What happened to the scorching El Niño summer we were bracing for? Why has the east coast of Australia been drenched while the north and west gets the heat?

For beachgoers, a wrong weather forecast is annoying. For farmers, it can be very expensive. And for northern Queensland residents surprised by flooding after Cyclone Jasper, it can be devastating. Small wonder there's been plenty of criticism leveled at the Bureau of Meteorology and other forecasting agencies this summer.

The criticism is understandable. But is it fair? No. The reason is that weather forecasting is inherently not about certainty but probability. Our atmosphere and oceans do not behave in simple, easily predictable ways. They are non-linear, chaotic systems. That means we can only predict large weather features such as highs and lows or bands of storms with relative certainty and even then only for a few days in advance.

We want certainty—but we have to settle for probability

Let's say you check your weather app and see your location has a 60% chance of rain at midday. What does this actually mean?

It means if this forecast was issued 100 times, you should get wet 60 times and stay dry 40 times.

To forecast rainfall for a whole season ahead, meteorologists generally calculate the chance of exceeding average conditions, rather than stating that we will have a dry or wet summer with certainty.

So if we predict a 25% chance of above-average rain during an El Niño summer, we would expect that one out of every four times we make this prediction, we would observe higher rainfall than the average.

So how then do we know if we are making good forecasts? Given that a 60% chance of rain can mean wet or dry, albeit with different odds, we certainly won't be able to judge the forecast quality based on a single event. Instead, we assess many forecasts of 60% rain made in the past to see if the 60 to 40 split of wet and dry eventuated. If it did for this and all other possible probabilities, the forecasts work well.

This isn't what we'd like. Many of us find probabilistic forecasts confusing. Intuitively, we would prefer to simplify them into absolute statements.

Take a picnic you have planned for tomorrow. If you read the statement "there will be thunderstorms at noon tomorrow at Picnic Spot," you will feel confident it's best to cancel the event. But the statement "there's a 60% chance of thunderstorms at noon tomorrow at Picnic Spot" is far more accurate. The first gives false certainty, by vastly oversimplifying what we really know.

Let's not forget, there is a 40% chance it will stay dry, which the first statement completely ignores. And if it does stay dry, how will your friends react to the cancelled picnic? How much risk are you willing to take?

This article is republished from The Conversation under a Creative Commons license. Read the original article.