Wellington in southern Victoria, average snow
depths and season-lengths were underestimated. This could be due to problems with the input
climate data (temperatures too high and/or precipitation too low) and/or a deficiency in the snow
model at low elevations. The latter is unlikely since the model performs well at low elevations in
New South Wales (e.g. Three Mile Dam). Closer investigation revealed that, while the ANU-
derived temperature values were realistic, the ANU-derived precipitation was not increasing with
elevation as much as expected in this region, and temperatures may be too high. The most likely
reason is the sparsity of high-elevation measurements contributing to the data network in southern
Victoria. In addition, it was also considered likely that the monthly temperature data used in the
snow model to estimate the proportion of precipitation falling as snow would not operate as
accurately in the southern Alps as opposed to the region as a whole. Sensitivity tests indicate that
the observed snow profiles at Mt Baw Baw and Mt Wellington were well simulated when the ANU-
derived precipitation data were increased by 20% and when the temperature data were lowered by
0.5
o
C. The Lake Mountain profile was more realistic when ANU-derived precipitation data were
increased by 20% and when the temperature data were lowered by 1.0
o
C. ANU and CSIRO are
seeking records of high-elevation weather data in southern Victoria so that the ANU precipitation
grid can be improved. In the meantime, for the purposes of this study we have applied the
precipitation and temperature corrections above so that the simulated snow profiles are more
realistic at Mt Baw Baw, Mt Wellington and Lake Mountain.
45
Appendix 2 Intergovernmental Panel on Climate
Change (IPCC) scenarios of global warming
The IPCC (2001a) attributes most of the global warming observed over the last 50 years to
greenhouse gases released by human activities. To estimate future climate change, the IPCC
(2001a) has prepared forty greenhouse gas and sulfate aerosol emission scenarios for the 21
st
century that combine a variety of assumptions about demographic, economic and technological
driving forces likely to influence such emissions in the future. They do not include the effects of
measures to reduce greenhouse gas emissions such as the Kyoto Protocol.
Each scenario represents a variation within one of four 'storylines': A1, A2, B1 and B2. The experts
who created the storylines (described below) were unable to arrive at a most likely scenario, and
probabilities were not assigned to the storylines.
A1 describes a world of very rapid economic growth in which the population peaks around 2050
and declines thereafter and there is rapid introduction of new and more efficient technologies. The
three sub-groups of A1 are fossil fuel intensive (A1FI), non-fossil fuel using (A1T), and balanced
across all energy sources (A1B).
The A2 storyline depicts a world of regional self-reliance and preservation of local culture. In A2,
fertility patterns across regions converge slowly, leading to a steadily increasing population and per
capita economic growth and technological change is slower and more fragmented slower than for
the other storylines.
The B1 storyline describes a convergent world with the same population as in A1, but with an
emphasis on global solutions to economic, social and environmental sustainability, including the
introduction of clean, efficient technologies.
The B2 storyline places emphasis on local solutions to economic, social and environmental
sustainability. The population increases more slowly than that in A2. Compared with A1 and B1,
economic development is intermediate and less rapid, and technological change is more diverse.
The projected carbon dioxide concentrations for the various scenarios are shown in Figure 1-A2.
Figure 1-A2: IPCC (2001a) projected concentrations of carbon dioxide (CO
2
) for the A1, A2, B1
and B2 storylines. IS92a is a mid-range scenario from the IPCC’s previous assessment in 1996.
Units are parts per million (ppm).
46
By incorporating these changes in gas and aerosol concentrations into computer models of the
Earth’s climate, the IPCC (2001a) has estimated a global-average warming of 0.7 to 2.5
o
C by the
year 2050 (Figure 2-A2) and 1.4 to 5.8
o
C by the year 2100. The analysis allowed for both
uncertainty in projecting future greenhouse gas and aerosol concentrations (behavioural uncertainty)
and uncertainty due to differences between models in their response to atmospheric changes
(scientific uncertainty).
0
1
2
3
4
5
6
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57
Year
Global warming oC
Series1
Series2
0
0.5
1
1.5
2
2.5
3
1990 2000 2010 2020 2030 2040 2050
Year
Global warming (
o
C)
Figure 2-A2: IPCC projected range of global-average warming relative to 1990.
Climate simulations indicate that warming will be greater near the poles and over the land, and that
global-average rainfall will increase. More rainfall is predicted nearer the poles and in the tropics,
and less rainfall is expected in the middle latitudes such as southern Australia.
