Season 2014 snow depth prediction #4

One of a series, also see editions: #1, #2, #3, #5

Time for a real prediction … the Spencers Creek¹ peak depth for season 2014 will be 170 ± 45 cm.

Last time I described my new prediction model incorporating southern sea surface temperature. The new formula goes like this:

Spencers Creek peak depth (cm) = 1248 – 0.49 x year – 19.3 x AAO + 1.63 x SOI – 11.9 x IOD – 9.06 x PDO – 130.9 x SST

 

To the parameters…

 

Calendar year

2014

Antarctic Oscillation (AAO)

This is the hardest one.  AAO2 exhibits moderate persistence (months), but it can also change suddenly, as it did to our benefit early last snow season.  There is no meaningful numerical prediction that I’m aware of (this prediction is short range — it’s just AAO calculated from the 15-day GFS output).

We want the “three month running mean” to the end of August (the winter average).  I reckon it’ll be positive (which is negative for our snow), but not by much.  I’m going with +0.5.

 

Southern Oscillation Index (SOI)

 

SOI reflects ENSO, which I think is headed towards El Niño (meaning SOI generally below about -10 — that is minus one monthly standard deviation — and staying there for several months).  The Australian Bureau of Meteorology’s (BOM’s) POAMA model has this prediction:

 

Internationally, dynamic numerical model predictions look like this (note that the Nino3.4 index has the opposite sign to SOI; June/July/August for 2014 is at the second tick from the right edge):

This could be very bad, but it’s hard to tell whether that will happen quickly enough to matter for this season.  I’m adopting a winter average SOI of -10, which is bad but not terrible.

 

Indian Ocean Dipole (IOD)

BOM’s model has this:

It looks like we can expect a slightly positive IOD over June, July and August, which is slightly negative for our snow depth.  I’m adopting +0.2.

 

Pacific Decadal Oscillation (PDO)

We want the two year average to the end of August, most of which is already in.  The current average from September 2012 to February 2014 is -0.52, but if the PDO index averages around +0.5 for the remaining months (February was +0.97), the 2-year average we want will come in at -0.29.  I’m adopting -0.3, which is very slightly positive for our snow.

 

Sea Surface Temperature (SST)

Sea surface temperatures in our area of interest have been rising again after falling sharply in December:

SST_box        SST

 

That data is from the Hadley Centre’s HadISST dataset through February.  Where’s it headed?  Here is BOM’s SST anomaly plot for the last week (will update; values vs the 1961-1990 average — add 0.13°C to get 1951-1980):

Things are very warm in the north-west Tasman Sea, but not so much in the Great Australian Bight.  I think the chance of our southern SST anomalies rising back towards recent unprecedented high levels are substantial.  I’m adopting +0.5°C.

 

Outcome

In the new model, those six parameter choices give a 2014 best-estimate depth of 170 cm.  The 1-σ error is about 45 cm, so the range 125 – 215 cm would be expected to include about two-thirds of possible outcomes, if the parameters were perfectly known (they’re not!).

For comparison, a statistically thorough naive prediction yields a 2014 peak depth of 177 cm with a ±1-σ range from about 120 – 240 cm.  So you could say my prediction is for a slightly below average peak depth, whatever “average” means with our alpine climate changing so rapidly.  Also, for what it’s worth, my previous model would have predicted a 2014 peak snow depth of just 161 cm.  Here’s a comparison:

Prediction_v_performance_2014_new

 

Mr Peterson has “fluked” (I think) two years in a row.  We’ll see how he goes this year with his (rather precise, and rather high) 201.2 cm.

 

Notes:

1. Spencers Creek near Charlotte Pass, Australia, midway between Perisher Valley and Thredbo; data courtesy Snowy Hydro Limited.

2. 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.

3. Southern Oscillation Index (SOI) is the difference between Tahiti and Darwin surface atmospheric pressures expressed as monthly standard deviations x10. 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 some say wetter) Australian snow. The winter average is used.

4. Indian Ocean Dipole (IOD) is an ENSO-like variation in the smaller Indian Ocean, which correlates with precipitation across southern Australia, including with alpine snow. The winter average is used.

5. Pacific Decadal Oscillation (PDO) is a long-cycle, mostly north-south variation in the western Pacific Ocean, closely related (but not equivalent) to yet another claimed mode called the Inter-decadal Pacific Oscillation (IPO). Negative long-average PDO is weakly correlated with more snow. The 2-year average to August is used.

6. 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 average annual cycle, detrended about 2014. The winter average is used. (I detrend the SSTs to make the regression equation parameters appear more sensible, and to make them more comparable with the previous equation. Detrending doesn’t alter the regression outcome.)

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