El Niño: How to Hear the Storm a Year in Advance ✎

Original: "Forecasting the magnitude and onset of El Nino based on climate network"
arXiv:1703.09138 · 2017-03-27 · arXiv non-exclusive · independent analysis · 1 min
Temperature in one patch of the Pacific Ocean hints at when El Niño will strike and how strong it will be.

An orchestra falls out of tune before a storm, then plays in unison again. In the Pacific Ocean, there is a small region where water temperature is measured daily at 105 points. Usually, these points change in a coordinated way, like musicians playing the same part. But before El Niño (the warm phase of the climate cycle), the harmony breaks: each point starts to follow its own rhythm.

Scientists have linked all the points with invisible threads. If temperatures at two locations change similarly, the thread is strong. Before El Niño, the threads weaken. This discord can be quantified by a number called the FI index. When FI rises, disorder in the region grows. It turns out that such disorder appears about a year before El Niño, and its magnitude hints at how strong the event will be.

Previously, El Niño's strength could not be predicted more than six months ahead. Now it can be forecast a year in advance, simply by watching temperatures in one ocean region.

Such forecasts help farmers know whether to expect drought, and cities to prepare for floods. The orchestra, once out of tune, plays in unison again, but now we hear the opening notes much earlier.

The FI index uses only temperature data from a small region of the Pacific Ocean, yet it predicts an event that affects weather worldwide. The strongest El Niño since 1948 (2014–2016) was predicted by this method a year before it began.

Concepts
El Niño La Niña Oceanic Niño index NOAA CPC ERSST forecast ENSO Multivariate ENSO Index spring predictability barrier interannual variability relative ONI climate network seasonal cycle Westerly wind burst monsoon onset NOAA PMEL / TAO forecasting equatorial wave propagation drought probabilistic forecast