The coming zero snow depth season #1

Spencers_correlation_SST_anomalies

I guess lots of people hear global warming predictions of a couple of degrees increase and think, so what? Take a close look at the snow depth prediction equation. The factor on southern sea surface temperature¹ (SST) is -130. That’s right, a 1°C increase in southern ocean temperature decreases our season peak, high-altitude snow depth² . . . → Read More: The coming zero snow depth season, #1

Season 2014 snow depth prediction #4

Prediction_v_performance_2014_new

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 . . . → Read More: Season 2014 snow depth prediction, #4

The gathering El Niño...

wkxzteq_anm

For us, El Niño1 means drought conditions and little snow. Unfortunately the Pacific could well be brewing up a strong one right now. For our 2014 snow season, much depends on how quickly it develops (if it does at all).

The Southern Oscillation Index2 has already plummeted well into El Niño territory, and so far . . . → Read More: The gathering El Niño…

Season 2014 snow depth prediction #3

Spencers_predictions_MkIII

Updated…

OK, here’s how the new prediction model including southern sea surface temperatures goes:

 

The correlation coefficient rises to a healthy 44% (the standard error falls to 47 cm). The new model really only misses badly around Pinatubo1 (1991 & 1992) and the 2006 wipe-out.

Basis:

My previous model used these parameters:

calendar . . . → Read More: Season 2014 snow depth prediction, #3

Season 2014 snow depth prediction #2

Spencers_peak_distribution_3

 

While we wait for the prediction parameters to firm up (El Niño is now even money), it’s worth checking what a naive statistical prediction for 2014 looks like. By naive I mean one that ignores influences external to the snow depth data (like El Niño), not one with weak statistics.

The obvious approach, . . . → Read More: Season 2014 snow depth prediction, #2

Earth temperature

Temperature of Planet Earth

 

The average surface temperature of our planet over the ~540 My of the Phanerozoic (since the first proliferation of complex life forms) is a fascinating topic, with some immediate relevance to what we face in the decades and centuries ahead. One of the more startling achievements of the last 30 years of climate . . . → Read More: Earth temperature

Sea surface temperature and snow

SST_box

 

It’s hardly news to suggest that our snow season is affected by Southern Ocean sea surface temperatures (SSTs). The evidence is mostly anecdotal: some claim it’s the Great Australian Bight which controls things, others say it’s the southern Indian Ocean. Here I take a Hadley Centre sea surface temperature reconstruction¹ and compare SST . . . → Read More: Sea surface temperature and snow

Season 2014 snow depth prediction #1

Prediction_v_performance_2014

Readers will know that I don’t make my peak depth prediction for the Australian snow season until April, when the required parameters become clearer, but I see that Mr Peterson’s prediction for the 2014 Spencers Creek peak depth is in at a generous 201.2 cm.

Peterson’s method users cycles he detects in the Spencers Creek . . . → Read More: Season 2014 snow depth prediction, #1

Warming accelerates!

Far from "pausing", warming this century has been above trend.

Technical writing rule #303: never, ever use that exclamation thing. Yet there are times when absurdity demands an ugly response. The meme that claims a “pause” in global warming has become so pervasive that even experienced scientists — who ought to know better — have been spouting it¹.

I’m going to show here that recent . . . → Read More: Warming accelerates!

What pause?

Global temperatures since 1970

Global temperatures since 1970

 

One thing I’ve learned in 30 years working with real climate and hydrological data is to always check the maximum available temporal resolution¹. If there’s daily data, don’t just look at monthly averages. If you’ve got monthlies, don’t just rely on annual data. Doing so destroys information, even when . . . → Read More: What pause?