Australian Alps snow depth history – 78 years of noisy data but little long term trend

A reader asked me if I had any historic snow depth data for Australia and drew my attention to Chiefios blog.
There is a Snowy Hydro webpage with some annual snow depth charts and I have used those charts from Spencers Creek to build a maximum depth time series.
I also have a 1990 report – “The South East Australian Alpine Climate Study” – by CSIRO, University of Melbourne and Alpine Resorts Commission. That has a graphic on page 19, Fig 2.4 Annual maximum snow depth (water equivalent in cm) for Rocky Valley Snow Pole Line 1935 – 1989.
I have digitised those data and let Excel plot the two time series below.

As usual the data do not support the normal media predictions that the ski industry is doomed. We know the Australian ski-fields do not have great heights of mountains above them – the pioneers worked that out – no news there.
But the data does not show any sign that “Global Warming” is wiping out the Australian ski resorts.

9 thoughts on “Australian Alps snow depth history – 78 years of noisy data but little long term trend”

  1. I tried to find, without success, data on snowfall as opposed to snow cover or depth. The reason being to look for an aerosol and BC/solar insolation effect (the 2 should diverge if such an effect exists). Many people including me, think this is the main reason for the Arctic – Antarctic sea ice divergence.

  2. Cloud cover must be an important factor as it is the source of snow and shade. The Chacaltaya Glacier of Bolivia faded away due to a lack of replacement snow tho’ Al Gore used it as an an example of global warming which in this case was quite wrong because local thermometers show no significant warming at 16,000ft.
    On Australia’s Main Range there is a big difference between a sunny September day and a cloudy one. The intense sunlight causes rapid melting.
    Ed note: If you make a time chart of South East Australian winter rainfall anomaly here – you see the increase in 1950 – possible to make a case that broadly the snow depth chart follows winter precip. Of course with increased rain there should be more cloud anyway.

  3. Hmmm, I like what you’ve done there with that graph.
    I bet when you plot the average of all of that data (incorporating both lines), you get a straight line right? Implying no worsening trend.
    Might be worth mentioning that the two locations presented are at different elevations. Rocky valley at 1650m and Spencer at 1820m.
    So looking at the data presented, you follow a downward trending Rocky Valley, then try to conceal that by throwing in Spencer (which is higher elevation and greater snow depth) data to make it a little “noisy”.
    Dear oh dear….

  4. There is a downward trend in the Spencers Creek Data, but hard to see in the noise. For example take a look at the average peak snow depth for each decade since 1956 :
    1956 – 1965 = 226cm
    1966 – 1975 = 208cm
    1976 – 1985 = 194cm
    1986 – 1995 = 203cm
    1996 – 2005 = 190cm
    2006 – 2015 = 164cm

    I have also observed a similar downward trend in data from Mt Buller too over the same period. To be continued…

  5. Hi Warwick,

    Have you run a linear regression on your excel plot?
    I can see from the data you’ve presented, despite the noise, that there will be a decreasing trend for both data sets. You could confirm this for yourself by simply adding a trendline in excel for each data set.

    If you fail to find this, you may need to look at your own methodology for errors, as there are peer-reviewed publications that have sadly demonstrated a significant decrease in snow depth using the same data as you.

    Green’s 2010 article ‘Alpine Taxa Exhibit Differing Responses to Climate Warming in the Snowy Mountains of Australia’ in the Journal of Mountain Science is a good place to start.

  6. Shorter ski seasons, lower snow depths and increasing reliance on artificial snow making are reported by skiers worldwide. The data is not so much noisy but loud. A search on ‘ski resort future’ or similar will give you a consistent list of accounts that confirm global warming.

  7. If you use simple maths theres a rolling 30 year trend downwards , if theres no exceleration or decrease the trend might look something like this until there is zero snow fall in less than 200 years from now: the sad part is as a keen skier the snow fall vs what stays on the ground is even less particularly if theres liquid snow falling inbetween so the trend looks like being no skiing in a lot shorter time with a lot more season failures due to less below zero days and more rain eg liquid snow.

    1956 – 1965 = 226cm
    1966 – 1975 = 208cm
    1976 – 1985 = 194cm

    1986 – 1995 = 203cm
    1996 – 2005 = 190cm
    2006 – 2015 = 164cm

    2016 – 2025 = 180cm
    2026 – 2035 = 172cm
    2036 – 2045 = 134cm

    2046 – 2055 = 157cm
    2056 – 2065 = 154cm
    2066 – 2075 = 104cm

    2076 – 2085 = 134cm
    2086 – 2095 = 136cm
    2096 – 2105 = 74cm

    2106 – 2115 = 111cm
    2116 – 2125 = 118cm
    2126 – 2135 = 44cm

    2136 – 2145 = 88cm
    2146 – 2155 = 100cm
    2156 – 2165 = 14cm

    2166 – 2175 = 65cm
    2176 – 2185 = 82cm
    2186 – 2195 = 0cm

    Predictions on the future based on historical facts are often the safest gauge , if the emerging populations all want the american dream of car ownership and white goods and big macs and pancakes with syrup which they clearly want and who could blame them we have that why cant they , so middle class comforts are the driving force behind polution and clearing forests to grow beef and palm oil , all the cars and electrical goods producing co2 from there power suply and production all equals one thing… this historic set of numbers will be conservative as the volume of greenhouse causes grow its likley Australia only has 35 years of skiing before the rain and plus zero days wash out july and august falls.
    David Pillinger

  8. David Camacho

    One of the biggest reasons for lower snow depths and shorter seasons is increased skier numbers and increased descents/day, carving away the snow which is not totally overcome by piste grooming machines.

    Every time a skier descends, they carve away some snow. This is most extreme on warm sunny days. But also important after fresh snowfalls on thin bases. Especially if the season opens before a firm natural base has developed.

    Data should be limited to stations where no skiers affect the snowbase, as then you have eliminated an enormous variable from data.

    Just like quadrupling the size of resort villages creates an urban heat island effect….

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