Carbon dioxide, and other greenhouse gases such as methane and nitrous oxide, have a lifetime of
many decades in the atmosphere. About 50% of the carbon dioxide emitted is absorbed by the
ocean and terrestrial biosphere, leaving about 50% in the atmosphere. Greenhouse gas emissions
have been growing since 1750, so atmospheric concentrations of these gases have been rising. Even
if emissions were held constant from today, concentrations in the atmosphere would continue to rise
for decades due to the long lifetime of greenhouse gases. The IPCC has concluded that it is unlikely
that concentrations can be stabilized at present levels. In order to stabilize concentrations at a higher
level than present, and eventually stabilize the world climate at a warmer level, emissions must be
significantly reduced. For example, to stabilize carbon dioxide concentrations at 550 ppm by the
year 2120 would require a halving of current emission rates by 2100 and would result in global
warming of 1.5 to 2.9
o
C (O’Neill and Oppenheimer, 2002).
Uncertainties and confidence levels
As shown in Figure 2-A2, the range of uncertainty in projections of global warming increases with
time. Half of this range is due to uncertainty about human socio-economic behaviour, and
consequent emissions of greenhouse gases and sulfate aerosols. The other half of the range is due to
different climate model responses to these scenarios of greenhouse gases and sulfate aerosols. Each
of the models is considered equally reliable.
47
It is important to note that at present, it is not possible to assign probabilities to values within these
ranges. However, the IPCC (2001b) defined confidence levels that represent “the degree of belief
among the authors in the validity of a conclusion, based on their collective expert judgment of
observational evidence, modeling results and theory that they have examined”. The confidence
levels are:
Very high (95% or greater);
High (67-94%);
Medium (33-66%);
Low (5-32%);
Very low (4% or less).
For the global warming data in Figure 2-A2, we have very high confidence that the lower warming
limits will be exceeded and that the higher limits will not be exceeded.
... For example, mountain surface air temperature observations in Western North America, the European Alps, and High Mountain Asia have shown contemporary warming at an average rate of 0.3 C per decade, thereby outpacing the global warming rate of 0.2 C 6 0.1 C per decade (IPCC, 2018). In contrast, warming has been less intense in other alpine zones such as in Australia, with increases in air temperature of approximately 0.2 C per decade over the last 35 years (Hennessy et al., 2003;Green and Pickering, 2009), the Tropical Andes (0.13 C per decade; Vuille, 2013), and mountain chains of South and East Africa (0.14 C per decade; Pepin and Seidel, 2005). Climate warming also has shown contrasting trends between Arctic and Antarctic regions (see Meredith et al., 2019 for a review). ...
... For example, by the end of 21st century the average mean annual temperature increase per decade is estimated to be 0.49 C for the mountains of North America (,55 N;Noguès-Bravo et al., 2007), 0.36 C for the European Alps (Gobiet et al., 2014), 0.45 C in Caucasus and Middle East (Babaeian et al., 2015), and 0.34 C for the Southern Andes (Noguès-Bravo et al., 2007). Other predictions for temperature increase report 10.6 C to 12.9 C for the Australian alpine areas (Hennessy et al., 2003) by 2050 relative to 1990, and 11.8 C to 14 C in the European Alps for the period 2051À80 (Zimmermann et al., 2013). As the Earth approaches a warming of 2 C, the Arctic and Antarctic may reach 4 C and 2 C mean annual warmings, respectively, relative to 1981À2005 (Post et al., 2019). ...
Chapter
Alpine and arctic environments are predicted to be strongly influenced by climate change because their cold-adapted species may be sensitive to rapid warming. Genetic diversity, phenotypic plasticity and dispersal ability of seeds might be crucial for species to persist and/or migrate in these habitats. We reviewed the literature to synthetize current knowledge on seed-trait responses to direct and indirect effects of climate warming. Most experimental and observational studies we reviewed have focused on the effects of warming on seed germination, while other seed functions have received less attention. Overall, there is compelling evidence that increasing temperatures and water stress decreases the number, size and germination of seeds, suggesting that the net effect of warming will depend mostly on changes in water availability. These responses to climate change have been evaluated mainly in alpine temperate and arctic life zones, while alpine-tropical mountains have been largely neglected.
