The Ocean Whispers a Warning

Original: "Using Network Theory and Machine Learning to predict El Niño"
arXiv:1803.10076 · 2018-03-27 · CC BY 4.0 · 1 min
El Niño can now be spotted a year in advance: the ocean starts 'whispering' in pairs.

The ocean knows how to warn us. Before El Niño—the warm current that every few years brings droughts and floods—erupts in full force, it begins to 'whisper': distant points in the ocean become linked in pairs. In the past, no one knew how to listen to this whisper, and forecasts stopped at six months. In spring, models went blind, as if the ocean had fallen silent.

Scientists built a network of points across the ocean map. If oscillations at two points match, a connection forms between them. It turned out that a year before El Niño, the number of pairs—points with exactly one partner—increases. This metric is called c2.

c2 is simply the fraction of nodes that form pairs. When the ocean starts linking up in pairs, c2 rises, and this is visible a year before the event.

The new model works in two steps. First, simple statistics catch seasonal oscillations; then, a neural network learns what the statistics missed. Instead of relying solely on temperature readings, the model now includes c2. And a 12-month forecast has become as accurate as the old 6-month one. Now the ocean warns us long before it cries out.

The network metric c2 is simply the fraction of nodes that form pairs. In the climate network, it increases when local correlations strengthen before El Niño and decreases afterward. In essence, the ocean starts 'whispering' in pairs before it screams.

NRMSE(y_A, y_B) = \frac{1}{\max(y_A, y_B) - \min(y_A, y_B)} \sqrt{\frac{\sum_{k=1}^{n} (y_A^k - y_B^k)^2}{n}}
Forecast error divided by the range of observed values; the smaller, the better.
c_s = \frac{s n_s}{N}
Fraction of nodes belonging to clusters of size s; for s=2 this is pairs of connected nodes.
Z_t = Y_t + N_t
The observed series is decomposed into a linear part Y_t (ARIMA) and a nonlinear residual N_t (neural network).
Concepts
spring predictability barrier El Niño-Southern Oscillation neural network regression ENSO Jin-Timmermann model El Niño index La Niña Multivariate ENSO Index predictive learning neural networks equatorial wave propagation zebiak cane model ensemble forecast Machine Learning forecast skill NOAA CPC ERSST NOAA PMEL / TAO relative ONI TAO/TRITON moorings prediction