Why hurricanes can't be predicted any more accurately ✛ ✎

Original: "Bound on forecasting skill for models of North Atlantic tropical cyclone counts"
arXiv:2410.05503 · 2024-10-07 · arXiv non-exclusive · independent analysis · 2 min
Scientists have found that forecasts of the number of Atlantic hurricanes have already hit a precision limit that cannot be overcome.

Every year, a different number of hurricanes is born in the Atlantic: sometimes five, sometimes fifteen. Scientists have long tried to figure out whether this number can be predicted in advance, using three main factors: warm ocean water, the El Niño phenomenon, and atmospheric pressure differences. These three things explain about half of why one year differs from another. But recently it turned out that further improvement of forecasts is no longer possible.

Even a perfect model cannot predict the number of hurricanes more accurately than with an error of about 2.5 cyclones on average. It is like trying to guess how many times heads will come up when flipping a coin: there is a limit to accuracy set by randomness itself.

Scientists tested this on data spanning 140 years. They took a simple model with three factors and compared it with very complex methods that use maps of the entire ocean's temperature. It turned out that both the simple and the complex models make errors of about the same size: roughly 2.5 hurricanes. This means that extra complexity does not help. The reason is that hurricanes themselves arise randomly, like rare events, and this randomness cannot be circumvented by any computation.

Now researchers are thinking about where to go next. Perhaps there are hidden connections between neighboring years that have not yet been noticed. Or we need to abandon the assumption that hurricanes occur independently of each other. But so far, all checks show that randomness is indeed random, and the ceiling of accuracy has been reached. For people living on the coast, this is important knowledge: it is better to prepare for variability than to wait for a miracle from computers.

The average number of cyclones per season in the Atlantic is about ten, but due to the Poisson nature of the process, the year-to-year spread is roughly three cyclones. This means even a perfect model cannot predict the exact number better than with an error of about 2.5 cyclones on average.

In Kim Stanley Robinson's novel 'Forty Signs of Rain', scientists try to improve extreme weather forecasts while grappling with atmospheric chaos. Here the situation is similar: even with complete knowledge of climate indices, an irreducible randomness remains that no computation can overcome.

\mathrm{err}(z, \lambda) = \sum_{y \ge 0} |y - z| \, P(y|\lambda)
Average deviation of the predicted number of events from the observed one, averaged over the Poisson distribution.
b(z) = \min_{\lambda} \mathrm{err}(z, \lambda)
Minimum possible error for unknown but fixed intensity.
B = \frac{1}{n} \sum_{t=1}^{n} b(y_t)
Averaging over all observations gives the theoretical minimum mean absolute error for any Poisson model.
Scientists
Max PlanckClaude ShannonJohn von NeumannLudwig BoltzmannJosiah Willard GibbsAndrey Markov
Concepts
spring predictability barrier NOAA CPC El Niño index ERSST ENSO Oceanic Niño index Jin-Timmermann model TAO/TRITON moorings relative ONI tropical cyclone Southern Oscillation La Niña North Equatorial Countercurrent recharge oscillator model Pacific decadal oscillation natural variability seasonal cycle NOAA PMEL / TAO forecast skill Warm water volume