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A chaotic beast, probably: wacky weather and climate forecasting

“Prediction is very difficult. Especially about the future” – so said Neils Bohr, the Danish physicist and 1922 Nobel Prize winner. And you know what? I think the bloke was onto something there, especially…

A precise weather forecast, months in advance, will never be possible. Matt Smith

“Prediction is very difficult. Especially about the future” – so said Neils Bohr, the Danish physicist and 1922 Nobel Prize winner.

And you know what? I think the bloke was onto something there, especially when it comes to seasonal climate outlooks.

In the past 12 months we’ve seen – as Today Tonight likes to describe it when they interview myself and my colleagues at the Australian Bureau of Meteorology – truly wild and wacky weather.

But no-one tells you this wackiness is a combination of weather and climate. And that, despite all the calls for a precise weather forecast for everyone’s town/farm/dam many months in advance, it will never be possible.

Why?

Well, first of all, let’s get the weather and climate thing sorted. As the saying goes: climate is what you expect, weather is what you get. And indeed that’s pretty much it – climate is just the averaging of weather over time.

But when it comes to climate forecasts – in this case we’re talking about seasonal forecasts – there are some fundamental differences between what you can do for weather and what you can do for climate.

*Melody*

Predicting weather and predicting climate are actually quite separate beasts. Sure, they both use stonking big computers and telephone-book-thick reams of Fortran code (yes, Fortran, for those sniggering up the back – it’s still the fastest floating point arithmetic language). But the divergence is in how the forecasts are made.

Climate is all about forces. Not “F=ma” type force, but rather a push towards one state (such as wetter and cooler over Australia) or another. Think of the sun – that’s your classic climate driver.

As it moves from overhead in the northern hemisphere to overhead in the southern hemisphere it “forces” a change in the climate. Hence the seasons we get each and every year. In fact, you could say the calendar on your wall is your very own climate model – how cool is that?

So when it comes to producing a seasonal climate outlook, the important things to look at are the climate drivers. The biggies for myself and colleagues are the oceans, as they store a massive amount of heat (and “evaporable” moisture), and will in turn force the atmosphere to respond one way or another.

So, for a climate forecast, we ask: “Is the Pacific Ocean warmer or cooler than normal?” In other words, do we have El Niño, neutral or La Niña? Are the sea surface temperatures around Australia warmer or cooler? What’s the state of the Indian Ocean? All these factors (and more) will try and force the atmosphere to alter slightly from its mean state.

These drivers and their strength are known as the “boundary conditions”, because they set the limits of what’s possible over the season ahead.

Cyclone Yasi NASA

Look at what happened in the 2010 wet season. Late in the year the Coral Sea was very warm – typical of a La Niña – which nudged the region considered “favourable” for tropical cyclone development closer to the Queensland coast. The result? Tropical Cyclones Tasha, Anthony, then the biggie, Yasi all did their darndest to ruin the sunshine state’s summer.

To highlight what a boundary condition can do, tropical cyclones have only ever crossed the Queensland coast multiple times in the one wet season during La Niña events.

So what about weather forecasts?

Weather forecasts

With a weather forecast, while it’s important to have the boundary conditions about right, the most important things are your initial values. This is the data you plug into the model from every single spot you can get your hands on.

Manual observations, satellites, floating buoys, aircraft, ships, automatic weather stations, ocean gliders, radars, weather balloons – all of it goes in to paint an extremely detailed picture of the three-dimensional current state of the atmosphere and its weather at a precise time.

macton

Now, the weather being a chaotic beast, any little errors in these observations have the potential to become big ones – it’s like the butterfly flapping its wings in the Amazon causing a cyclone over India a week later.

OK, that’s stretching it a bit, but fundamentally that’s what chaos says can happen: a cascade of ever-increasing errors/impacts.

Hence if you have your physics right (just as for a climate outlook) and you have really good initial conditions, the “chaos” (or the explosion of the errors) is minimised for a few days and we can get really good forecasts on small time and space scales.

But this will only last a few days. Typical accuracy is about a week, or approximately the time it takes to cycle from one weather pattern to the next. Beyond that, the atmosphere “forgets” what happened a week ago and starts to head down its own merry path.

So if it’s a billion-to-one to predict exactly what will happen on a particular day in a few months time, how can we trust a seasonal forecast? Well, a colleague of mine came up with a great analogy.

Take one hammer. Take one vase. Bang ‘em together. What will you get?

The drover’s dog could tell you the answer is a smashed vase. (That’s the climate-forecasty bit; the known forces/drivers/boundary conditions have given a very predictable, general, result: an ex-vase.)

drubuntu

But Professor Bohr’s wisest owl couldn’t be expected to tell you where every single shard of pottery will land. That’s nigh-on impossible (well, unless you knew the exact location and force on every particle in the universe – then it’d be a doddle). Not being able to predict where every piece will land doesn’t discount the fact the hammer is the natural enemy of the vase.

Similarly, just because you can’t predict exactly where rain will fall on a particular Thursday in five weeks' time (or even five days' time), that doesn’t mean we can’t predict what will generally happen that month or season.

Playing with probabilities

Any decent climate outlook worth its Fortran code will be given as a probability.

Why?

Research shows us that, at best, only about 70% of our year-to-year seasonal climate is predictable; the other 30% is chaotic random (weathery) stuff. (It must be one of the few fields where you expect to be “wrong” occasionally and just have to cop it.)

A typical climate forecast will calculate there’s a 60% chance of more rain than normal in a general region next season.

Does this help?

Well, we know that hedging one way or another, over time, will let you win in the end.

Zdenko Zivkovic

Think of it this way – a European roulette wheel has one “0”, 18 red and 18 black slots. If you only bet one colour and the payout is double for a win, the casino would still win in the end because they have the 0. (In reality, when odds and payouts are taken into account, a casino has about a 2.7% edge).

The casino knows it will always make a profit if the game is played often enough.

Climate forecasts give you far better odds that 2.7% and, used wisely over a period of time, will give an edge and ultimately a win overall.

And in a changing climate, an edge is what we all need.

Which leads me to proclaim the following:

“Prediction is very difficult, especially about the future, but at least we have stonking great computers to sway the odds.” – Andrew Watkins, 2012. (Don’t think that will win me a Nobel Prize though.)

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