Season 2016 snow depth prediction

Yes, very very late, but here we go. For those who just want the answer, the Spencers Creek¹ peak snow depth for 2016 will be 159 ± 44 cm.

Regards the delay, I’ve now given up on trying to use the ERSSTv4 sea surface temperatures, because they produce a prediction model correlation significantly poorer than my previous mark-IV model (r² 0.47 vs 0.50). I don’t know why.

My mark-IV prediction model goes as follows:

Spencers Creek peak depth (cm) = 899 – 0.311 x year – 13.8 x AAO + 2.09 x SOI – 8.80 x IOD – 5.36 x PDO – 143 x SST + 534 x aerosol

The parameters are explained in the notes at the end. Click on the graphs for sources.


Calendar year

We’re up to 2016, so take off another centimetre for the fossil fuel apologists.²


Antarctic oscillation (AAO)³

This is always the hard one. AAO exhibits moderate persistence (months), but it can also change suddenly. There is no meaningful numerical prediction (this prediction is short range — it’s just AAO calculated from the 15-day GFS output). Here’s how AAO is looking (linked image will update):

Antarctic oscillation

We want the winter average, the “3-month running mean” to the end of August. It’s going to be positive unfortunately (which is negative for our snow), reflecting the long term global warming uptrend in this parameter. I’m going with +1.5.


Southern oscillation index (SOI)⁴

SOI reflects ENSO, which appears to be flicking rapidly from El Niño straight into La Niña (meaning SOI generally above about 10, i.e. plus one monthly standard deviation, and staying there for several months). The Australian Bureau of Meteorology’s (BOM’s) POAMA model has this prediction (static image link; remember a negative Nino3.4 temperature anomaly correlates with a positive SOI):

Internationally, the various model predictions look like this (the 2016 winter average that we want is at the fourth tick in from the right edge; static image link):
IRI dynamic models

I’m not quite convinced on a winter La Niña yet. I’m adopting a winter average SOI of just +5, which is pretty good for our snow.


Indian ocean dipole (IOD)⁵

BOM’s model has this (static link):

The model is looking for a rapidly developing, strong negative IOD event, which is very good for our snow. I’m going with a fairly conservative -0.5.


Pacific decadal oscillation (PDO)⁶

My model uses the two year average to the end of winter. The PDO seems to be stuck firmly in positive mode now, despite the recent dissipation of the north-west Pacific ‘blob’. I’m adopting +1.7, which is a little negative for our snow.


Sea surface temperature (SST)⁷

This is the ugly one:

The trend in sea surface temperatures in our area of interest is relentless, and the correlation of that with our season peak snow depth is the strongest of the lot. One degree Celsius rise in local sea surface temperature loses us about a metre of expected peak snow depth.

Our winter SSTs have been near or below trend in recent years (they could easily have been much worse). But recent monthly SSTs have an above-trend look:

SST_box SST_monthly_recent

That data is from the Hadley Centre’s HadISST dataset through March. Here’s what NOAA’s CFSv2 model is predicting for winter (anomalies vs 1999-2010, add about 0.5°C for 1951-1980 in our region; will update):

CFSv2 SST anomaly prediction for June-July-August 2015
(vs 1999-2010, add 0.5°C for 1951-1980)

And here’s BOM’s SSTs over the last available week (anomalies vs 1961-1990, add 0.1°C for 1951-1980; static link):

Latest BOM weekly SSTs vs 1961-1990, add 0.1°C for 1951-1980

As seems to have become standard in recent years, things look warm in the Tasman but not so much in the Great Australian Bight. A tough call as always, but I think the chances are our local SST anomalies will be a little above trend, which is of course strongly negative for our snow. I’m adopting a winter average SST of +0.7°C vs the 1951-1980 average (detrending adjustment to this is not really necessary, because the detrending used in my model rotates around 2015).