... The Australian Alpine region has experienced a significant decline in snow cover with large year-to-year variability (Green and Pickering, 2009) and a decrease by up to 24% in precipitation and an increase in the frequency of drought and severe storms has been forecasted (Hennessy et al., 2003). Further, recent observed trends have shown that since the mid-1990s southeast Australia has experienced a decline in cool-season (AprileOctober) rainfall (e.g., Freund et al., 2017;Rauniyar and Power, 2020). ...
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Oxygen isotopic (δ¹⁸O) variations in stalagmite records have the potential to provide new insights about past climates beyond the instrumental record. This paper presents the first high-resolution oxygen isotope time series of three coeval stalagmite records from the alpine region of south-eastern Australia covering the period 1922–2006 CE. We use extended surface and cave monitoring datasets, petrographic investigation, modelled recharge time series and farmed calcite precipitates to assess the controls on speleothem δ¹⁸O and investigate the coherence between three records from Harrie Wood Cave. The drip water response to recent interannual rainfall variability shows that cave drip water Cl⁻, δ¹⁸O and drip rate display a clear response to an increase in rainfall recharge. It is demonstrated that stalagmites from the same drip sites also record variability in interannual recharge, where an increase in δ¹⁸O values is observed with lower recharge, while a decrease in δ¹⁸O values correspond to higher recharge amounts. The three stalagmite δ¹⁸O records are in broad agreement, showing common responses to relatively higher recharge between 1945 and 1995 CE and the low recharge periods between 1937 and 1945 CE (World War II drought) and late 1996 to 2006 CE (beginning of the Millennium Drought). However, differences in the magnitude of the relative response of each stalagmite δ¹⁸O record varies. Based on evidence from our cave monitoring study and farmed calcites, we conclude that the differences between the three stalagmite records is attributed to variability in the contribution of preferential flows during recharge events and the store reservoir volume supplying the drip site. When the δ¹⁸O decreases in response to enhanced recharge, the speleothem δ¹³C also decreases, and this is interpreted to reflect a soil respiration response to changes in soil moisture availability due to recharge. Hence, stalagmite δ¹⁸O from the Australian alpine region can be applied to reconstruct periods of relatively higher and lower rainfall recharge and thus extend our knowledge of the timing and relative magnitude of droughts as well as past periods of higher recharge in this region.
... König and Abegg (1997) or Moen and Fredman (2007), Tranos and Davoudi (2014)), others include snowmaking in the evaluation of future snow reliability, (e.g. Scott et al. (2003); Hennessy et al. (2003); Scott et al. (2006); Scott et al. (2007), Steiger and Mayer (2008); Bark et al. (2010); Teich et al. (2007), Steiger and Abbeg (2013), Bausch et al. (2017), and Spandre et al. (2018)). The following common conclusions can be drawn from these studies: While natural snow reliability is predicted to deteriorate significantly in the future, for most areas snowmaking will be feasible still in the next decades, but also conditions for snowmaking are becoming less reliable. ...
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The paper presents a Regional Distribution Model (RDM), simulating the distribution of alpine skiing tourists in Europe, based on climate change impacts. For 71 European NUTS 3 winter tourism regions possible substitution effects on basis of climate change linked snow reliability and of two behavioural adaptation strategies that result in three different scenarios on touristic market connectedness are investigated. Results for 2050 are presented for European countries and on NUTS 3 level. Without adaptation strategies essential losses in overnight stays in all regions have to be expected (Scenario 1). Allowing shifts to more snow reliable areas (Scenario 2), these losses are significantly reduced, some countries and winter tourism regions profit from climate change by showing small increases. If skiers are allowed to shift also to other tourist activities outside NUTS 3 winter tourism regions within Europe (Scenario 3), all countries and winter tourism regions show again losses in overnight stays, which are usually lower than those for Scenario 1. Management implications The paper gives detailed information on Climate Change related competitiveness of touristic NUTS III regions. It argues that multi-regional modelling of tourism activities that take the connectedness of tourism markets into account, are an important part of climate change impact modelling for the tourism sector. We predict, that due to COVID-19 travel restrictions that may be a phenomenon that will occur more often in future, these kind of tourism models that explicitly model, the tourists of which region originate from which other region, will be used more often, since the connectedness of markets might no longer be a static variable.
