Forecasting weather in a changing climate

Is the current El Niño, the cyclical Pacific weather phenomenon, breaking down? That’s what the US National Oceanic and Atmospheric Administration is saying, predicting that it will happen this southern autumn. 

Australia’s Bureau of Meteorology is not quite so sure. It agrees that the current El Niño is breaking down, but its modelling suggests that a return to neutral El Niño–Southern Oscillation (ENSO) levels won’t happen until June or July. 

The summer is far from over – the Bureau expects this month to be warmer and drier than average – and the worst of Tasmania’s fire season may be yet to come. But the hot, dry summer forecast for eastern Australia back in spring has for the most part failed to materialise. 

Last August meteorologists were saying we’d seen the last of La Niña’s cool, damp weather in eastern Australia (and bad fire weather in North America) for quite a while. But now NOAA is reporting increasing odds of yet another La Niña emerging late this year. That would mean that we would have had an active La Niña in each of the past five years – six if we include 2025.

The more we know about weather and climate, the more we realise we have yet to learn. The confusion over ENSO patterns and what they mean for our seasonal weather is just one of many mixed, apparently contradictory messages coming out of our meteorology agencies as we struggle to get a handle on climate change.

The problem is bad enough for those whose livelihoods depend on having a handle well ahead of time on how coming seasons are likely to pan out – people like farmers and tourism operators. But human life is at stake if the matter in question is extreme weather – especially storms.

In mid-October 2023 Otis was a tropical storm 250km off Mexico’s Pacific coast. Weather experts predicted its impact would be unexceptional, even as late as the day before it made landfall. But in just 12 hours it intensified from a category one event to category five, the highest cyclone rating, and ended up the most powerful storm ever to cross an eastern Pacific coast.

To those in its path, including a million people in the city of Acapulco, the impact of Otis was devastating. At least 52 people died (locals claimed the toll was in the hundreds) and property losses were calculated at $16 billion.

But Otis was also a shock for the meteorologists whose projections had been so badly awry. In an article in The Conversation, a coastal engineering specialist from the University of East London, Ravindra Jayaratne, described the hurricane as “a pivotal moment in the history of weather forecasting”.

“It is critical that we address the pressing concerns related to the tools we use for forecasting these catastrophic events, all while recognising the significant influence of rapid climate change on our forecasting capabilities,” wrote Jayaratne.

Otis highlighted the limits of historical data in predicting weather in changing climate. The rate at which it grew from a tropical storm to a full-strength hurricane has never previously been observed. It may be a one-in-1000-year storm – a term we’re hearing repeatedly these days in reference to major weather events – but in truth we have no idea because we have no recorded precedent.

Weather and climate are fundamentally chaotic. The atmospheric variables that modellers try to assess are nonlinear, which means that a low level of uncertainty early in a weather event can turn into a very large discrepancy in the event’s later stages. 

Never was that more evident than in the case of Otis, but it’s an issue right across the forecasting spectrum from storm and flood events with a short term local or regional impact – Australian summer floods, for example –  to large-scale systems like ENSO, whose impact lasts months or years.

The first need is data. As an eastern Pacific hurricane, Otis had many fewer data-gathering points to feed into forecasts than the far more active Gulf and Atlantic hurricane zones on the other side of North America. An obvious need across the globe is more weather buoys and satellites.

Beyond that, we need our forecasting models to take account of a far wider range of variables, requiring an exponential increase in computing capacity. Along with increasingly powerful hardware, artificial intelligence will have a pivotal role.

Those developments can’t come soon enough. As the impact of climate change on established weather patterns rises, and given the level of scepticism that has always confronted weather forecasting professionals, predicting the weather is now more challenging than ever. 

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