Southern Hemisphere stratospheric aerosols⁸

There have been no major volcanic eruptions affecting the stratosphere, but low levels of aerosols have persisted lately (paywalled text) from minor eruptions and pollution. For a real-time guide to the stratospheric situation, the high-altitude Mauna Loa data (Hawaii … yes I know, not SH) provides a rough indication:

Optical thickness is the negative log of transmission, so the left panel is an inverted view. The right panel is a direct stratospheric aerosol measurement by laser probing (LIDAR). I’m adopting 0.003, the same as last year.



In the mark-IV model, those seven parameter choices give a 2016 best-estimate peak snow depth of 159 cm — not great, but not terrible by current standards. The one standard deviation (‘±1σ’) error is 44 cm as before, so the range 115 – 203 cm would be expected to include about two-thirds of likely outcomes, if the parameters were perfectly known (they’re not!).

For comparison, a statistically thorough naive prediction would yield a 2016 peak depth of 175 cm, with a ±1σ range from about 120 – 240 cm. On that basis my prediction is for a peak depth a little below average (whatever “average” means with our alpine climate changing so rapidly). It looks like this:

Spencers Creek peak snow depth pre-season prediction for 2016

Spencers Creek peak snow depth pre-season prediction for 2016



  1. Spencers Creek near Charlotte Pass, NSW, Australia, midway between Perisher Valley and Thredbo; data courtesy Snowy Hydro Limited.
  2. The current raw downtrend would be about 1 cm/yr allowing for the non-linearity. Only 0.3 cm/yr appears in the calendar year term of the prediction model, because it’s a linear model and because there are global warming trends in some of the other parameters.
  3. Antarctic oscillation (AAO), also called “southern annular mode” or SAM, is a measure of how tightly the circumpolar winds (“polar vortex” in one usage) blow around the pole. A loose pattern (negative AAO) leads to more polar storms reaching southern Australia, and more snow. The winter average AAO is used — the average of June, July and August.
  4. Southern oscillation index (SOI) is the difference between Tahiti and Darwin surface atmospheric pressure expressed as monthly standard deviations times ten. SOI is an indicator of the El Niño Southern Oscillation (ENSO), an east-west quasicycle in equatorial Pacific Ocean surface temperature and wind patterns which correlates with precipitation across much of Australia, including with alpine snow. A positive SOI is associated with more (and wetter) Australian snow. The winter average is used.
  5. Indian ocean dipole (IOD) is an ENSO-like variation in the smaller Indian Ocean, which correlates with winter precipitation across southern Australia, including with alpine snow. Negative IOD is associated with more snow (the sign is consistent with SOI, but we’re on the opposite side of the Indian Ocean). The winter average is used.
  6. Pacific decadal oscillation (PDO) is a long-cycle, largely north-south variation in the Pacific Ocean, which interacts with (and perhaps partially mediates) ENSO. Negative long-average PDO is weakly correlated with more Australian snow. The 2-year average to August is used.
  7. Sea surface temperature (SST) is that in the Great Australian Bight and northwest Tasman Sea, averaged over the box: latitude 30-37°S, longitude 115-160°E, and expressed as degrees Celsius anomalies from the 1951-1980 mean, detrended about 2015. Cool SSTs correlate strongly with more snow, but unfortunately local SSTs are rising rapidly with global warming. The winter average is used. More at my post here. (I detrend the SSTs to make the factors in the model equation appear more sensible, and to make them more comparable with previous versions. Because of the model linearity, linear detrending doesn’t alter the regression outcome — it just shifts the SST trend effect across to the “calendar year” term.)
  8. Stratospheric aerosol optical thickness is the average for the Southern Hemisphere at 550 nm wavelength (green visible light). Large optical thicknesses from big volcanic eruptions correlate with big snow seasons. The winter average is used. More at my post here.

1 comment to Season 2016 snow depth prediction

  • richardoz45

    157.5 cm actual v. 159 cm. estimated.

    Good estimate but sad snow. Manufactured snow saved the day temporarily. Maybe the global warming deniers should try skiing. All skiers should get right behind renewable energy and help shut down large scale fossil fuel burning for energy. We can’t depend on volcanic eruptions and the misery they often cause to give us good snow.