... The present study showed that, firstly, the amounts of MSD in the study area are tangible but decreasing. The result of this research is somewhat in line with the findings of [74], which depicted the impact of climate change on snow conditions in mainland Australia. Variability in SCA is highly regional, similarly to the trends identified for the European Alps [75]. ...
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This study detected the spatial changes in Snow Cover Area (SCA) over the Snowy Mountains in New South Wales, Australia. We applied a combination of Object-Based Image Analysis (OBIA) algorithms by segmentation, classification, and thresholding rules to extract the snow, water, vegetation, and non-vegetation land covers. For validation, the Maximum Snow Depths (MSDs) were collected at three local snow observation sites (namely Three Mile Dam, Spencer Creek, and Deep Creek) from 1984 to 2020. Multiple Landsat 5, 7, and 8 imageries extracted daily MSDs. The process was followed by applying an Estimation Scale Parameter (ESP) tool to build the local variance (LV) of object heterogeneity for each satellite scene. By matching the required segmentation parameters, the optimal separation step of the image objects was weighted for each of the image bands and the Digital Elevation Model (DEM). In the classification stage, a few land cover classes were initially assigned, and three different indices—Normalized Differential Vegetation Index (NDVI), Surface Water Index (SWI), and a Normalized Differential Snow Index (NDSI)—were created. These indices were used to adjust a few classification thresholds and ruleset functions. The resulting MSDs in all snow observation sites proves noticeable reduction trends during the study period. The SCA classified maps, with an overall accuracy of nearly 0.96, reveal non-significant trends, although with considerable fluctuations over the past 37 years. The variations concentrate in the north and south-east directions, to some extent with a similar pattern each year. Although the long-term changes in SCA are not significant, since 2006, the pattern of maximum values has decreased, with fewer fluctuations in wet and dry episodes. A preliminary analysis of climate drivers’ influences on MSD and SCA variability has also been performed. A dynamic indexing OBIA indicated that continuous processing of satellite images is an effective method of obtaining accurate spatial–temporal SCA information, which is critical for managing water resources and other geo-environmental investigations.
... The tree line in this region is between 1800 and 1900 m above sea level (Pickering & Venn, 2013). Over the past 60 years, the mean temperature in this region has increased by 0.02°C per year (Hennessy et al., 2003;Sritharan et al., 2021; Figure S1a) and snow regimes are F I G U R E 1 Site location of our study within (a) Australia in (b) Kosciuszko National Park, which is also used as the boundary for selecting historic species records from the Atlas of Living Australia. (c) Shows the transects taken during fieldwork within Kosciuszko to determine modern distribution of alpine species. ...
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Full-text available
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... These climatic changes are occurring particularly rapidly at higher elevations and above-average warming is predicted in the alpine zone (Beniston 2003;Kullman 2004;Olson et al. 2016). As a consequence, significant reductions are predicted in both the frequency and duration of snow cover in the Australian Alps (Whetton et al. 1996;Whetton 1998;Hennessy et al. 2003;Ji et al. 2017;Di Luca et al. 2018) and indeed significant changes in regional climate, including declining snow cover, have already been reported ). ...
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The Australian Alps contain an assemblage of soil types that is unique on the Australian continent. The above‐ground ecosystems of the Australian Alps have received considerable scientific attention but research relating to the nature of its soils has been much more limited. A fuller understanding of the role of soils in these ecosystems is required to inform effective management strategies. This review was undertaken to assess existing research on soils in the Australian Alps. We aimed to summarise our current knowledge of their nature, distribution and characteristics, to examine the services they provide and to assess their vulnerability to the range of threats that exist to the soil resource both local and external. Soils of higher elevations, namely Transitional Alpine Humus Soils, Alpine Humus Soils and upland Peat Soils are particularly important to the ecology, hydrology and potential carbon storage of the region, yet our understanding of the nature, formation and functioning of these soil types remains weak. A series of knowledge gaps and research priorities are identified, relating to basic knowledge needs on the formation, distribution and function of these soils, particularly their microbial populations and the impacts of specific threats (i.e. climate change, grazing, fire, visitors, infrastructure, feral animals and pollution).
... (Burki et al., 2003). Hennessy et al. (2003) aimed to determine the impacts of climate change on snow cover and precipitation in mountain areas in Australia. It has been deduced that the thickness of snow, the duration of snow on the ground, and the rate of precipitation would decrease in mountainous areas in 2020 and 2050. ...